\chapter{Advanced Compiler Use and Efficiency Hints}
\label{advanced-compiler}

\credits{by Robert MacLachlan}


\section{Advanced Compiler Introduction}

In \cmucl{}, as with any language on any computer, the path to efficient
code starts with good algorithms and sensible programming techniques,
but to avoid inefficiency pitfalls, you need to know some of this
implementation's quirks and features.  This chapter is mostly a fairly
long and detailed overview of what optimizations \python{} does.
Although there are the usual negative suggestions of inefficient
features to avoid, the main emphasis is on describing the things that
programmers can count on being efficient.

The optimizations described here can have the effect of speeding up
existing programs written in conventional styles, but the potential
for new programming styles that are clearer and less error-prone is at
least as significant.  For this reason, several sections end with a
discussion of the implications of these optimizations for programming
style.



\subsection{Types}

\python{}'s support for types is unusual in three major ways:
\begin{itemize}
  
\item Precise type checking encourages the specific use of type
  declarations as a form of run-time consistency checking.  This
  speeds development by localizing type errors and giving more
  meaningful error messages.  \xlref{precise-type-checks}.  \python{}
  produces completely safe code; optimized type checking maintains
  reasonable efficiency on conventional hardware
  (\pxlref{type-check-optimization}.)
  
\item Comprehensive support for the \clisp{} type system makes complex
  type specifiers useful.  Using type specifiers such as \code{or} and
  \code{member} has both efficiency and robustness advantages.
  \xlref{advanced-type-stuff}.
  
\item Type inference eliminates the need for some declarations, and
  also aids compile-time detection of type errors.  Given detailed
  type declarations, type inference can often eliminate type checks
  and enable more efficient object representations and code sequences.
  Checking all types results in fewer type checks.  See sections
  \ref{type-inference} and \ref{non-descriptor}.
\end{itemize}


\subsection{Optimization}

The main barrier to efficient Lisp programs is not that there is no
efficient way to code the program in Lisp, but that it is difficult to
arrive at that efficient coding.  \clisp{} is a highly complex
language, and usually has many semantically equivalent ``reasonable''
ways to code a given problem.  It is desirable to make all of these
equivalent solutions have comparable efficiency so that programmers
don't have to waste time discovering the most efficient solution.

Source level optimization increases the number of efficient ways to
solve a problem.  This effect is much larger than the increase in the
efficiency of the ``best'' solution.  Source level optimization
transforms the original program into a more efficient (but equivalent)
program.  Although the optimizer isn't doing anything the programmer
couldn't have done, this high-level optimization is important because:

\begin{itemize} 
\item The programmer can code simply and directly, rather than
  obfuscating code to please the compiler.
  
\item When presented with a choice of similar coding alternatives, the
  programmer can chose whichever happens to be most convenient,
  instead of worrying about which is most efficient.
\end{itemize}

Source level optimization eliminates the need for macros to optimize
their expansion, and also increases the effectiveness of inline
expansion.  See sections \ref{source-optimization} and
\ref{inline-expansion}.

Efficient support for a safer programming style is the biggest
advantage of source level optimization.  Existing tuned programs
typically won't benefit much from source optimization, since their
source has already been optimized by hand.  However, even tuned
programs tend to run faster under \python{} because:

\begin{itemize} 
\item Low level optimization and register allocation provides modest
  speedups in any program.
  
\item Block compilation and inline expansion can reduce function call
  overhead, but may require some program restructuring.  See sections
  \ref{inline-expansion}, \ref{local-call} and
  \ref{block-compilation}.
  
\item Efficiency notes will point out important type declarations that
  are often missed even in highly tuned programs.
  \xlref{efficiency-notes}.
  
\item Existing programs can be compiled safely without prohibitive
  speed penalty, although they would be faster and safer with added
  declarations.  \xlref{type-check-optimization}.
  
\item The context declaration mechanism allows both space and runtime
  of large systems to be reduced without sacrificing robustness by
  semi-automatically varying compilation policy without addition any
  \code{optimize} declarations to the source.
  \xlref{context-declarations}.
  
\item Byte compilation can be used to dramatically reduce the size of
  code that is not speed-critical. \xlref{byte-compile}
\end{itemize}


\subsection{Function Call}

The sort of symbolic programs generally written in \llisp{} often
favor recursion over iteration, or have inner loops so complex that
they involve multiple function calls.  Such programs spend a larger
fraction of their time doing function calls than is the norm in other
languages; for this reason \llisp{} implementations strive to make the
general (or full) function call as inexpensive as possible.  \python{}
goes beyond this by providing two good alternatives to full call:

\begin{itemize} 
\item Local call resolves function references at compile time,
  allowing better calling sequences and optimization across function
  calls.  \xlref{local-call}.
  
\item Inline expansion totally eliminates call overhead and allows
  many context dependent optimizations.  This provides a safe and
  efficient implementation of operations with function semantics,
  eliminating the need for error-prone macro definitions or manual
  case analysis.  Although most \clisp{} implementations support
  inline expansion, it becomes a more powerful tool with \python{}'s
  source level optimization.  See sections \ref{source-optimization}
  and \ref{inline-expansion}.
\end{itemize}


Generally, \python{} provides simple implementations for simple uses
of function call, rather than having only a single calling convention.
These features allow a more natural programming style:

\begin{itemize} 
\item Proper tail recursion.  \xlref{tail-recursion}
  
\item Relatively efficient closures.
  
\item A \code{funcall} that is as efficient as normal named call.
  
\item Calls to local functions such as from \code{labels} are
  optimized:
\begin{itemize}
  
\item Control transfer is a direct jump.
  
\item The closure environment is passed in registers rather than heap
  allocated.
  
\item Keyword arguments and multiple values are implemented more
  efficiently.
\end{itemize}

\xlref{local-call}.
\end{itemize}


\subsection{Representation of Objects}

Sometimes traditional \llisp{} implementation techniques compare so
poorly to the techniques used in other languages that \llisp{} can
become an impractical language choice.  Terrible inefficiencies appear
in number-crunching programs, since \llisp{} numeric operations often
involve number-consing and generic arithmetic.  \python{} supports
efficient natural representations for numbers (and some other types),
and allows these efficient representations to be used in more
contexts.  \python{} also provides good efficiency notes that warn
when a crucial declaration is missing.

See section \ref{non-descriptor} for more about object representations and
numeric types.  Also \pxlref{efficiency-notes} about efficiency notes.


\subsection{Writing Efficient Code}
\label{efficiency-overview}

Writing efficient code that works is a complex and prolonged process.
It is important not to get so involved in the pursuit of efficiency
that you lose sight of what the original problem demands.  Remember
that:
\begin{itemize}
  
\item The program should be correct\dash{}it doesn't matter how
  quickly you get the wrong answer.
  
\item Both the programmer and the user will make errors, so the
  program must be robust\dash{}it must detect errors in a way that
  allows easy correction.
  
\item A small portion of the program will consume most of the
  resources, with the bulk of the code being virtually irrelevant to
  efficiency considerations.  Even experienced programmers familiar
  with the problem area cannot reliably predict where these ``hot
  spots'' will be.
\end{itemize}



The best way to get efficient code that is still worth using, is to separate
coding from tuning.  During coding, you should:
\begin{itemize}
  
\item Use a coding style that aids correctness and robustness without
  being incompatible with efficiency.
  
\item Choose appropriate data structures that allow efficient
  algorithms and object representations
  (\pxlref{object-representation}).  Try to make interfaces abstract
  enough so that you can change to a different representation if
  profiling reveals a need.
  
\item Whenever you make an assumption about a function argument or
  global data structure, add consistency assertions, either with type
  declarations or explicit uses of \code{assert}, \code{ecase}, etc.
\end{itemize}

During tuning, you should:
\begin{itemize}
  
\item Identify the hot spots in the program through profiling (section
  \ref{profiling}.)
  
\item Identify inefficient constructs in the hot spot with efficiency
  notes, more profiling, or manual inspection of the source.  See
  sections \ref{general-efficiency} and \ref{efficiency-notes}.
  
\item Add declarations and consider the application of optimizations.
  See sections \ref{local-call}, \ref{inline-expansion} and
  \ref{non-descriptor}.
  
\item If all else fails, consider algorithm or data structure changes.
  If you did a good job coding, changes will be easy to introduce.
\end{itemize}


\section{More About Types in Python}
\label{advanced-type-stuff}
\cpsubindex{types}{in python}

This section goes into more detail describing what types and declarations are
recognized by \python.  The area where \python{} differs most radically from
previous \llisp{} compilers is in its support for types:
\begin{itemize}
  
\item Precise type checking helps to find bugs at run time.
  
\item Compile-time type checking helps to find bugs at compile time.
  
\item Type inference minimizes the need for generic operations, and
  also increases the efficiency of run time type checking and the
  effectiveness of compile time type checking.
  
\item Support for detailed types provides a wealth of opportunity for
  operation-specific type inference and optimization.
\end{itemize}



\subsection{More Types Meaningful}

\clisp{} has a very powerful type system, but conventional \llisp{}
implementations typically only recognize the small set of types
special in that implementation.  In these systems, there is an
unfortunate paradox: a declaration for a relatively general type like
\code{fixnum} will be recognized by the compiler, but a highly
specific declaration such as \code{\w{(integer 3 17)}} is totally
ignored.

This is obviously a problem, since the user has to know how to specify
the type of an object in the way the compiler wants it.  A very
minimal (but rarely satisfied) criterion for type system support is
that it be no worse to make a specific declaration than to make a
general one.  \python{} goes beyond this by exploiting a number of
advantages obtained from detailed type information.

Using more restrictive types in declarations allows the compiler to do
better type inference and more compile-time type checking.  Also, when
type declarations are considered to be consistency assertions that
should be verified (conditional on policy), then complex types are
useful for making more detailed assertions.

\python{} ``understands'' the list-style \code{or}, \code{member},
\code{function}, array and number type specifiers.  Understanding
means that:
\begin{itemize}
  
\item If the type contains more information than is used in a
  particular context, then the extra information is simply ignored,
  rather than derailing type inference.
  
\item In many contexts, the extra information from these type
  specifier is used to good effect.  In particular, type checking in
  \python{} is \var{precise}, so these complex types can be used
  in declarations to make interesting assertions about functions and
  data structures (\pxlref{precise-type-checks}.)  More specific
  declarations also aid type inference and reduce the cost for type
  checking.
\end{itemize}

For related information, \pxlref{numeric-types} for numeric types, and
section \ref{array-types} for array types.


\subsection{Canonicalization}
\cpsubindex{types}{equivalence}
\cindex{canonicalization of types}
\cindex{equivalence of types}

When given a type specifier, \python{} will often rewrite it into a
different (but equivalent) type.  This is the mechanism that \python{}
uses for detecting type equivalence.  For example, in \python{}'s
canonical representation, these types are equivalent:
\begin{example}
(or list (member :end)) \myequiv (or cons (member nil :end))
\end{example}
This has two implications for the user:
\begin{itemize}
  
\item The standard symbol type specifiers for \code{atom},
  \code{null}, \code{fixnum}, etc., are in no way magical.  The
  \tindexed{null} type is actually defined to be \code{\w{(member
      nil)}}, \tindexed{list} is \code{\w{(or cons null)}}, and
  \tindexed{fixnum} is \code{\w{(signed-byte 30)}}.
  
\item When the compiler prints out a type, it may not look like the
  type specifier that originally appeared in the program.  This is
  generally not a problem, but it must be taken into consideration
  when reading compiler error messages.
\end{itemize}


\subsection{Member Types}
\cindex{member types}

The \tindexed{member} type specifier can be used to represent
``symbolic'' values, analogous to the enumerated types of Pascal.  For
example, the second value of \code{find-symbol} has this type:
\begin{lisp}
(member :internal :external :inherited nil)
\end{lisp}
Member types are very useful for expressing consistency constraints on data
structures, for example:
\begin{lisp}
(defstruct ice-cream
  (flavor :vanilla :type (member :vanilla :chocolate :strawberry)))
\end{lisp}
Member types are also useful in type inference, as the number of members can
sometimes be pared down to one, in which case the value is a known constant.


\subsection{Union Types}
\cindex{union (\code{or}) types}
\cindex{or (union) types}

The \tindexed{or} (union) type specifier is understood, and is
meaningfully applied in many contexts.  The use of \code{or} allows
assertions to be made about types in dynamically typed programs.  For
example:

\begin{lisp}
(defstruct box
  (next nil :type (or box null))
  (top :removed :type (or box-top (member :removed))))
\end{lisp}

The type assertion on the \code{top} slot ensures that an error will be signaled
when there is an attempt to store an illegal value (such as \kwd{rmoved}.)
Although somewhat weak, these union type assertions provide a useful input into
type inference, allowing the cost of type checking to be reduced.  For example,
this loop is safely compiled with no type checks:

\begin{lisp}
(defun find-box-with-top (box)
  (declare (type (or box null) box))
  (do ((current box (box-next current)))
      ((null current))
    (unless (eq (box-top current) :removed)
      (return current))))
\end{lisp}

Union types are also useful in type inference for representing types that are
partially constrained.  For example, the result of this expression:
\begin{lisp}
(if foo
    (logior x y)
    (list x y))
\end{lisp}
can be expressed as \code{\w{(or integer cons)}}.


\subsection{The Empty Type}
\label{empty-type}
\cindex{NIL type}
\cpsubindex{empty type}{the}
\cpsubindex{errors}{result type of}

The type \false{} is also called the empty type, since no object is of
type \false{}.  The union of no types, \code{(or)}, is also empty.
\python{}'s interpretation of an expression whose type is \false{} is
that the expression never yields any value, but rather fails to
terminate, or is thrown out of.  For example, the type of a call to
\code{error} or a use of \code{return} is \false{}.  When the type of
an expression is empty, compile-time type warnings about its value are
suppressed; presumably somebody else is signaling an error.  If a
function is declared to have return type \false{}, but does in fact
return, then (in safe compilation policies) a ``\code{NIL Function
  returned}'' error will be signaled.  See also the function
\funref{required-argument}.


\subsection{Function Types}
\label{function-types}
\cpsubindex{function}{types}
\cpsubindex{types}{function}

\findexed{function} types are understood in the restrictive sense, specifying:
\begin{itemize}
  
\item The argument syntax that the function must be called with.  This
  is information about what argument counts are acceptable, and which
  keyword arguments are recognized.  In \python, warnings about
  argument syntax are a consequence of function type checking.
  
\item The types of the argument values that the caller must pass.  If
  the compiler can prove that some argument to a call is of a type
  disallowed by the called function's type, then it will give a
  compile-time type warning.  In addition to being used for
  compile-time type checking, these type assertions are also used as
  output type assertions in code generation.  For example, if
  \code{foo} is declared to have a \code{fixnum} argument, then the
  \code{1+} in \w{\code{(foo (1+ x))}} is compiled with knowledge that
  the result must be a fixnum.
  
\item The types the values that will be bound to argument variables in
  the function's definition.  Declaring a function's type with
  \code{ftype} implicitly declares the types of the arguments in the
  definition.  \python{} checks for consistency between the definition
  and the \code{ftype} declaration.  Because of precise type checking,
  an error will be signaled when a function is called with an
  argument of the wrong type.
  
\item The type of return value(s) that the caller can expect.  This
  information is a useful input to type inference.  For example, if a
  function is declared to return a \code{fixnum}, then when a call to
  that function appears in an expression, the expression will be
  compiled with knowledge that the call will return a \code{fixnum}.
  
\item The type of return value(s) that the definition must return.
  The result type in an \code{ftype} declaration is treated like an
  implicit \code{the} wrapped around the body of the definition.  If
  the definition returns a value of the wrong type, an error will be
  signaled.  If the compiler can prove that the function returns the
  wrong type, then it will give a compile-time warning.
\end{itemize}

This is consistent with the new interpretation of function types and
the \code{ftype} declaration in the proposed X3J13
``function-type-argument-type-semantics'' cleanup.  Note also, that if
you don't explicitly declare the type of a function using a global
\code{ftype} declaration, then \python{} will compute a function type
from the definition, providing a degree of inter-routine type
inference, \pxlref{function-type-inference}.


\subsection{The Values Declaration}
\cindex{values declaration}

\cmucl{} supports the \code{values} declaration as an extension to
\clisp.  The syntax of the declaration is 
{\code{(values \var{type1} \var{type2}$\ldots$\var{typen})}}.  This
declaration is semantically equivalent to a \code{the} form wrapped
around the body of the special form in which the \code{values}
declaration appears. The advantage of \code{values} over
\findexed{the} is purely syntactic\dash{}it doesn't introduce more
indentation.  For example:

\begin{example}
(defun foo (x)
  (declare (values single-float))
  (ecase x
    (:this ...)
    (:that ...)
    (:the-other ...)))
\end{example}

is equivalent to:

\begin{example}
(defun foo (x)
  (the single-float
       (ecase x
         (:this ...)
         (:that ...)
         (:the-other ...))))
\end{example}

and

\begin{example}
(defun floor (number &optional (divisor 1))
  (declare (values integer real))
  ...)
\end{example}

is equivalent to:

\begin{example}
(defun floor (number &optional (divisor 1))
  (the (values integer real)
       ...))
\end{example}

In addition to being recognized by \code{lambda} (and hence by
\code{defun}), the \code{values} declaration is recognized by all the
other special forms with bodies and declarations: \code{let},
\code{let*}, \code{labels} and \code{flet}.  Macros with declarations
usually splice the declarations into one of the above forms, so they
will accept this declaration too, but the exact effect of a
\code{values} declaration will depend on the macro.

If you declare the types of all arguments to a function, and also
declare the return value types with \code{values}, you have described
the type of the function.  \python{} will use this argument and result
type information to derive a function type that will then be applied
to calls of the function (\pxlref{function-types}.)  This provides a
way to declare the types of functions that is much less syntactically
awkward than using the \code{ftype} declaration with a \code{function}
type specifier.

Although the \code{values} declaration is non-standard, it is
relatively harmless to use it in otherwise portable code, since any
warning in non-CMU implementations can be suppressed with the standard
\code{declaration} proclamation.


\subsection{Structure Types}
\label{structure-types}
\cindex{structure types}
\cindex{defstruct types}
\cpsubindex{types}{structure}

Because of precise type checking, structure types are much better
supported by \python{} than by conventional compilers:

\begin{itemize}  
\item The structure argument to structure accessors is precisely
  checked\dash{}if you call \code{foo-a} on a \code{bar}, an error
  will be signaled.
  
\item The types of slot values are precisely checked\dash{}if you pass
  the wrong type argument to a constructor or a slot setter, then an
  error will be signaled.
\end{itemize}

This error checking is tremendously useful for detecting bugs in
programs that manipulate complex data structures.

An additional advantage of checking structure types and enforcing slot
types is that the compiler can safely believe slot type declarations.
\python{} effectively moves the type checking from the slot access to
the slot setter or constructor call.  This is more efficient since
caller of the setter or constructor often knows the type of the value,
entirely eliminating the need to check the value's type.  Consider
this example:

\begin{lisp}
(defstruct coordinate
  (x nil :type single-float)
  (y nil :type single-float))

(defun make-it ()
  (make-coordinate :x 1.0 :y 1.0))

(defun use-it (it)
  (declare (type coordinate it))
  (sqrt (expt (coordinate-x it) 2) (expt (coordinate-y it) 2)))
\end{lisp}

\code{make-it} and \code{use-it} are compiled with no checking on the
types of the float slots, yet \code{use-it} can use
\code{single-float} arithmetic with perfect safety.  Note that
\code{make-coordinate} must still check the values of \code{x} and
\code{y} unless the call is block compiled or inline expanded
(\pxlref{local-call}.)  But even without this advantage, it is almost
always more efficient to check slot values on structure
initialization, since slots are usually written once and read many
times.


\subsection{The Freeze-Type Declaration}
\cindex{freeze-type declaration}
\label{freeze-type}

The \code{extensions:freeze-type} declaration is a \cmucl{} extension that
enables more efficient compilation of user-defined types by asserting
that the definition is not going to change.  This declaration may only
be used globally (with \code{declaim} or \code{proclaim}).  Currently
\code{freeze-type} only affects structure type testing done by
\code{typep}, \code{typecase}, etc.  Here is an example:

\begin{lisp}
(declaim (freeze-type foo bar))
\end{lisp}

This asserts that the types \code{foo} and \code{bar} and their
subtypes are not going to change.  This allows more efficient type
testing, since the compiler can open-code a test for all possible
subtypes, rather than having to examine the type hierarchy at
run-time.


\subsection{Type Restrictions}
\cpsubindex{types}{restrictions on}

Avoid use of the \code{and}, \code{not} and \code{satisfies} types in
declarations, since type inference has problems with them.  When these
types do appear in a declaration, they are still checked precisely,
but the type information is of limited use to the compiler.
\code{and} types are effective as long as the intersection can be
canonicalized to a type that doesn't use \code{and}.  For example:

\begin{example}
(and fixnum unsigned-byte)
\end{example}

is fine, since it is the same as:

\begin{example}
(integer 0 \var{most-positive-fixnum})
\end{example}

but this type:

\begin{example}
(and symbol (not (member :end)))
\end{example}

will not be fully understood by type interference since the \code{and}
can't be removed by canonicalization.

Using any of these type specifiers in a type test with \code{typep} or
\code{typecase} is fine, since as tests, these types can be translated
into the \code{and} macro, the \code{not} function or a call to the
satisfies predicate.


\subsection{Type Style Recommendations}
\cindex{style recommendations}

\python{} provides good support for some currently unconventional ways of
using the \clisp{} type system.  With \python{}, it is desirable to make
declarations as precise as possible, but type inference also makes
some declarations unnecessary.  Here are some general guidelines for
maximum robustness and efficiency:
\begin{itemize}
  
\item Declare the types of all function arguments and structure slots
  as precisely as possible (while avoiding \code{not}, \code{and} and
  \code{satisfies}).  Put these declarations in during initial coding
  so that type assertions can find bugs for you during debugging.
  
\item Use the \tindexed{member} type specifier where there are a small
  number of possible symbol values, for example: \w{\code{(member :red
      :blue :green)}}.
  
\item Use the \tindexed{or} type specifier in situations where the
  type is not certain, but there are only a few possibilities, for
  example: \w{\code{(or list vector)}}.
  
\item Declare integer types with the tightest bounds that you can,
  such as \code{\w{(integer 3 7)}}.
  
\item Define \findexed{deftype} or \findexed{defstruct} types before
  they are used.  Definition after use is legal (producing no
  ``undefined type'' warnings), but type tests and structure
  operations will be compiled much less efficiently.
  
\item Use the \code{extensions:freeze-type} declaration to speed up
  type testing for structure types which won't have new subtypes added
  later. \xlref{freeze-type}
  
\item In addition to declaring the array element type and simpleness,
  also declare the dimensions if they are fixed, for example:
  \begin{example}
    (simple-array single-float (1024 1024))
  \end{example}
  This bounds information allows array indexing for multi-dimensional
  arrays to be compiled much more efficiently, and may also allow
  array bounds checking to be done at compile time.
  \xlref{array-types}.

\item Avoid use of the \findexed{the} declaration within expressions.
  Not only does it clutter the code, but it is also almost worthless
  under safe policies.  If the need for an output type assertion is
  revealed by efficiency notes during tuning, then you can consider
  \code{the}, but it is preferable to constrain the argument types
  more, allowing the compiler to prove the desired result type.
  
\item Don't bother declaring the type of \findexed{let} or other
  non-argument variables unless the type is non-obvious.  If you
  declare function return types and structure slot types, then the
  type of a variable is often obvious both to the programmer and to
  the compiler.  An important case where the type isn't obvious, and a
  declaration is appropriate, is when the value for a variable is
  pulled out of untyped structure (e.g., the result of \code{car}), or
  comes from some weakly typed function, such as \code{read}.
  
\item Declarations are sometimes necessary for integer loop variables,
  since the compiler can't always prove that the value is of a good
  integer type.  These declarations are best added during tuning, when
  an efficiency note indicates the need.
\end{itemize}


\section{Type Inference}
\label{type-inference}
\cindex{type inference}
\cindex{inference of types}
\cindex{derivation of types}

Type inference is the process by which the compiler tries to figure
out the types of expressions and variables, given an inevitable lack
of complete type information.  Although \python{} does much more type
inference than most \llisp{} compilers, remember that the more precise
and comprehensive type declarations are, the more type inference will
be able to do.


\subsection{Variable Type Inference}
\label{variable-type-inference}

The type of a variable is the union of the types of all the
definitions.  In the degenerate case of a let, the type of the
variable is the type of the initial value.  This inferred type is
intersected with any declared type, and is then propagated to all the
variable's references.  The types of \findexed{multiple-value-bind}
variables are similarly inferred from the types of the individual
values of the values form.

If multiple type declarations apply to a single variable, then all the
declarations must be correct; it is as though all the types were intersected
producing a single \tindexed{and} type specifier.  In this example:
\begin{example}
(defmacro my-dotimes ((var count) &body body)
  `(do ((,var 0 (1+ ,var)))
       ((>= ,var ,count))
     (declare (type (integer 0 *) ,var))
     ,@body))

(my-dotimes (i ...)
  (declare (fixnum i))
  ...)
\end{example}
the two declarations for \code{i} are intersected, so \code{i} is
known to be a non-negative fixnum.

In practice, this type inference is limited to lets and local
functions, since the compiler can't analyze all the calls to a global
function.  But type inference works well enough on local variables so
that it is often unnecessary to declare the type of local variables.
This is especially likely when function result types and structure
slot types are declared.  The main areas where type inference breaks
down are:
\begin{itemize}
  
\item When the initial value of a variable is a untyped expression,
  such as \code{\w{(car x)}}, and
  
\item When the type of one of the variable's definitions is a function
  of the variable's current value, as in: \code{(setq x (1+ x))}
\end{itemize}


\subsection{Local Function Type Inference}
\cpsubindex{local call}{type inference}

The types of arguments to local functions are inferred in the same was
as any other local variable; the type is the union of the argument
types across all the calls to the function, intersected with the
declared type.  If there are any assignments to the argument
variables, the type of the assigned value is unioned in as well.

The result type of a local function is computed in a special way that
takes tail recursion (\pxlref{tail-recursion}) into consideration.
The result type is the union of all possible return values that aren't
tail-recursive calls.  For example, \python{} will infer that the
result type of this function is \code{integer}:

\begin{lisp}
(defun ! (n res)
  (declare (integer n res))
  (if (zerop n)
      res
      (! (1- n) (* n res))))
\end{lisp}

Although this is a rather obvious result, it becomes somewhat less
trivial in the presence of mutual tail recursion of multiple
functions.  Local function result type inference interacts with the
mechanisms for ensuring proper tail recursion mentioned in section
\ref{local-call-return}.


\subsection{Global Function Type Inference}
\label{function-type-inference}
\cpsubindex{function}{type inference}

As described in section \ref{function-types}, a global function type
(\tindexed{ftype}) declaration places implicit type assertions on the
call arguments, and also guarantees the type of the return value.  So
wherever a call to a declared function appears, there is no doubt as
to the types of the arguments and return value.  Furthermore,
\python{} will infer a function type from the function's definition if
there is no \code{ftype} declaration.  Any type declarations on the
argument variables are used as the argument types in the derived
function type, and the compiler's best guess for the result type of
the function is used as the result type in the derived function type.

This method of deriving function types from the definition implicitly assumes
that functions won't be redefined at run-time.  Consider this example:
\begin{lisp}
(defun foo-p (x)
  (let ((res (and (consp x) (eq (car x) 'foo))))
    (format t "It is ~:[not ~;~]foo." res)))

(defun frob (it)
  (if (foo-p it)
      (setf (cadr it) 'yow!)
      (1+ it)))
\end{lisp}

Presumably, the programmer really meant to return \code{res} from
\code{foo-p}, but he seems to have forgotten.  When he tries to call
do \code{\w{(frob (list 'foo nil))}}, \code{frob} will flame out when
it tries to add to a \code{cons}.  Realizing his error, he fixes
\code{foo-p} and recompiles it.  But when he retries his test case, he
is baffled because the error is still there.  What happened in this
example is that \python{} proved that the result of \code{foo-p} is
\code{null}, and then proceeded to optimize away the \code{setf} in
\code{frob}.

Fortunately, in this example, the error is detected at compile time
due to notes about unreachable code (\pxlref{dead-code-notes}.)
Still, some users may not want to worry about this sort of problem
during incremental development, so there is a variable to control
deriving function types.

\begin{defvar}{extensions:}{derive-function-types}
  
  If true (the default), argument and result type information derived
  from compilation of \code{defun}s is used when compiling calls to
  that function.  If false, only information from \code{ftype}
  proclamations will be used.
\end{defvar}


\subsection{Operation Specific Type Inference}
\label{operation-type-inference}
\cindex{operation specific type inference}
\cindex{arithmetic type inference}
\cpsubindex{numeric}{type inference}

Many of the standard \clisp{} functions have special type inference
procedures that determine the result type as a function of the
argument types.  For example, the result type of \code{aref} is the
array element type.  Here are some other examples of type inferences:
\begin{lisp}
(logand x #xFF) \result{} (unsigned-byte 8)

(+ (the (integer 0 12) x) (the (integer 0 1) y)) \result{} (integer 0 13)

(ash (the (unsigned-byte 16) x) -8) \result{} (unsigned-byte 8)
\end{lisp}


\subsection{Dynamic Type Inference}
\label{constraint-propagation}
\cindex{dynamic type inference}
\cindex{conditional type inference}
\cpsubindex{type inference}{dynamic}

\python{} uses flow analysis to infer types in dynamically typed
programs.  For example:

\begin{example}
(ecase x
  (list (length x))
  ...)
\end{example}

Here, the compiler knows the argument to \code{length} is a list,
because the call to \code{length} is only done when \code{x} is a
list.  The most significant efficiency effect of inference from
assertions is usually in type check optimization.

Dynamic type inference has two inputs: explicit conditionals and
implicit or explicit type assertions.  Flow analysis propagates these
constraints on variable type to any code that can be executed only
after passing though the constraint.  Explicit type constraints come
from \findexed{if}s where the test is either a lexical variable or a
function of lexical variables and constants, where the function is
either a type predicate, a numeric comparison or \code{eq}.

If there is an \code{eq} (or \code{eql}) test, then the compiler will
actually substitute one argument for the other in the true branch.
For example:
\begin{lisp}
(when (eq x :yow!) (return x))
\end{lisp}
becomes:
\begin{lisp}
(when (eq x :yow!) (return :yow!))
\end{lisp}
This substitution is done when one argument is a constant, or one
argument has better type information than the other.  This
transformation reveals opportunities for constant folding or
type-specific optimizations.  If the test is against a constant, then
the compiler can prove that the variable is not that constant value in
the false branch, or \w{\code{(not (member :yow!))}}  in the example
above.  This can eliminate redundant tests, for example:
\begin{example}
(if (eq x nil)
    ...
    (if x a b))
\end{example}
is transformed to this:
\begin{example}
(if (eq x nil)
    ...
    a)
\end{example}
Variables appearing as \code{if} tests are interpreted as
\code{\w{(not (eq \var{var} nil))}} tests.  The compiler also converts
\code{=} into \code{eql} where possible.  It is difficult to do
inference directly on \code{=} since it does implicit coercions.

When there is an explicit \code{\textless} or \code{\textgreater} test on numeric
variables, the compiler makes inferences about the ranges the
variables can assume in the true and false branches. This is mainly
useful when it proves that the values are small enough in magnitude to
allow open-coding of arithmetic operations. For example, in many uses
of \code{dotimes} with a \code{fixnum} repeat count, the compiler
proves that fixnum arithmetic can be used.

Implicit type assertions are quite common, especially if you declare
function argument types.  Dynamic inference from implicit type
assertions sometimes helps to disambiguate programs to a useful
degree, but is most noticeable when it detects a dynamic type error.
For example:

\begin{lisp}
(defun foo (x)
  (+ (car x) x))
\end{lisp} 

results in this warning:

\begin{example}
In: DEFUN FOO
  (+ (CAR X) X)
==>
  X
Warning: Result is a LIST, not a NUMBER.
\end{example}

Note that \llisp{}'s dynamic type checking semantics make dynamic type
inference useful even in programs that aren't really dynamically
typed, for example:

\begin{lisp}
(+ (car x) (length x))
\end{lisp}

Here, \code{x} presumably always holds a list, but in the absence of a
declaration the compiler cannot assume \code{x} is a list simply
because list-specific operations are sometimes done on it.  The
compiler must consider the program to be dynamically typed until it
proves otherwise.  Dynamic type inference proves that the argument to
\code{length} is always a list because the call to \code{length} is
only done after the list-specific \code{car} operation.


\subsection{Type Check Optimization}
\label{type-check-optimization}
\cpsubindex{type checking}{optimization}
\cpsubindex{optimization}{type check}

\python{} backs up its support for precise type checking by minimizing
the cost of run-time type checking.  This is done both through type
inference and though optimizations of type checking itself.

Type inference often allows the compiler to prove that a value is of
the correct type, and thus no type check is necessary.  For example:
\begin{lisp}
(defstruct foo a b c)
(defstruct link
  (foo (required-argument) :type foo)
  (next nil :type (or link null)))

(foo-a (link-foo x))
\end{lisp}

Here, there is no need to check that the result of \code{link-foo} is
a \code{foo}, since it always is.  Even when some type checks are
necessary, type inference can often reduce the number:
\begin{example}
(defun test (x)
  (let ((a (foo-a x))
        (b (foo-b x))
        (c (foo-c x)))
    ...))
\end{example}
In this example, only one \w{\code{(foo-p x)}} check is needed.  This
applies to a lesser degree in list operations, such as:
\begin{lisp}
(if (eql (car x) 3) (cdr x) y)
\end{lisp}
Here, we only have to check that \code{x} is a list once.

Since \python{} recognizes explicit type tests, code that explicitly
protects itself against type errors has little introduced overhead due
to implicit type checking.  For example, this loop compiles with no
implicit checks checks for \code{car} and \code{cdr}:
\begin{lisp}
(defun memq (e l)
  (do ((current l (cdr current)))
      ((atom current) nil)
    (when (eq (car current) e) (return current))))
\end{lisp}

\cindex{complemented type checks}
\python{} reduces the cost of checks that must be done through an
optimization called \var{complementing}.  A complemented check for
\var{type} is simply a check that the value is not of the type
\w{\code{(not \var{type})}}.  This is only interesting when something
is known about the actual type, in which case we can test for the
complement of \w{\code{(and \var{known-type} (not \var{type}))}}, or
the difference between the known type and the assertion.  An example:
\begin{lisp}
(link-foo (link-next x))
\end{lisp}
Here, we change the type check for \code{link-foo} from a test for
\code{foo} to a test for:
\begin{lisp}
(not (and (or foo null) (not foo)))
\end{lisp}
or more simply \w{\code{(not null)}}.  This is probably the most
important use of complementing, since the situation is fairly common,
and a \code{null} test is much cheaper than a structure type test.

Here is a more complicated example that illustrates the combination of
complementing with dynamic type inference:
\begin{lisp}
(defun find-a (a x)
  (declare (type (or link null) x))
  (do ((current x (link-next current)))
      ((null current) nil)
    (let ((foo (link-foo current)))
      (when (eq (foo-a foo) a) (return foo)))))
\end{lisp}
This loop can be compiled with no type checks.  The \code{link} test
for \code{link-foo} and \code{link-next} is complemented to
\w{\code{(not null)}}, and then deleted because of the explicit
\code{null} test.  As before, no check is necessary for \code{foo-a},
since the \code{link-foo} is always a \code{foo}.  This sort of
situation shows how precise type checking combined with precise
declarations can actually result in reduced type checking.


\section{Source Optimization}
\label{source-optimization}
\cindex{optimization}

This section describes source-level transformations that \python{} does on
programs in an attempt to make them more efficient.  Although source-level
optimizations can make existing programs more efficient, the biggest advantage
of this sort of optimization is that it makes it easier to write efficient
programs.  If a clean, straightforward implementation is can be transformed
into an efficient one, then there is no need for tricky and dangerous hand
optimization. 


\subsection{Let Optimization}
\label{let-optimization}

\cindex{let optimization} \cpsubindex{optimization}{let}

The primary optimization of let variables is to delete them when they
are unnecessary.  Whenever the value of a let variable is a constant,
a constant variable or a constant (local or non-notinline) function,
the variable is deleted, and references to the variable are replaced
with references to the constant expression.  This is useful primarily
in the expansion of macros or inline functions, where argument values
are often constant in any given call, but are in general non-constant
expressions that must be bound to preserve order of evaluation.  Let
variable optimization eliminates the need for macros to carefully
avoid spurious bindings, and also makes inline functions just as
efficient as macros.

A particularly interesting class of constant is a local function.
Substituting for lexical variables that are bound to a function can
substantially improve the efficiency of functional programming styles,
for example:
\begin{lisp}
(let ((a #'(lambda (x) (zow x))))
  (funcall a 3))
\end{lisp}
effectively transforms to:
\begin{lisp}
(zow 3)
\end{lisp}
This transformation is done even when the function is a closure, as in:
\begin{lisp}
(let ((a (let ((y (zug)))
           #'(lambda (x) (zow x y)))))
  (funcall a 3))
\end{lisp}
becoming:
\begin{lisp}
(zow 3 (zug))
\end{lisp}

A constant variable is a lexical variable that is never assigned to,
always keeping its initial value.  Whenever possible, avoid setting
lexical variables\dash{}instead bind a new variable to the new value.
Except for loop variables, it is almost always possible to avoid
setting lexical variables.  This form:
\begin{example}
(let ((x (f x)))
  ...)
\end{example}
is \var{more} efficient than this form:
\begin{example}
(setq x (f x))
...
\end{example}
Setting variables makes the program more difficult to understand, both
to the compiler and to the programmer.  \python{} compiles assignments
at least as efficiently as any other \llisp{} compiler, but most let
optimizations are only done on constant variables.

Constant variables with only a single use are also optimized away,
even when the initial value is not constant.\footnote{The source
  transformation in this example doesn't represent the preservation of
  evaluation order implicit in the compiler's internal representation.
  Where necessary, the back end will reintroduce temporaries to
  preserve the semantics.}  For example, this expansion of
\code{incf}:
\begin{lisp}
(let ((#:g3 (+ x 1)))
  (setq x #:G3))
\end{lisp}
becomes:
\begin{lisp}
(setq x (+ x 1))
\end{lisp}
The type semantics of this transformation are more important than the
elimination of the variable itself.  Consider what happens when
\code{x} is declared to be a \code{fixnum}; after the transformation,
the compiler can compile the addition knowing that the result is a
\code{fixnum}, whereas before the transformation the addition would
have to allow for fixnum overflow.

Another variable optimization deletes any variable that is never read.
This causes the initial value and any assigned values to be unused,
allowing those expressions to be deleted if they have no side-effects.

Note that a let is actually a degenerate case of local call
(\pxlref{let-calls}), and that let optimization can be done on calls
that weren't created by a let.  Also, local call allows an applicative
style of iteration that is totally assignment free.


\subsection{Constant Folding}
\cindex{constant folding}
\cpsubindex{folding}{constant}

Constant folding is an optimization that replaces a call of constant
arguments with the constant result of that call.  Constant folding is
done on all standard functions for which it is legal.  Inline
expansion allows folding of any constant parts of the definition, and
can be done even on functions that have side-effects.

It is convenient to rely on constant folding when programming, as in this
example:
\begin{example}
(defconstant limit 42)

(defun foo ()
  (... (1- limit) ...))
\end{example}
Constant folding is also helpful when writing macros or inline
functions, since it usually eliminates the need to write a macro that
special-cases constant arguments.

\cindex{constant-function declaration} Constant folding of a user
defined function is enabled by the \code{extensions:constant-function}
proclamation.  In this example:
\begin{example}
(declaim (ext:constant-function myfun))
(defun myexp (x y)
  (declare (single-float x y))
  (exp (* (log x) y)))

 ... (myexp 3.0 1.3) ...
\end{example}
The call to \code{myexp} is constant-folded to \code{4.1711674}.


\subsection{Unused Expression Elimination}
\cindex{unused expression elimination}
\cindex{dead code elimination}

If the value of any expression is not used, and the expression has no
side-effects, then it is deleted.  As with constant folding, this
optimization applies most often when cleaning up after inline
expansion and other optimizations.  Any function declared an
\code{extensions:constant-function} is also subject to unused
expression elimination.

Note that \python{} will eliminate parts of unused expressions known
to be side-effect free, even if there are other unknown parts.  For
example:
\begin{lisp}
(let ((a (list (foo) (bar))))
  (if t
      (zow)
      (raz a)))
\end{lisp}
becomes:
\begin{lisp}
(progn (foo) (bar))
(zow)
\end{lisp}


\subsection{Control Optimization}
\cindex{control optimization}
\cpsubindex{optimization}{control}

The most important optimization of control is recognizing when an
\findexed{if} test is known at compile time, then deleting the
\code{if}, the test expression, and the unreachable branch of the
\code{if}.  This can be considered a special case of constant folding,
although the test doesn't have to be truly constant as long as it is
definitely not \false.  Note also, that type inference propagates the
result of an \code{if} test to the true and false branches,
\pxlref{constraint-propagation}.

A related \code{if} optimization is this transformation:\footnote{Note
  that the code for \code{x} and \code{y} isn't actually replicated.}
\begin{lisp}
(if (if a b c) x y)
\end{lisp}
into:
\begin{lisp}
(if a
    (if b x y)
    (if c x y))
\end{lisp}
The opportunity for this sort of optimization usually results from a
conditional macro.  For example:
\begin{lisp}
(if (not a) x y)
\end{lisp}
is actually implemented as this:
\begin{lisp}
(if (if a nil t) x y)
\end{lisp}
which is transformed to this:
\begin{lisp}
(if a
    (if nil x y)
    (if t x y))
\end{lisp}
which is then optimized to this:
\begin{lisp}
(if a y x)
\end{lisp}
Note that due to \python{}'s internal representations, the
\code{if}\dash{}\code{if} situation will be recognized even if other
forms are wrapped around the inner \code{if}, like:
\begin{example}
(if (let ((g ...))
      (loop
        ...
        (return (not g))
        ...))
    x y)
\end{example}

In \python, all the \clisp{} macros really are macros, written in
terms of \code{if}, \code{block} and \code{tagbody}, so user-defined
control macros can be just as efficient as the standard ones.
\python{} emits basic blocks using a heuristic that minimizes the
number of unconditional branches.  The code in a \code{tagbody} will
not be emitted in the order it appeared in the source, so there is no
point in arranging the code to make control drop through to the
target.


\subsection{Unreachable Code Deletion}
\label{dead-code-notes}
\cindex{unreachable code deletion}
\cindex{dead code elimination}

\python{} will delete code whenever it can prove that the code can never be
executed.  Code becomes unreachable when:

\begin{itemize}
\item
An \code{if} is optimized away, or

\item
There is an explicit unconditional control transfer such as \code{go} or
\code{return-from}, or

\item
The last reference to a local function is deleted (or there never was any
reference.)
\end{itemize}

When code that appeared in the original source is deleted, the compiler prints
a note to indicate a possible problem (or at least unnecessary code.)  For
example:
\begin{lisp}
(defun foo ()
  (if t
      (write-line "True.")
      (write-line "False.")))
\end{lisp}
will result in this note:
\begin{example}
In: DEFUN FOO
  (WRITE-LINE "False.")
Note: Deleting unreachable code.
\end{example}

It is important to pay attention to unreachable code notes, since they often
indicate a subtle type error.  For example:
\begin{example}
(defstruct foo a b)

(defun lose (x)
  (let ((a (foo-a x))
        (b (if x (foo-b x) :none)))
    ...))
\end{example}
results in this note:
\begin{example}
In: DEFUN LOSE
  (IF X (FOO-B X) :NONE)
==>
  :NONE
Note: Deleting unreachable code.
\end{example}
The \kwd{none} is unreachable, because type inference knows that the argument
to \code{foo-a} must be a \code{foo}, and thus can't be \false.  Presumably the
programmer forgot that \code{x} could be \false{} when he wrote the binding for
\code{a}.

Here is an example with an incorrect declaration:
\begin{lisp}
(defun count-a (string)
  (do ((pos 0 (position #\back{a} string :start (1+ pos)))
       (count 0 (1+ count)))
      ((null pos) count)
    (declare (fixnum pos))))
\end{lisp}
This time our note is:
\begin{example}
In: DEFUN COUNT-A
  (DO ((POS 0 #) (COUNT 0 #))
      ((NULL POS) COUNT)
    (DECLARE (FIXNUM POS)))
--> BLOCK LET TAGBODY RETURN-FROM PROGN 
==>
  COUNT
Note: Deleting unreachable code.
\end{example}

The problem here is that \code{pos} can never be null since it is declared a
\code{fixnum}.

It takes some experience with unreachable code notes to be able to
tell what they are trying to say.  In non-obvious cases, the best
thing to do is to call the function in a way that should cause the
unreachable code to be executed.  Either you will get a type error, or
you will find that there truly is no way for the code to be executed.

Not all unreachable code results in a note:

\begin{itemize} 
\item A note is only given when the unreachable code textually appears
  in the original source.  This prevents spurious notes due to the
  optimization of macros and inline functions, but sometimes also
  foregoes a note that would have been useful.
  
\item Since accurate source information is not available for non-list
  forms, there is an element of heuristic in determining whether or
  not to give a note about an atom.  Spurious notes may be given when
  a macro or inline function defines a variable that is also present
  in the calling function.  Notes about \false{} and \true{} are never
  given, since it is too easy to confuse these constants in expanded
  code with ones in the original source.
  
\item Notes are only given about code unreachable due to control flow.
  There is no note when an expression is deleted because its value is
  unused, since this is a common consequence of other optimizations.
\end{itemize}


Somewhat spurious unreachable code notes can also result when a macro
inserts multiple copies of its arguments in different contexts, for
example:
\begin{lisp}
(defmacro t-and-f (var form)
  `(if ,var ,form ,form))

(defun foo (x)
  (t-and-f x (if x "True." "False.")))
\end{lisp}
results in these notes:
\begin{example}
In: DEFUN FOO
  (IF X "True." "False.")
==>
  "False."
Note: Deleting unreachable code.

==>
  "True."
Note: Deleting unreachable code.
\end{example}

It seems like it has deleted both branches of the \code{if}, but it has really
deleted one branch in one copy, and the other branch in the other copy.  Note
that these messages are only spurious in not satisfying the intent of the rule
that notes are only given when the deleted code appears in the original source;
there is always \var{some} code being deleted when a unreachable code note is
printed.


\subsection{Multiple Values Optimization}
\cindex{multiple value optimization}
\cpsubindex{optimization}{multiple value}

Within a function, \python{} implements uses of multiple values
particularly efficiently.  Multiple values can be kept in arbitrary
registers, so using multiple values doesn't imply stack manipulation
and representation conversion.  For example, this code:
\begin{example}
(let ((a (if x (foo x) u))
      (b (if x (bar x) v)))
  ...)
\end{example}
is actually more efficient written this way:
\begin{example}
(multiple-value-bind
    (a b)
    (if x
        (values (foo x) (bar x))
        (values u v))
  ...)
\end{example}

Also, \pxlref{local-call-return} for information on how local call
provides efficient support for multiple function return values.


\subsection{Source to Source Transformation}
\cindex{source-to-source transformation}
\cpsubindex{transformation}{source-to-source}

The compiler implements a number of operation-specific optimizations as
source-to-source transformations.  You will often see unfamiliar code in error
messages, for example:

\begin{lisp}
(defun my-zerop () (zerop x))
\end{lisp}

gives this warning:

\begin{example}
In: DEFUN MY-ZEROP
  (ZEROP X)
==>
  (= X 0)
Warning: Undefined variable: X
\end{example}

The original \code{zerop} has been transformed into a call to
\code{=}.  This transformation is indicated with the same \code{==$>$}
used to mark macro and function inline expansion.  Although it can be
confusing, display of the transformed source is important, since
warnings are given with respect to the transformed source.  This a
more obscure example:

\begin{lisp}
(defun foo (x) (logand 1 x))
\end{lisp}

gives this efficiency note:

\begin{example}
In: DEFUN FOO
  (LOGAND 1 X)
==>
  (LOGAND C::Y C::X)
Note: Forced to do static-function Two-arg-and (cost 53).
      Unable to do inline fixnum arithmetic (cost 1) because:
      The first argument is a INTEGER, not a FIXNUM.
      etc.
\end{example}

Here, the compiler commuted the call to \code{logand}, introducing
temporaries.  The note complains that the \var{first} argument is not
a \code{fixnum}, when in the original call, it was the second
argument.  To make things more confusing, the compiler introduced
temporaries called \code{c::x} and \code{c::y} that are bound to
\code{y} and \code{1}, respectively.

You will also notice source-to-source optimizations when efficiency
notes are enabled (\pxlref{efficiency-notes}.)  When the compiler is
unable to do a transformation that might be possible if there was more
information, then an efficiency note is printed.  For example,
\code{my-zerop} above will also give this efficiency note:
\begin{example}
In: DEFUN FOO
  (ZEROP X)
==>
  (= X 0)
Note: Unable to optimize because:
      Operands might not be the same type, so can't open code.
\end{example}


\subsection{Style Recommendations}
\cindex{style recommendations}

Source level optimization makes possible a clearer and more relaxed programming
style:
\begin{itemize}
  
\item Don't use macros purely to avoid function call.  If you want an
  inline function, write it as a function and declare it inline.  It's
  clearer, less error-prone, and works just as well.
  
\item Don't write macros that try to ``optimize'' their expansion in
  trivial ways such as avoiding binding variables for simple
  expressions.  The compiler does these optimizations too, and is less
  likely to make a mistake.
  
\item Make use of local functions (i.e., \code{labels} or \code{flet})
  and tail-recursion in places where it is clearer.  Local function
  call is faster than full call.
  
\item Avoid setting local variables when possible.  Binding a new
  \code{let} variable is at least as efficient as setting an existing
  variable, and is easier to understand, both for the compiler and the
  programmer.
  
\item Instead of writing similar code over and over again so that it
  can be hand customized for each use, define a macro or inline
  function, and let the compiler do the work.
\end{itemize}


\section{Tail Recursion}
\label{tail-recursion}
\cindex{tail recursion}
\cindex{recursion}

A call is tail-recursive if nothing has to be done after the the call
returns, i.e. when the call returns, the returned value is immediately
returned from the calling function.  In this example, the recursive
call to \code{myfun} is tail-recursive:
\begin{lisp}
(defun myfun (x)
  (if (oddp (random x))
      (isqrt x)
      (myfun (1- x))))
\end{lisp}

Tail recursion is interesting because it is form of recursion that can be
implemented much more efficiently than general recursion.  In general, a
recursive call requires the compiler to allocate storage on the stack at
run-time for every call that has not yet returned.  This memory consumption
makes recursion unacceptably inefficient for representing repetitive algorithms
having large or unbounded size.  Tail recursion is the special case of
recursion that is semantically equivalent to the iteration constructs normally
used to represent repetition in programs.  Because tail recursion is equivalent
to iteration, tail-recursive programs can be compiled as efficiently as
iterative programs.

So why would you want to write a program recursively when you can write it
using a loop?  Well, the main answer is that recursion is a more general
mechanism, so it can express some solutions simply that are awkward to write as
a loop.  Some programmers also feel that recursion is a stylistically
preferable way to write loops because it avoids assigning variables.
For example, instead of writing:

\begin{lisp}
(defun fun1 (x)
  something-that-uses-x)

(defun fun2 (y)
  something-that-uses-y)

(do ((x something (fun2 (fun1 x))))
    (nil))
\end{lisp}

You can write:

\begin{lisp}
(defun fun1 (x)
  (fun2 something-that-uses-x))

(defun fun2 (y)
  (fun1 something-that-uses-y))

(fun1 something)
\end{lisp}

The tail-recursive definition is actually more efficient, in addition to being
(arguably) clearer.  As the number of functions and the complexity of their
call graph increases, the simplicity of using recursion becomes compelling.
Consider the advantages of writing a large finite-state machine with separate
tail-recursive functions instead of using a single huge \code{prog}.

It helps to understand how to use tail recursion if you think of a
tail-recursive call as a \code{psetq} that assigns the argument values to the
called function's variables, followed by a \code{go} to the start of the called
function.  This makes clear an inherent efficiency advantage of tail-recursive
call: in addition to not having to allocate a stack frame, there is no need to
prepare for the call to return (e.g., by computing a return PC.)

Is there any disadvantage to tail recursion?  Other than an increase
in efficiency, the only way you can tell that a call has been compiled
tail-recursively is if you use the debugger.  Since a tail-recursive
call has no stack frame, there is no way the debugger can print out
the stack frame representing the call.  The effect is that backtrace
will not show some calls that would have been displayed in a
non-tail-recursive implementation.  In practice, this is not as bad as
it sounds\dash{}in fact it isn't really clearly worse, just different.
\xlref{debug-tail-recursion} for information about the debugger
implications of tail recursion, and how to turn it off for the sake of
more conservative backtrace information.

In order to ensure that tail-recursion is preserved in arbitrarily
complex calling patterns across separately compiled functions, the
compiler must compile any call in a tail-recursive position as a
tail-recursive call.  This is done regardless of whether the program
actually exhibits any sort of recursive calling pattern.  In this
example, the call to \code{fun2} will always be compiled as a
tail-recursive call:

\begin{lisp}
(defun fun1 (x)
  (fun2 x))
\end{lisp}

So tail recursion doesn't necessarily have anything to do with recursion
as it is normally thought of.  \xlref{local-tail-recursion} for more
discussion of using tail recursion to implement loops.


\subsection{Tail Recursion Exceptions}

Although \python{} is claimed to be ``properly'' tail-recursive, some
might dispute this, since there are situations where tail recursion is
inhibited:
\begin{itemize}
  
\item When the call is enclosed by a special binding, or
  
\item When the call is enclosed by a \code{catch} or
  \code{unwind-protect}, or
  
\item When the call is enclosed by a \code{block} or \code{tagbody}
  and the block name or \code{go} tag has been closed over.
\end{itemize}
These dynamic extent binding forms inhibit tail recursion because they
allocate stack space to represent the binding.  Shallow-binding
implementations of dynamic scoping also require cleanup code to be
evaluated when the scope is exited.

In addition, optimization of tail-recursive calls is inhibited when
the \code{debug} optimization quality is greater than \code{2}
(\pxlref{debugger-policy}.)


\section{Local Call}
\label{local-call}
\cindex{local call}
\cpsubindex{call}{local}
\cpsubindex{function call}{local}

\python{} supports two kinds of function call: full call and local call.
Full call is the standard calling convention; its late binding and
generality make \llisp{} what it is, but create unavoidable overheads.
When the compiler can compile the calling function and the called
function simultaneously, it can use local call to avoid some of the
overhead of full call.  Local call is really a collection of
compilation strategies.  If some aspect of call overhead is not needed
in a particular local call, then it can be omitted.  In some cases,
local call can be totally free.  Local call provides two main
advantages to the user:
\begin{itemize}
  
\item Local call makes the use of the lexical function binding forms
  \findexed{flet} and \findexed{labels} much more efficient.  A local
  call is always faster than a full call, and in many cases is much
  faster.
  
\item Local call is a natural approach to \textit{block compilation}, a
  compilation technique that resolves function references at compile
  time.  Block compilation speeds function call, but increases
  compilation times and prevents function redefinition.
\end{itemize}



\subsection{Self-Recursive Calls}
\cpsubindex{recursion}{self}

Local call is used when a function defined by \code{defun} calls itself.  For
example:
\begin{lisp}
(defun fact (n)
  (if (zerop n)
      1
      (* n (fact (1- n)))))
\end{lisp}

This use of local call speeds recursion, but can also complicate
debugging, since \findexed{trace} will only show the first call to
\code{fact}, and not the recursive calls.  This is because the
recursive calls directly jump to the start of the function, and don't
indirect through the \code{symbol-function}.  Self-recursive local
call is inhibited when the \kwd{block-compile} argument to
\code{compile-file} is \false{} (\pxlref{compile-file-block}.)


\subsection{Let Calls}
\label{let-calls}
Because local call avoids unnecessary call overheads, the compiler
internally uses local call to implement some macros and special forms
that are not normally thought of as involving a function call.  For
example, this \code{let}:

\begin{example}
(let ((a (foo))
      (b (bar)))
  ...)
\end{example}

is internally represented as though it was macroexpanded into:

\begin{example}
(funcall #'(lambda (a b)
             ...)
         (foo)
         (bar))
\end{example}

This implementation is acceptable because the simple cases of local
call (equivalent to a \code{let}) result in good code.  This doesn't
make \code{let} any more efficient, but does make local calls that are
semantically the same as \code{let} much more efficient than full
calls.  For example, these definitions are all the same as far as the
compiler is concerned:

\begin{example}
(defun foo ()
  ...some other stuff...
  (let ((a something))
    ...some stuff...))

(defun foo ()
  (flet ((localfun (a)
           ...some stuff...))
    ...some other stuff...
    (localfun something)))

(defun foo ()
  (let ((funvar #'(lambda (a)
                    ...some stuff...)))
    ...some other stuff...
    (funcall funvar something)))
\end{example}

Although local call is most efficient when the function is called only
once, a call doesn't have to be equivalent to a \code{let} to be more
efficient than full call.  All local calls avoid the overhead of
argument count checking and keyword argument parsing, and there are a
number of other advantages that apply in many common situations.
\xlref{let-optimization} for a discussion of the optimizations done on
let calls.


\subsection{Closures}
\cindex{closures}

Local call allows for much more efficient use of closures, since the
closure environment doesn't need to be allocated on the heap, or even
stored in memory at all.  In this example, there is no penalty for
\code{localfun} referencing \code{a} and \code{b}:
\begin{lisp}
(defun foo (a b)
  (flet ((localfun (x)
           (1+ (* a b x))))
    (if (= a b)
        (localfun (- x))
        (localfun x))))
\end{lisp}
In local call, the compiler effectively passes closed-over values as
extra arguments, so there is no need for you to ``optimize'' local
function use by explicitly passing in lexically visible values.
Closures may also be subject to let optimization
(\pxlref{let-optimization}.)

Note: indirect value cells are currently always allocated on the heap
when a variable is both assigned to (with \code{setq} or \code{setf})
and closed over, regardless of whether the closure is a local function
or not.  This is another reason to avoid setting variables when you
don't have to.


\subsection{Local Tail Recursion}
\label{local-tail-recursion}
\cindex{tail recursion}
\cpsubindex{recursion}{tail}

Tail-recursive local calls are particularly efficient, since they are
in effect an assignment plus a control transfer.  Scheme programmers
write loops with tail-recursive local calls, instead of using the
imperative \code{go} and \code{setq}.  This has not caught on in the
\clisp{} community, since conventional \llisp{} compilers don't
implement local call.  In \python, users can choose to write loops
such as:
\begin{lisp}
(defun ! (n)
  (labels ((loop (n total)
             (if (zerop n)
                 total
                 (loop (1- n) (* n total)))))
    (loop n 1)))
\end{lisp}

\begin{defmac}{extensions:}{iterate}{%
    \args{\var{name} (\mstar{(\var{var} \var{initial-value})})
      \mstar{\var{declaration}} \mstar{\var{form}}}}
  
  This macro provides syntactic sugar for using \findexed{labels} to
  do iteration.  It creates a local function \var{name} with the
  specified \var{var}s as its arguments and the \var{declaration}s and
  \var{form}s as its body.  This function is then called with the
  \var{initial-values}, and the result of the call is return from the
  macro.

  Here is our factorial example rewritten using \code{iterate}:

  \begin{lisp}
    (defun ! (n)
      (iterate loop
               ((n n)
               (total 1))
        (if (zerop n)
          total
          (loop (1- n) (* n total)))))
  \end{lisp}
      
  The main advantage of using \code{iterate} over \code{do} is that
  \code{iterate} naturally allows stepping to be done differently
  depending on conditionals in the body of the loop.  \code{iterate}
  can also be used to implement algorithms that aren't really
  iterative by simply doing a non-tail call.  For example, the
  standard recursive definition of factorial can be written like this:
\begin{lisp}
(iterate fact
         ((n n))
  (if (zerop n)
      1
      (* n (fact (1- n)))))
\end{lisp}
\end{defmac}


\subsection{Return Values}
\label{local-call-return}
\cpsubindex{return values}{local call}
\cpsubindex{local call}{return values}

One of the more subtle costs of full call comes from allowing
arbitrary numbers of return values.  This overhead can be avoided in
local calls to functions that always return the same number of values.
For efficiency reasons (as well as stylistic ones), you should write
functions so that they always return the same number of values.  This
may require passing extra \false{} arguments to \code{values} in some
cases, but the result is more efficient, not less so.

When efficiency notes are enabled (\pxlref{efficiency-notes}), and the
compiler wants to use known values return, but can't prove that the
function always returns the same number of values, then it will print
a note like this:
\begin{example}
In: DEFUN GRUE
  (DEFUN GRUE (X) (DECLARE (FIXNUM X)) (COND (# #) (# NIL) (T #)))
Note: Return type not fixed values, so can't use known return convention:
  (VALUES (OR (INTEGER -536870912 -1) NULL) &REST T)
\end{example}

In order to implement proper tail recursion in the presence of known
values return (\pxlref{tail-recursion}), the compiler sometimes must
prove that multiple functions all return the same number of values.
When this can't be proven, the compiler will print a note like this:
\begin{example}
In: DEFUN BLUE
  (DEFUN BLUE (X) (DECLARE (FIXNUM X)) (COND (# #) (# #) (# #) (T #)))
Note: Return value count mismatch prevents known return from
      these functions:
  BLUE
  SNOO
\end{example}
\xlref{number-local-call} for the interaction between local call
and the representation of numeric types.


\section{Block Compilation}
\label{block-compilation}
\cindex{block compilation}
\cpsubindex{compilation}{block}

Block compilation allows calls to global functions defined by
\findexed{defun} to be compiled as local calls.  The function call
can be in a different top-level form than the \code{defun}, or even in a
different file.

In addition, block compilation allows the declaration of the \textit{entry points}
to the block compiled portion.  An entry point is any function that may be
called from outside of the block compilation.  If a function is not an entry
point, then it can be compiled more efficiently, since all calls are known at
compile time.  In particular, if a function is only called in one place, then
it will be let converted.  This effectively inline expands the function, but
without the code duplication that results from defining the function normally
and then declaring it inline.

The main advantage of block compilation is that it it preserves efficiency in
programs even when (for readability and syntactic convenience) they are broken
up into many small functions.  There is absolutely no overhead for calling a
non-entry point function that is defined purely for modularity (i.e. called
only in one place.)

Block compilation also allows the use of non-descriptor arguments and return
values in non-trivial programs (\pxlref{number-local-call}).


\subsection{Block Compilation Semantics}

The effect of block compilation can be envisioned as the compiler turning all
the \code{defun}s in the block compilation into a single \code{labels} form:
\begin{example}
(declaim (start-block fun1 fun3))

(defun fun1 ()
  ...)

(defun fun2 ()
  ...
  (fun1)
  ...)

(defun fun3 (x)
  (if x
      (fun1)
      (fun2)))

(declaim (end-block))
\end{example}
becomes:
\begin{example}
(labels ((fun1 ()
           ...)
         (fun2 ()
           ...
           (fun1)
           ...)
         (fun3 (x)
           (if x
               (fun1)
               (fun2))))
  (setf (fdefinition 'fun1) #'fun1)
  (setf (fdefinition 'fun3) #'fun3))
\end{example}
Calls between the block compiled functions are local calls, so changing the
global definition of \code{fun1} will have no effect on what \code{fun2} does;
\code{fun2} will keep calling the old \code{fun1}.

The entry points \code{fun1} and \code{fun3} are still installed in
the \code{symbol-function} as the global definitions of the functions,
so a full call to an entry point works just as before.  However,
\code{fun2} is not an entry point, so it is not globally defined.  In
addition, \code{fun2} is only called in one place, so it will be let
converted.


\subsection{Block Compilation Declarations}
\cpsubindex{declarations}{block compilation}
\cindex{start-block declaration}
\cindex{end-block declaration}

The \code{extensions:start-block} and \code{extensions:end-block}
declarations allow fine-grained control of block compilation.  These
declarations are only legal as a global declarations (\code{declaim}
or \code{proclaim}).

\noindent
\vspace{1 em}
The \code{start-block} declaration has this syntax:
\begin{example}
(start-block \mstar{\var{entry-point-name}})
\end{example}
When processed by the compiler, this declaration marks the start of
block compilation, and specifies the entry points to that block.  If
no entry points are specified, then \var{all} functions are made into
entry points.  If already block compiling, then the compiler ends the
current block and starts a new one.

\noindent
\vspace{1 em}
The \code{end-block} declaration has no arguments:
\begin{lisp}
(end-block)
\end{lisp}
The \code{end-block} declaration ends a block compilation unit without
starting a new one.  This is useful mainly when only a portion of a file
is worth block compiling.


\subsection{Compiler Arguments}
\label{compile-file-block}
\cpsubindex{compile-file}{block compilation arguments}

The \kwd{block-compile} and \kwd{entry-points} arguments to
\code{extensions:compile-from-stream} and \funref{compile-file} provide overall
control of block compilation, and allow block compilation without requiring
modification of the program source.

There are three possible values of the \kwd{block-compile} argument:
\begin{Lentry}
  
\item[\false{}] Do no compile-time resolution of global function
  names, not even for self-recursive calls.  This inhibits any
  \code{start-block} declarations appearing in the file, allowing all
  functions to be incrementally redefined.
  
\item[\true{}] Start compiling in block compilation mode.  This is
  mainly useful for block compiling small files that contain no
  \code{start-block} declarations.  See also the \kwd{entry-points}
  argument.
  
\item[\kwd{specified}] Start compiling in form-at-a-time mode, but
  exploit any \code{start-block} declarations and compile
  self-recursive calls as local calls.  Normally \kwd{specified} is
  the default for this argument (see \varref{block-compile-default}.)
\end{Lentry}

The \kwd{entry-points} argument can be used in conjunction with
\w{\kwd{block-compile} \true{}} to specify the entry-points to a
block-compiled file.  If not specified or \nil, all global functions
will be compiled as entry points.  When \kwd{block-compile} is not
\true, this argument is ignored.

\begin{defvar}{}{block-compile-default}
  
  This variable determines the default value for the
  \kwd{block-compile} argument to \code{compile-file} and
  \code{compile-from-stream}.  The initial value of this variable is
  \kwd{specified}, but \false{} is sometimes useful for totally
  inhibiting block compilation.
\end{defvar}


\subsection{Practical Difficulties}

The main problem with block compilation is that the compiler uses
large amounts of memory when it is block compiling.  This places an
upper limit on the amount of code that can be block compiled as a
unit.  To make best use of block compilation, it is necessary to
locate the parts of the program containing many internal calls, and
then add the appropriate \code{start-block} declarations.  When writing
new code, it is a good idea to put in block compilation declarations
from the very beginning, since writing block declarations correctly
requires accurate knowledge of the program's function call structure.
If you want to initially develop code with full incremental
redefinition, you can compile with \varref{block-compile-default} set to
\false.

Note if a \code{defun} appears in a non-null lexical environment, then
calls to it cannot be block compiled.

Unless files are very small, it is probably impractical to block compile
multiple files as a unit by specifying a list of files to \code{compile-file}.
Semi-inline expansion (\pxlref{semi-inline}) provides another way to
extend block compilation across file boundaries.


\subsection{Context Declarations}
\label{context-declarations}
\cindex{context sensitive declarations}
\cpsubindex{declarations}{context-sensitive}

\cmucl{} has a context-sensitive declaration mechanism which is useful
because it allows flexible control of the compilation policy in large
systems without requiring changes to the source files.  The primary
use of this feature is to allow the exported interfaces of a system to
be compiled more safely than the system internals.  The context used
is the name being defined and the kind of definition (function, macro,
etc.)

The \kwd{context-declarations} option to \macref{with-compilation-unit} has
dynamic scope, affecting all compilation done during the evaluation of the
body.  The argument to this option should evaluate to a list of lists of the
form:
\begin{example}
(\var{context-spec} \mplus{\var{declare-form}})
\end{example}
In the indicated context, the specified declare forms are inserted at
the head of each definition.  The declare forms for all contexts that
match are appended together, with earlier declarations getting
precedence over later ones.  A simple example:
\begin{example}
    :context-declarations
    '((:external (declare (optimize (safety 2)))))
\end{example}
This will cause all functions that are named by external symbols to be
compiled with \code{safety 2}.

The full syntax of context specs is:
\begin{Lentry}
  
\item[\kwd{internal}, \kwd{external}] True if the symbol is internal
  (external) in its home package.
  
\item[\kwd{uninterned}] True if the symbol has no home package.
  
\item[\code{\w{(:package \mstar{\var{package-name}})}}] True if the
  symbol's home package is in any of the named packages (false if
  uninterned.)
  
\item[\kwd{anonymous}] True if the function doesn't have any
  interesting name (not \code{defmacro}, \code{defun}, \code{labels}
  or \code{flet}).
  
\item[\kwd{macro}, \kwd{function}] \kwd{macro} is a global
  (\code{defmacro}) macro.  \kwd{function} is anything else.
  
\item[\kwd{local}, \kwd{global}] \kwd{local} is a \code{labels} or
  \code{flet}.  \kwd{global} is anything else.
  
\item[\code{\w{(:or \mstar{\var{context-spec}})}}] True when any
  supplied \var{context-spec} is true.
  
\item[\code{\w{(:and \mstar{\var{context-spec}})}}] True only when all
  supplied \var{context-spec}s are true.
  
\item[\code{\w{(:not \mstar{\var{context-spec}})}}] True when
  \var{context-spec} is false.
  
\item[\code{\w{(:member \mstar{\var{name}})}}] True when the defined
  name is one of these names (\code{equal} test.)
  
\item[\code{\w{(:match \mstar{\var{pattern}})}}] True when any of the
  patterns is a substring of the name.  The name is wrapped with
  \code{\$}'s, so ``\code{\$FOO}'' matches names beginning with
  ``\code{FOO}'', etc.
\end{Lentry}


\subsection{Context Declaration Example}

Here is a more complex example of \code{with-compilation-unit} options:
\begin{example}
:optimize '(optimize (speed 2) (space 2) (inhibit-warnings 2)
                     (debug 1) (safety 0))
:optimize-interface '(optimize-interface (safety 1) (debug 1))
:context-declarations
'(((:or :external (:and (:match "\%") (:match "SET")))
   (declare (optimize-interface (safety 2))))
  ((:or (:and :external :macro)
        (:match "\$PARSE-"))
   (declare (optimize (safety 2)))))
\end{example}
The \code{optimize} and \code{extensions:optimize-interface}
declarations (\pxlref{optimize-declaration}) set up the global
compilation policy.  The bodies of functions are to be compiled
completely unsafe (\code{safety 0}), but argument count and weakened
argument type checking is to be done when a function is called
(\code{speed 2 safety 1}).

The first declaration specifies that all functions that are external
or whose names contain both ``\code{\%}'' and ``\code{SET}'' are to be
compiled compiled with completely safe interfaces (\code{safety 2}).
The reason for this particular \kwd{match} rule is that \code{setf}
inverse functions in this system tend to have both strings in their
name somewhere.  We want \code{setf} inverses to be safe because they
are implicitly called by users even though their name is not exported.

The second declaration makes external macros or functions whose names
start with ``\code{PARSE-}'' have safe bodies (as well as interfaces).
This is desirable because a syntax error in a macro may cause a type
error inside the body.  The \kwd{match} rule is used because macros
often have auxiliary functions whose names begin with this string.

This particular example is used to build part of the standard \cmucl{}
system.  Note however, that context declarations must be set up
according to the needs and coding conventions of a particular system;
different parts of \cmucl{} are compiled with different context
declarations, and your system will probably need its own declarations.
In particular, any use of the \kwd{match} option depends on naming
conventions used in coding.


\section{Inline Expansion}
\label{inline-expansion}
\cindex{inline expansion}
\cpsubindex{expansion}{inline}
\cpsubindex{call}{inline}
\cpsubindex{function call}{inline}
\cpsubindex{optimization}{function call}

\python{} can expand almost any function inline, including functions
with keyword arguments.  The only restrictions are that keyword
argument keywords in the call must be constant, and that global
function definitions (\code{defun}) must be done in a null lexical
environment (not nested in a \code{let} or other binding form.)  Local
functions (\code{flet}) can be inline expanded in any environment.
Combined with \python{}'s source-level optimization, inline expansion
can be used for things that formerly required macros for efficient
implementation.  In \python, macros don't have any efficiency
advantage, so they need only be used where a macro's syntactic
flexibility is required.

Inline expansion is a compiler optimization technique that reduces
the overhead of a function call by simply not doing the call:
instead, the compiler effectively rewrites the program to appear as
though the definition of the called function was inserted at each
call site.  In \llisp, this is straightforwardly expressed by
inserting the \code{lambda} corresponding to the original definition:
\begin{lisp}
(proclaim '(inline my-1+))
(defun my-1+ (x) (+ x 1))

(my-1+ someval) \result{} ((lambda (x) (+ x 1)) someval)
\end{lisp}

When the function expanded inline is large, the program after inline
expansion may be substantially larger than the original program.  If
the program becomes too large, inline expansion hurts speed rather
than helping it, since hardware resources such as physical memory and
cache will be exhausted.  Inline expansion is called for:
\begin{itemize}
  
\item When profiling has shown that a relatively simple function is
  called so often that a large amount of time is being wasted in the
  calling of that function (as opposed to running in that function.)
  If a function is complex, it will take a long time to run relative
  the time spent in call, so the speed advantage of inline expansion
  is diminished at the same time the space cost of inline expansion is
  increased.  Of course, if a function is rarely called, then the
  overhead of calling it is also insignificant.
  
\item With functions so simple that they take less space to inline
  expand than would be taken to call the function (such as
  \code{my-1+} above.)  It would require intimate knowledge of the
  compiler to be certain when inline expansion would reduce space, but
  it is generally safe to inline expand functions whose definition is
  a single function call, or a few calls to simple \clisp{} functions.
\end{itemize}


In addition to this speed/space tradeoff from inline expansion's
avoidance of the call, inline expansion can also reveal opportunities
for optimization.  \python{}'s extensive source-level optimization can
make use of context information from the caller to tremendously
simplify the code resulting from the inline expansion of a function.

The main form of caller context is local information about the actual
argument values: what the argument types are and whether the arguments
are constant.  Knowledge about argument types can eliminate run-time
type tests (e.g., for generic arithmetic.)  Constant arguments in a
call provide opportunities for constant folding optimization after
inline expansion.

A hidden way that constant arguments are often supplied to functions
is through the defaulting of unsupplied optional or keyword arguments.
There can be a huge efficiency advantage to inline expanding functions
that have complex keyword-based interfaces, such as this definition of
the \code{member} function:
\begin{lisp}
(proclaim '(inline member))
(defun member (item list &key
                    (key #'identity)
                    (test #'eql testp)
                    (test-not nil notp))
  (do ((list list (cdr list)))
      ((null list) nil)
    (let ((car (car list)))
      (if (cond (testp
                 (funcall test item (funcall key car)))
                (notp
                 (not (funcall test-not item (funcall key car))))
                (t
                 (funcall test item (funcall key car))))
          (return list)))))

\end{lisp}
After inline expansion, this call is simplified to the obvious code:
\begin{lisp}
(member a l :key #'foo-a :test #'char=) \result{}

(do ((list list (cdr list)))
    ((null list) nil)
  (let ((car (car list)))
    (if (char= item (foo-a car))
        (return list))))
\end{lisp}
In this example, there could easily be more than an order of magnitude
improvement in speed.  In addition to eliminating the original call to
\code{member}, inline expansion also allows the calls to \code{char=}
and \code{foo-a} to be open-coded.  We go from a loop with three tests
and two calls to a loop with one test and no calls.

\xlref{source-optimization} for more discussion of source level
optimization.


\subsection{Inline Expansion Recording}
\cindex{recording of inline expansions}

Inline expansion requires that the source for the inline expanded function to
be available when calls to the function are compiled.  The compiler doesn't
remember the inline expansion for every function, since that would take an
excessive about of space.  Instead, the programmer must tell the compiler to
record the inline expansion before the definition of the inline expanded
function is compiled.  This is done by globally declaring the function inline
before the function is defined, by using the \code{inline} and
\code{extensions:maybe-inline} (\pxlref{maybe-inline-declaration})
declarations.

In addition to recording the inline expansion of inline functions at the time
the function is compiled, \code{compile-file} also puts the inline expansion in
the output file.  When the output file is loaded, the inline expansion is made
available for subsequent compilations; there is no need to compile the
definition again to record the inline expansion.

If a function is declared inline, but no expansion is recorded, then the
compiler will give an efficiency note like:

\begin{example}
Note: MYFUN is declared inline, but has no expansion.
\end{example}

When you get this note, check that the \code{inline} declaration and the
definition appear before the calls that are to be inline expanded.  This note
will also be given if the inline expansion for a \code{defun} could not be
recorded because the \code{defun} was in a non-null lexical environment.


\subsection{Semi-Inline Expansion}
\label{semi-inline}

\python{} supports \var{semi-inline} functions.  Semi-inline expansion
shares a single copy of a function across all the calls in a component
by converting the inline expansion into a local function
(\pxlref{local-call}.)  This takes up less space when there are
multiple calls, but also provides less opportunity for context
dependent optimization.  When there is only one call, the result is
identical to normal inline expansion.  Semi-inline expansion is done
when the \code{space} optimization quality is \code{0}, and the
function has been declared \code{extensions:maybe-inline}.

This mechanism of inline expansion combined with local call also
allows recursive functions to be inline expanded.  If a recursive
function is declared \code{inline}, calls will actually be compiled
semi-inline.  Although recursive functions are often so complex that
there is little advantage to semi-inline expansion, it can still be
useful in the same sort of cases where normal inline expansion is
especially advantageous, i.e. functions where the calling context can
help a lot.


\subsection{The Maybe-Inline Declaration}
\label{maybe-inline-declaration}
\cindex{maybe-inline declaration}

The \code{extensions:maybe-inline} declaration is a \cmucl{}
extension.  It is similar to \code{inline}, but indicates that inline
expansion may sometimes be desirable, rather than saying that inline
expansion should almost always be done.  When used in a global
declaration, \code{extensions:maybe-inline} causes the expansion for
the named functions to be recorded, but the functions aren't actually
inline expanded unless \code{space} is \code{0} or the function is
eventually (perhaps locally) declared \code{inline}.

Use of the \code{extensions:maybe-inline} declaration followed by the
\code{defun} is preferable to the standard idiom of:
\begin{lisp}
(proclaim '(inline myfun))
(defun myfun () ...)
(proclaim '(notinline myfun))

;;; \textit{Any calls to \code{myfun} here are not inline expanded.}

(defun somefun ()
  (declare (inline myfun))
  ;;
  ;; \textit{Calls to \code{myfun} here are inline expanded.}
  ...)
\end{lisp}
The problem with using \code{notinline} in this way is that in
\clisp{} it does more than just suppress inline expansion, it also
forbids the compiler to use any knowledge of \code{myfun} until a
later \code{inline} declaration overrides the \code{notinline}.  This
prevents compiler warnings about incorrect calls to the function, and
also prevents block compilation.

The \code{extensions:maybe-inline} declaration is used like this:
\begin{lisp}
(proclaim '(extensions:maybe-inline myfun))
(defun myfun () ...)

;;; \textit{Any calls to \code{myfun} here are not inline expanded.}

(defun somefun ()
  (declare (inline myfun))
  ;;
  ;; \textit{Calls to \code{myfun} here are inline expanded.}
  ...)

(defun someotherfun ()
  (declare (optimize (space 0)))
  ;;
  ;; \textit{Calls to \code{myfun} here are expanded semi-inline.}
  ...)
\end{lisp}
In this example, the use of \code{extensions:maybe-inline} causes the
expansion to be recorded when the \code{defun} for \code{somefun} is
compiled, and doesn't waste space through doing inline expansion by
default.  Unlike \code{notinline}, this declaration still allows the
compiler to assume that the known definition really is the one that
will be called when giving compiler warnings, and also allows the
compiler to do semi-inline expansion when the policy is appropriate.

When the goal is merely to control whether inline expansion is done by
default, it is preferable to use \code{extensions:maybe-inline} rather
than \code{notinline}.  The \code{notinline} declaration should be
reserved for those special occasions when a function may be redefined
at run-time, so the compiler must be told that the obvious definition
of a function is not necessarily the one that will be in effect at the
time of the call.


\section{Byte Coded Compilation}
\label{byte-compile}
\cindex{byte coded compilation}
\cindex{space optimization}

\python{} supports byte compilation to reduce the size of Lisp
programs by allowing functions to be compiled more compactly.  Byte
compilation provides an extreme speed/space tradeoff: byte code is
typically six times more compact than native code, but runs fifty
times (or more) slower.  This is about ten times faster than the
standard interpreter, which is itself considered fast in comparison to
other \clisp{} interpreters.

Large Lisp systems (such as \cmucl{} itself) often have large amounts
of user-interface code, compile-time (macro) code, debugging code, or
rarely executed special-case code.  This code is a good target for
byte compilation: very little time is spent running in it, but it can
take up quite a bit of space.  Straight-line code with many function
calls is much more suitable than inner loops.

When byte-compiling, the compiler compiles about twice as fast, and
can produce a hardware independent object file (\file{.bytef} type.)
This file can be loaded like a normal fasl file on any implementation
of \cmucl{} with the same byte-ordering.

The decision to byte compile or native compile can be done on a
per-file or per-code-object basis.  The \kwd{byte-compile} argument to
\funref{compile-file} has these possible values:

\begin{Lentry}
\item[\false{}] Don't byte compile anything in this file.
  
\item[\true{}] Byte compile everything in this file and produce a
  processor-independent \file{.bytef} file.
  
\item[\kwd{maybe}] Produce a normal fasl file, but byte compile any
  functions for which the \code{speed} optimization quality is
  \code{0} and the \code{debug} quality is not greater than \code{1}.
\end{Lentry}

\begin{defvar}{extensions:}{byte-compile-top-level}
  
  If this variable is true (the default) and the \kwd{byte-compile}
  argument to \code{compile-file} is \kwd{maybe}, then byte compile
  top-level code (code outside of any \code{defun}, \code{defmethod},
  etc.)
\end{defvar}

\begin{defvar}{extensions:}{byte-compile-default}
  
  This variable determines the default value for the
  \kwd{byte-compile} argument to \code{compile-file}, initially
  \kwd{maybe}.
\end{defvar}


\section{Object Representation}
\label{object-representation}
\cindex{object representation}
\cpsubindex{representation}{object}
\cpsubindex{efficiency}{of objects}

A somewhat subtle aspect of writing efficient \clisp{} programs is
choosing the correct data structures so that the underlying objects
can be implemented efficiently.  This is partly because of the need
for multiple representations for a given value
(\pxlref{non-descriptor}), but is also due to the sheer number of
object types that \clisp{} has built in.  The number of possible
representations complicates the choice of a good representation
because semantically similar objects may vary in their efficiency
depending on how the program operates on them.


\subsection{Think Before You Use a List}
\cpsubindex{lists}{efficiency of}

Although Lisp's creator seemed to think that it was for LISt
Processing, the astute observer may have noticed that the chapter on
list manipulation makes up less that three percent of \cltltwo{}. The
language has grown since Lisp 1.5\dash{}new data types supersede lists
for many purposes.


\subsection{Structure Representation}
\cpsubindex{structure types}{efficiency of} One of the best ways of
building complex data structures is to define appropriate structure
types using \findexed{defstruct}.  In \python, access of structure
slots is always at least as fast as list or vector access, and is
usually faster.  In comparison to a list representation of a tuple,
structures also have a space advantage.

Even if structures weren't more efficient than other representations, structure
use would still be attractive because programs that use structures in
appropriate ways are much more maintainable and robust than programs written
using only lists.  For example:
\begin{lisp}
(rplaca (caddr (cadddr x)) (caddr y))
\end{lisp}
could have been written using structures in this way:
\begin{lisp}
(setf (beverage-flavor (astronaut-beverage x)) (beverage-flavor y))
\end{lisp}
The second version is more maintainable because it is easier to
understand what it is doing.  It is more robust because structures
accesses are type checked.  An \code{astronaut} will never be confused
with a \code{beverage}, and the result of \code{beverage-flavor} is
always a flavor.  See sections \ref{structure-types} and
\ref{freeze-type} for more information about structure types.
\xlref{type-inference} for a number of examples that make clear the
advantages of structure typing.

Note that the structure definition should be compiled before any uses
of its accessors or type predicate so that these function calls can be
efficiently open-coded.


\subsection{Arrays}
\label{array-types}
\cpsubindex{arrays}{efficiency of}

Arrays are often the most efficient representation for collections of objects
because:
\begin{itemize}
  
\item Array representations are often the most compact.  An array is
  always more compact than a list containing the same number of
  elements.
  
\item Arrays allow fast constant-time access.
  
\item Arrays are easily destructively modified, which can reduce
  consing.
  
\item Array element types can be specialized, which reduces both
  overall size and consing (\pxlref{specialized-array-types}.)
\end{itemize}


Access of arrays that are not of type \code{simple-array} is less
efficient, so declarations are appropriate when an array is of a
simple type like \code{simple-string} or \code{simple-bit-vector}.
Arrays are almost always simple, but the compiler may not be able to
prove simpleness at every use.  The only way to get a non-simple array
is to use the \kwd{displaced-to}, \kwd{fill-pointer} or
\code{adjustable} arguments to \code{make-array}.  If you don't use
these hairy options, then arrays can always be declared to be simple.

Because of the many specialized array types and the possibility of
non-simple arrays, array access is much like generic arithmetic
(\pxlref{generic-arithmetic}).  In order for array accesses to be
efficiently compiled, the element type and simpleness of the array
must be known at compile time.  If there is inadequate information,
the compiler is forced to call a generic array access routine.  You
can detect inefficient array accesses by enabling efficiency notes,
\pxlref{efficiency-notes}.


\subsection{Vectors}
\cpsubindex{vectors}{efficiency of}

Vectors (one dimensional arrays) are particularly useful, since in
addition to their obvious array-like applications, they are also well
suited to representing sequences.  In comparison to a list
representation, vectors are faster to access and take up between two
and sixty-four times less space (depending on the element type.)  As
with arbitrary arrays, the compiler needs to know that vectors are not
complex, so you should use \code{simple-string} in preference to
\code{string}, etc.

The only advantage that lists have over vectors for representing
sequences is that it is easy to change the length of a list, add to it
and remove items from it.  Likely signs of archaic, slow lisp code are
\code{nth} and \code{nthcdr}.  If you are using these functions you
should probably be using a vector.


\subsection{Bit-Vectors}
\cpsubindex{bit-vectors}{efficiency of}

Another thing that lists have been used for is set manipulation.  In
applications where there is a known, reasonably small universe of
items bit-vectors can be used to improve performance.  This is much
less convenient than using lists, because instead of symbols, each
element in the universe must be assigned a numeric index into the bit
vector.  Using a bit-vector will nearly always be faster, and can be
tremendously faster if the number of elements in the set is not small.
The logical operations on \code{simple-bit-vector}s are efficient,
since they operate on a word at a time.


\subsection{Hashtables}
\cpsubindex{hash-tables}{efficiency of}

Hashtables are an efficient and general mechanism for maintaining associations
such as the association between an object and its name.  Although hashtables
are usually the best way to maintain associations, efficiency and style
considerations sometimes favor the use of an association list (a-list).

\code{assoc} is fairly fast when the \var{test} argument is \code{eq}
or \code{eql} and there are only a few elements, but the time goes up
in proportion with the number of elements.  In contrast, the
hash-table lookup has a somewhat higher overhead, but the speed is
largely unaffected by the number of entries in the table.  For an
\code{equal} hash-table or alist, hash-tables have an even greater
advantage, since the test is more expensive.  Whatever you do, be sure
to use the most restrictive test function possible.

The style argument observes that although hash-tables and alists
overlap in function, they do not do all things equally well.
\begin{itemize}
  
\item Alists are good for maintaining scoped environments.  They were
  originally invented to implement scoping in the Lisp interpreter,
  and are still used for this in \python.  With an alist one can
  non-destructively change an association simply by consing a new
  element on the front.  This is something that cannot be done with
  hash-tables.
  
\item Hashtables are good for maintaining a global association.  The
  value associated with an entry can easily be changed with
  \code{setf}.  With an alist, one has to go through contortions,
  either \code{rplacd}'ing the cons if the entry exists, or pushing a
  new one if it doesn't.  The side-effecting nature of hash-table
  operations is an advantage here.
\end{itemize}


Historically, symbol property lists were often used for global name
associations.  Property lists provide an awkward and error-prone
combination of name association and record structure.  If you must use
the property list, please store all the related values in a single
structure under a single property, rather than using many properties.
This makes access more efficient, and also adds a modicum of typing
and abstraction.  \xlref{advanced-type-stuff} for information on types
in \cmucl.


\section{Numbers}
\label{numeric-types}
\cpsubindex{numeric}{types}
\cpsubindex{types}{numeric}

Numbers are interesting because numbers are one of the few \llisp{} data types
that have direct support in conventional hardware.  If a number can be
represented in the way that the hardware expects it, then there is a big
efficiency advantage.

Using hardware representations is problematical in \llisp{} due to
dynamic typing (where the type of a value may be unknown at compile
time.)  It is possible to compile code for statically typed portions
of a \llisp{} program with efficiency comparable to that obtained in
statically typed languages such as C, but not all \llisp{}
implementations succeed.  There are two main barriers to efficient
numerical code in \llisp{}:
\begin{itemize}
  
\item The compiler must prove that the numerical expression is in fact
  statically typed, and
  
\item The compiler must be able to somehow reconcile the conflicting
  demands of the hardware mandated number representation with the
  \llisp{} requirements of dynamic typing and garbage-collecting
  dynamic storage allocation.
\end{itemize}

Because of its type inference (\pxlref{type-inference}) and efficiency
notes (\pxlref{efficiency-notes}), \python{} is better than
conventional \llisp{} compilers at ensuring that numerical expressions
are statically typed.  \python{} also goes somewhat farther than existing
compilers in the area of allowing native machine number
representations in the presence of garbage collection.


\subsection{Descriptors}
\cpsubindex{descriptors}{object}
\cindex{object representation}
\cpsubindex{representation}{object}
\cpsubindex{consing}{overhead of}

\llisp{}'s dynamic typing requires that it be possible to represent
any value with a fixed length object, known as a \var{descriptor}.
This fixed-length requirement is implicit in features such as:
\begin{itemize}
  
\item Data types (like \code{simple-vector}) that can contain any type
  of object, and that can be destructively modified to contain
  different objects (of possibly different types.)
  
\item Functions that can be called with any type of argument, and that
  can be redefined at run time.
\end{itemize}

In order to save space, a descriptor is invariably represented as a
single word.  Objects that can be directly represented in the
descriptor itself are said to be \var{immediate}.  Descriptors for
objects larger than one word are in reality pointers to the memory
actually containing the object.

Representing objects using pointers has two major disadvantages:
\begin{itemize}
  
\item The memory pointed to must be allocated on the heap, so it must
  eventually be freed by the garbage collector.  Excessive heap
  allocation of objects (or ``consing'') is inefficient in several
  ways.  \xlref{consing}.
  
\item Representing an object in memory requires the compiler to emit
  additional instructions to read the actual value in from memory, and
  then to write the value back after operating on it.
\end{itemize}

The introduction of garbage collection makes things even worse, since
the garbage collector must be able to determine whether a descriptor
is an immediate object or a pointer.  This requires that a few bits in
each descriptor be dedicated to the garbage collector.  The loss of a
few bits doesn't seem like much, but it has a major efficiency
implication\dash{}objects whose natural machine representation is a
full word (integers and single-floats) cannot have an immediate
representation.  So the compiler is forced to use an unnatural
immediate representation (such as \code{fixnum}) or a natural pointer
representation (with the attendant consing overhead.)


\subsection{Non-Descriptor Representations}
\label{non-descriptor}
\cindex{non-descriptor representations}
\cindex{stack numbers}

From the discussion above, we can see that the standard descriptor
representation has many problems, the worst being number consing.
\llisp{} compilers try to avoid these descriptor efficiency problems by using
\var{non-descriptor} representations.  A compiler that uses non-descriptor
representations can compile this function so that it does no number consing:
\begin{lisp}
(defun multby (vec n)
  (declare (type (simple-array single-float (*)) vec)
           (single-float n))
  (dotimes (i (length vec))
    (setf (aref vec i)
          (* n (aref vec i)))))
\end{lisp}
If a descriptor representation were used, each iteration of the loop might
cons two floats and do three times as many memory references.

As its negative definition suggests, the range of possible non-descriptor
representations is large.  The performance improvement from non-descriptor
representation depends upon both the number of types that have non-descriptor
representations and the number of contexts in which the compiler is forced to
use a descriptor representation.

Many \llisp{} compilers support non-descriptor representations for
float types such as \code{single-float} and \code{double-float}
(section \ref{float-efficiency}.)  \python{} adds support for full
word integers (\pxlref{word-integers}), characters
(\pxlref{characters}) and system-area pointers (unconstrained
pointers, \pxlref{system-area-pointers}.)  Many \llisp{} compilers
support non-descriptor representations for variables (section
\ref{ND-variables}) and array elements (section
\ref{specialized-array-types}.)  \python{} adds support for
non-descriptor arguments and return values in local call
(\pxlref{number-local-call}) and structure slots (\pxlref{raw-slots}).


\subsection{Variables}
\label{ND-variables}
\cpsubindex{variables}{non-descriptor}
\cpsubindex{type declarations}{variable}
\cpsubindex{efficiency}{of numeric variables}

In order to use a non-descriptor representation for a variable or
expression intermediate value, the compiler must be able to prove that
the value is always of a particular type having a non-descriptor
representation.  Type inference (\pxlref{type-inference}) often needs
some help from user-supplied declarations.  The best kind of type
declaration is a variable type declaration placed at the binding
point:
\begin{lisp}
(let ((x (car l)))
  (declare (single-float x))
  ...)
\end{lisp}
Use of \code{the}, or of variable declarations not at the binding form
is insufficient to allow non-descriptor representation of the
variable\dash{}with these declarations it is not certain that all
values of the variable are of the right type.  It is sometimes useful
to introduce a gratuitous binding that allows the compiler to change
to a non-descriptor representation, like:
\begin{lisp}
(etypecase x
  ((signed-byte 32)
   (let ((x x))
     (declare (type (signed-byte 32) x)) 
     ...))
  ...)
\end{lisp}
The declaration on the inner \code{x} is necessary here due to a phase
ordering problem.  Although the compiler will eventually prove that
the outer \code{x} is a \w{\code{(signed-byte 32)}} within that
\code{etypecase} branch, the inner \code{x} would have been optimized
away by that time.  Declaring the type makes let optimization more
cautious.

Note that storing a value into a global (or \code{special}) variable
always forces a descriptor representation.  Wherever possible, you
should operate only on local variables, binding any referenced globals
to local variables at the beginning of the function, and doing any
global assignments at the end.

Efficiency notes signal use of inefficient representations, so
programmer's needn't continuously worry about the details of
representation selection (\pxlref{representation-eff-note}.)


\subsection{Generic Arithmetic}
\label{generic-arithmetic}
\cindex{generic arithmetic}
\cpsubindex{arithmetic}{generic}
\cpsubindex{numeric}{operation efficiency}

In \clisp, arithmetic operations are \var{generic}.\footnote{As Steele
  notes in CLTL II, this is a generic conception of generic, and is
  not to be confused with the CLOS concept of a generic function.}
The \code{+} function can be passed \code{fixnum}s, \code{bignum}s,
\code{ratio}s, and various kinds of \code{float}s and
\code{complex}es, in any combination.  In addition to the inherent
complexity of \code{bignum} and \code{ratio} operations, there is also
a lot of overhead in just figuring out which operation to do and what
contagion and canonicalization rules apply.  The complexity of generic
arithmetic is so great that it is inconceivable to open code it.
Instead, the compiler does a function call to a generic arithmetic
routine, consuming many instructions before the actual computation
even starts.

This is ridiculous, since even \llisp{} programs do a lot of
arithmetic, and the hardware is capable of doing operations on small
integers and floats with a single instruction.  To get acceptable
efficiency, the compiler special-cases uses of generic arithmetic that
are directly implemented in the hardware.  In order to open code
arithmetic, several constraints must be met:
\begin{itemize}
  
\item All the arguments must be known to be a good type of number.
  
\item The result must be known to be a good type of number.
  
\item Any intermediate values such as the result of \w{\code{(+ a b)}}
  in the call \w{\code{(+ a b c)}} must be known to be a good type of
  number.
  
\item All the above numbers with good types must be of the \var{same}
  good type.  Don't try to mix integers and floats or different float
  formats.
\end{itemize}

The ``good types'' are \w{\code{(signed-byte 32)}},
\w{\code{(unsigned-byte 32)}}, \code{single-float},
\code{double-float}, \code{(complex single-float)}, and \code{(complex
  double-float)}.  See sections \ref{fixnums}, \ref{word-integers}
and \ref{float-efficiency} for more discussion of good numeric types.

\code{float} is not a good type, since it might mean either
\code{single-float} or \code{double-float}.  \code{integer} is not a
good type, since it might mean \code{bignum}.  \code{rational} is not
a good type, since it might mean \code{ratio}.  Note however that
these types are still useful in declarations, since type inference may
be able to strengthen a weak declaration into a good one, when it
would be at a loss if there was no declaration at all
(\pxlref{type-inference}).  The \code{integer} and
\code{unsigned-byte} (or non-negative integer) types are especially
useful in this regard, since they can often be strengthened to a good
integer type.

% Arithmetic with \code{complex} numbers is inefficient in comparison to
% float and integer arithmetic.  Complex numbers are always represented
% with a pointer descriptor (causing consing overhead), and complex
% arithmetic is always closed coded using the general generic arithmetic
As noted above, \cmucl{} has support for \code{(complex single-float)}
and \code{(complex double-float)}.  These can be unboxed and, thus,
are quite efficient.  However, arithmetic with complex types such as:
\begin{lisp}
(complex float)
(complex fixnum)
\end{lisp}
will be significantly slower than the good complex types but is still
faster than \code{bignum} or \code{ratio} arithmetic, since the
implementation is much simpler.

Note: don't use \code{/} to divide integers unless you want the
overhead of rational arithmetic.  Use \code{truncate} even when you
know that the arguments divide evenly.

You don't need to remember all the rules for how to get open-coded
arithmetic, since efficiency notes will tell you when and where there
is a problem\dash{}\pxlref{efficiency-notes}.


\subsection{Fixnums}
\label{fixnums}
\cindex{fixnums}
\cindex{bignums}

A fixnum is a ``FIXed precision NUMber''.  In modern \llisp{}
implementations, fixnums can be represented with an immediate
descriptor, so operating on fixnums requires no consing or memory
references.  Clever choice of representations also allows some
arithmetic operations to be done on fixnums using hardware supported
word-integer instructions, somewhat reducing the speed penalty for
using an unnatural integer representation.

It is useful to distinguish the \code{fixnum} type from the fixnum
representation of integers.  In \python, there is absolutely nothing
magical about the \code{fixnum} type in comparison to other finite
integer types.  \code{fixnum} is equivalent to (is defined with
\code{deftype} to be) \w{\code{(signed-byte 30)}}.  \code{fixnum} is
simply the largest subset of integers that {\em can be represented}
using an immediate fixnum descriptor.

Unlike in other \clisp{} compilers, it is in no way desirable to use
the \code{fixnum} type in declarations in preference to more
restrictive integer types such as \code{bit}, \w{\code{(integer -43
    7)}} and \w{\code{(unsigned-byte 8)}}.  Since \python{} does
understand these integer types, it is preferable to use the more
restrictive type, as it allows better type inference
(\pxlref{operation-type-inference}.)

The small, efficient fixnum is contrasted with bignum, or ``BIG
NUMber''.  This is another descriptor representation for integers, but
this time a pointer representation that allows for arbitrarily large
integers.  Bignum operations are less efficient than fixnum
operations, both because of the consing and memory reference overheads
of a pointer descriptor, and also because of the inherent complexity
of extended precision arithmetic.  While fixnum operations can often
be done with a single instruction, bignum operations are so complex
that they are always done using generic arithmetic.

A crucial point is that the compiler will use generic arithmetic if it
can't \var{prove} that all the arguments, intermediate values, and
results are fixnums.  With bounded integer types such as
\code{fixnum}, the result type proves to be especially problematical,
since these types are not closed under common arithmetic operations
such as \code{+}, \code{-}, \code{*} and \code{/}.  For example,
\w{\code{(1+ (the fixnum x))}} does not necessarily evaluate to a
\code{fixnum}.  Bignums were added to \llisp{} to get around this
problem, but they really just transform the correctness problem ``if
this add overflows, you will get the wrong answer'' to the efficiency
problem ``if this add \var{might} overflow then your program will run
slowly (because of generic arithmetic.)''

There is just no getting around the fact that the hardware only
directly supports short integers.  To get the most efficient open
coding, the compiler must be able to prove that the result is a good
integer type.  This is an argument in favor of using more restrictive
integer types: \w{\code{(1+ (the fixnum x))}} may not always be a
\code{fixnum}, but \w{\code{(1+ (the (unsigned-byte 8) x))}} always
is.  Of course, you can also assert the result type by putting in lots
of \code{the} declarations and then compiling with \code{safety}
\code{0}.


\subsection{Word Integers}
\label{word-integers}
\cindex{word integers}

\python{} is unique in its efficient implementation of arithmetic
on full-word integers through non-descriptor representations and open coding.
Arithmetic on any subtype of these types:

\begin{lisp}
(signed-byte 32)
(unsigned-byte 32)
\end{lisp}

is reasonably efficient, although subtypes of \code{fixnum} remain
somewhat more efficient.

If a word integer must be represented as a descriptor, then the
\code{bignum} representation is used, with its associated consing
overhead.  The support for word integers in no way changes the
language semantics, it just makes arithmetic on small bignums vastly
more efficient.  It is fine to do arithmetic operations with mixed
\code{fixnum} and word integer operands; just declare the most
specific integer type you can, and let the compiler decide what
representation to use.

In fact, to most users, the greatest advantage of word integer
arithmetic is that it effectively provides a few guard bits on the
fixnum representation.  If there are missing assertions on
intermediate values in a fixnum expression, the intermediate results
can usually be proved to fit in a word.  After the whole expression is
evaluated, there will often be a fixnum assertion on the final result,
allowing creation of a fixnum result without even checking for
overflow.

The remarks in section \ref{fixnums} about fixnum result type also
apply to word integers; you must be careful to give the compiler
enough information to prove that the result is still a word integer.
This time, though, when we blow out of word integers we land in into
generic bignum arithmetic, which is much worse than sleazing from
\code{fixnum}s to word integers.  Note that mixing
\w{\code{(unsigned-byte 32)}} arguments with arguments of any signed
type (such as \code{fixnum}) is a no-no, since the result might not be
unsigned.


\subsection{Floating Point Efficiency}
\label{float-efficiency}
\cindex{floating point efficiency}

Arithmetic on objects of type \code{single-float} and \code{double-float} is
efficiently implemented using non-descriptor representations and open coding.
As for integer arithmetic, the arguments must be known to be of the same float
type.  Unlike for integer arithmetic, the results and intermediate values
usually take care of themselves due to the rules of float contagion, i.e.
\w{\code{(1+ (the single-float x))}} is always a \code{single-float}.

Although they are not specially implemented, \code{short-float} and
\code{long-float} are also acceptable in declarations, since they are
synonyms for the \code{single-float} and \code{double-float} types,
respectively.

In \cmucl{}, list-style float type specifiers such as
\w{\code{(single-float 0.0 1.0)}} will be used to good effect.

For example, in this function,

\begin{example}
  (defun square (x)
    (declare (type (single-float 0f0 10f0)))
    (* x x))
\end{example}

\python{} can deduce that the
return type of the function \code{square} is \w{\code{(single-float
    0f0 100f0)}}.

Many union types are also supported so that

\begin{example}
  (+ (the (or (integer 1 1) (integer 5 5)) x)
     (the (or (integer 10 10) (integer 20 20)) y))
\end{example}

has the inferred type \code{(or (integer 11 11) (integer 15 15)
  (integer 21 21) (integer 25 25))}.  This also works for
floating-point numbers.  Member types are also supported.
  
\cmucl{} can also infer types for many mathematical functions
including square root, exponential and logarithmic functions,
trignometric functions and their inverses, and hyperbolic functions
and their inverses.  For numeric code, this can greatly enhance
efficiency by allowing the compiler to use specialized versions of
the functions instead of the generic versions.  The greatest benefit 
of this type inference is determining that the result of the
function is real-valued number instead of possibly being
a complex-valued number.

For example, consider the function
\begin{example}
  (defun fun (x)
    (declare (type (single-float (0f0) 100f0) x))
    (values (sqrt x) (log x)))
\end{example}
With this declaration, the compiler can determine that the argument
to \code{sqrt} and \code{log} are always non-negative so that the result 
is always a \code{single-float}.  In fact, the return type for this
function is derived to be \code{(values (single-float 0f0 10f0)
    (single-float * 2f0))}.

If the declaration were reduced to just \w{\code{(declare
    (single-float x))}}, the argument to \code{sqrt} and \code{log}
could be negative.  This forces the use of the generic versions of
these functions because the result could be a complex number.

We note, however, that proper interval arithmetic is not fully
implemented in the compiler so the inferred types may be slightly in
error due to round-off errors.  This round-off error could
accumulate to cause the compiler to erroneously deduce the result
type and cause code to be removed as being
unreachable.\footnote{This, however, has not actually happened, but
  it is a possibility.}%
Thus, the declarations should only be precise enough for the
compiler to deduce that a real-valued argument to a function would
produce a real-valued result.  The efficiency notes
(\pxlref{representation-eff-note}) from the compiler will guide you
on what declarations might be useful.

When a float must be represented as a descriptor, a pointer representation is
used, creating consing overhead.  For this reason, you should try to avoid
situations (such as full call and non-specialized data structures) that force a
descriptor representation.  See sections \ref{specialized-array-types},
\ref{raw-slots} and \ref{number-local-call}.

\xlref{ieee-float} for information on the extensions to support IEEE
floating point.

\subsubsection{Signed Zeroes and Special Functions}

\cmucl{} supports IEEE signed zeroes.  In typical usage, the signed
zeroes are not a problem and can be treated as an unsigned zero.
However, some of the special functions have branch points at zero, so
care must be taken.

For example, suppose we have the function
\begin{example}
  (defun fun (x)
    (declare (type (single-float 0f0) x))
    (log x))
\end{example}
The derived result of the function is \code{(OR SINGLE-FLOAT
(COMPLEX SINGLE-FLOAT))} because the declared values for
\code{x} includes both $-0.0$ and $0.0$ and \code{(log -0.0)} is
actually a complex number.  Because of this, the generic complex log
routine is used.

If the declaration for \code{x} were \code{(single-float (0f0))} so $+0.0$
is not included or \code{(or (single-float (0f0)) (member 0f0))} so
  $+0.0$ is include but not $-0.0$, the derived type would be
  \code{single-float} for both cases.  By declaring \code{x} this way,
  the log can be implemented using a fast real-valued log routine
  instead of the generic log routine.

\cmucl{} implements the branch cuts and values given by
Kahan\footnote{Kahan, W., ``Branch Cuts for Complex Elementary
Functions, or Much Ado About Nothing's Sign Bit''
in Iserles and Powell (eds.) \textit{The State of the Art
in Numerical Analysis}, pp. 165-211, Clarendon
Press, 1987}.
  


\subsection{Specialized Arrays}
\label{specialized-array-types}
\cindex{specialized array types}
\cpsubindex{array types}{specialized}
\cpsubindex{types}{specialized array}

\clisp{} supports specialized array element types through the
\kwd{element-type} argument to \code{make-array}.  When an array has a
specialized element type, only elements of that type can be stored in
the array.  From this restriction comes two major efficiency
advantages:
\begin{itemize}
  
\item A specialized array can save space by packing multiple elements
  into a single word.  For example, a \code{base-char} array can have
  4 elements per word, and a \code{bit} array can have 32.  This
  space-efficient representation is possible because it is not
  necessary to separately indicate the type of each element.
  
\item The elements in a specialized array can be given the same
  non-descriptor representation as the one used in registers and on
  the stack, eliminating the need for representation conversions when
  reading and writing array elements.  For objects with pointer
  descriptor representations (such as floats and word integers) there
  is also a substantial consing reduction because it is not necessary
  to allocate a new object every time an array element is modified.
\end{itemize}


These are the specialized element types currently supported:
\begin{lisp}
bit
(unsigned-byte 2)
(unsigned-byte 4)
(unsigned-byte 8)
(unsigned-byte 16)
(unsigned-byte 32)
(signed-byte 8)
(signed-byte 16)
(signed-byte 30)
(signed-byte 32)
base-character
single-float
double-float
(complex single-float)
(complex double-float)
\end{lisp}

Although a \code{simple-vector} can hold any type of object, \true{}
should still be considered a specialized array type, since arrays with
element type \true{} are specialized to hold descriptors.



When using non-descriptor representations, it is particularly
important to make sure that array accesses are open-coded, since in
addition to the generic operation overhead, efficiency is lost when
the array element is converted to a descriptor so that it can be
passed to (or from) the generic access routine.  You can detect
inefficient array accesses by enabling efficiency notes,
\pxlref{efficiency-notes}.  \xlref{array-types}.


\subsection{Specialized Structure Slots}
\label{raw-slots}
\cpsubindex{structure types}{numeric slots}
\cindex{specialized structure slots}

Structure slots declared by the \kwd{type} \code{defstruct} slot option
to have certain known numeric types are also given non-descriptor
representations.  These types (and subtypes of these types) are supported:
\begin{lisp}
(unsigned-byte 32)
single-float
double-float
(complex single-float)
(complex double-float)
\end{lisp}

The primary advantage of specialized slot representations is a large
reduction spurious memory allocation and access overhead of programs
that intensively use these types.


\subsection{Interactions With Local Call}
\label{number-local-call}
\cpsubindex{local call}{numeric operands}
\cpsubindex{call}{numeric operands}
\cindex{numbers in local call}

Local call has many advantages (\pxlref{local-call}); one relevant to
our discussion here is that local call extends the usefulness of
non-descriptor representations.  If the compiler knows from the
argument type that an argument has a non-descriptor representation,
then the argument will be passed in that representation.  The easiest
way to ensure that the argument type is known at compile time is to
always declare the argument type in the called function, like:
\begin{lisp}
(defun 2+f (x)
  (declare (single-float x))
  (+ x 2.0))
\end{lisp}
The advantages of passing arguments and return values in a non-descriptor
representation are the same as for non-descriptor representations in general:
reduced consing and memory access (\pxlref{non-descriptor}.)  This
extends the applicative programming styles discussed in section
\ref{local-call} to numeric code.  Also, if source files are kept reasonably
small, block compilation can be used to reduce number consing to a minimum.

Note that non-descriptor return values can only be used with the known return
convention (section \ref{local-call-return}.)  If the compiler can't prove that
a function always returns the same number of values, then it must use the
unknown values return convention, which requires a descriptor representation.
Pay attention to the known return efficiency notes to avoid number consing.
 

\subsection{Representation of Characters}
\label{characters}
\cindex{characters}
\cindex{strings}

\python{} also uses a non-descriptor representation for characters when
convenient.  This improves the efficiency of string manipulation, but is
otherwise pretty invisible; characters have an immediate descriptor
representation, so there is not a great penalty for converting a character to a
descriptor.  Nonetheless, it may sometimes be helpful to declare
character-valued variables as \code{base-character}.


\section{General Efficiency Hints}
\label{general-efficiency}
\cpsubindex{efficiency}{general hints}

This section is a summary of various implementation costs and ways to get
around them.  These hints are relatively unrelated to the use of the \python{}
compiler, and probably also apply to most other \llisp{} implementations.  In
each section, there are references to related in-depth discussion.


\subsection{Compile Your Code}
\cpsubindex{compilation}{why to}

At this point, the advantages of compiling code relative to running it
interpreted probably need not be emphasized too much, but remember that
in \cmucl, compiled code typically runs hundreds of times faster than
interpreted code.  Also, compiled (\code{fasl}) files load significantly faster
than source files, so it is worthwhile compiling files which are loaded many
times, even if the speed of the functions in the file is unimportant.

Even disregarding the efficiency advantages, compiled code is as good or better
than interpreted code.  Compiled code can be debugged at the source level (see
chapter \ref{debugger}), and compiled code does more error checking.  For these
reasons, the interpreter should be regarded mainly as an interactive command
interpreter, rather than as a programming language implementation.

\b{Do not} be concerned about the performance of your program until you
see its speed compiled.  Some techniques that make compiled code run
faster make interpreted code run slower.


\subsection{Avoid Unnecessary Consing}
\label{consing}
\cindex{consing}
\cindex{garbage collection}
\cindex{memory allocation}
\cpsubindex{efficiency}{of memory use}


Consing is another name for allocation of storage, as done by the
\code{cons} function (hence its name.)  \code{cons} is by no means the
only function which conses\dash{}so does \code{make-array} and many
other functions.  Arithmetic and function call can also have hidden
consing overheads.  Consing hurts performance in the following ways:
\begin{itemize}
  
\item Consing reduces memory access locality, increasing paging
  activity.
  
\item Consing takes time just like anything else.
  
\item Any space allocated eventually needs to be reclaimed, either by
  garbage collection or by starting a new \code{lisp} process.
\end{itemize}


Consing is not undiluted evil, since programs do things other than
consing, and appropriate consing can speed up the real work.  It would
certainly save time to allocate a vector of intermediate results that
are reused hundreds of times.  Also, if it is necessary to copy a
large data structure many times, it may be more efficient to update
the data structure non-destructively; this somewhat increases update
overhead, but makes copying trivial.

Note that the remarks in section \ref{efficiency-overview} about the
importance of separating tuning from coding also apply to consing
overhead.  The majority of consing will be done by a small portion of
the program.  The consing hot spots are even less predictable than the
CPU hot spots, so don't waste time and create bugs by doing
unnecessary consing optimization.  During initial coding, avoid
unnecessary side-effects and cons where it is convenient.  If
profiling reveals a consing problem, \var{then} go back and fix the
hot spots.

\xlref{non-descriptor} for a discussion of how to avoid number consing
in \python.


\subsection{Complex Argument Syntax}
\cpsubindex{argument syntax}{efficiency}
\cpsubindex{efficiency}{of argument syntax}
\cindex{keyword argument efficiency}
\cindex{rest argument efficiency}

\clisp{} has very powerful argument passing mechanisms.  Unfortunately, two
of the most powerful mechanisms, rest arguments and keyword arguments, have a
significant performance penalty:

\begin{itemize}
\item
With keyword arguments, the called function has to parse the supplied keywords
by iterating over them and checking them against the desired keywords.

\item
With rest arguments, the function must cons a list to hold the arguments.  If a
function is called many times or with many arguments, large amounts of memory
will be allocated.
\end{itemize}

Although rest argument consing is worse than keyword parsing, neither problem
is serious unless thousands of calls are made to such a function.  The use of
keyword arguments is strongly encouraged in functions with many arguments or
with interfaces that are likely to be extended, and rest arguments are often
natural in user interface functions.

Optional arguments have some efficiency advantage over keyword
arguments, but their syntactic clumsiness and lack of extensibility
has caused many \clisp{} programmers to abandon use of optionals
except in functions that have obviously simple and immutable
interfaces (such as \code{subseq}), or in functions that are only
called in a few places.  When defining an interface function to be
used by other programmers or users, use of only required and keyword
arguments is recommended.

Parsing of \code{defmacro} keyword and rest arguments is done at
compile time, so a macro can be used to provide a convenient syntax
with an efficient implementation.  If the macro-expanded form contains
no keyword or rest arguments, then it is perfectly acceptable in inner
loops.

Keyword argument parsing overhead can also be avoided by use of inline
expansion (\pxlref{inline-expansion}) and block compilation (section
\ref{block-compilation}.)

Note: the compiler open-codes most heavily used system functions which have
keyword or rest arguments, so that no run-time overhead is involved.


\subsection{Mapping and Iteration}
\cpsubindex{mapping}{efficiency of}

One of the traditional \llisp{} programming styles is a highly applicative one,
involving the use of mapping functions and many lists to store intermediate
results.  To compute the sum of the square-roots of a list of numbers, one
might say:

\begin{lisp}
(apply #'+ (mapcar #'sqrt list-of-numbers))
\end{lisp}

This programming style is clear and elegant, but unfortunately results
in slow code.  There are two reasons why:

\begin{itemize} 
\item The creation of lists of intermediate results causes much
  consing (see \ref{consing}).
  
\item Each level of application requires another scan down the list.
  Thus, disregarding other effects, the above code would probably take
  twice as long as a straightforward iterative version.
\end{itemize}


An example of an iterative version of the same code:
\begin{lisp}
(do ((num list-of-numbers (cdr num))
     (sum 0 (+ (sqrt (car num)) sum)))
    ((null num) sum))
\end{lisp}

See sections \ref{variable-type-inference} and \ref{let-optimization}
for a discussion of the interactions of iteration constructs with type
inference and variable optimization.  Also, section
\ref{local-tail-recursion} discusses an applicative style of
iteration.


\subsection{Trace Files and Disassembly}
\label{trace-files}
\cindex{trace files}
\cindex{assembly listing}
\cpsubindex{listing files}{trace}
\cindex{Virtual Machine (VM, or IR2) representation}
\cindex{implicit continuation representation (IR1)}
\cpsubindex{continuations}{implicit representation}

In order to write efficient code, you need to know the relative costs
of different operations.  The main reason why writing efficient
\llisp{} code is difficult is that there are so many operations, and
the costs of these operations vary in obscure context-dependent ways.
Although efficiency notes point out some problem areas, the only way
to ensure generation of the best code is to look at the assembly code
output.

The \code{disassemble} function is a convenient way to get the assembly code for a
function, but it can be very difficult to interpret, since the correspondence
with the original source code is weak.  A better (but more awkward) option is
to use the \kwd{trace-file} argument to \code{compile-file} to generate a trace
file.

A trace file is a dump of the compiler's internal representations,
including annotated assembly code.  Each component in the program gets
four pages in the trace file (separated by ``\code{$\hat{ }L$}''):
\begin{itemize}
  
\item The implicit-continuation (or IR1) representation of the
  optimized source.  This is a dump of the flow graph representation
  used for ``source level'' optimizations.  As you will quickly
  notice, it is not really very close to the source.  This
  representation is not very useful to even sophisticated users.
  
\item The Virtual Machine (VM, or IR2) representation of the program.
  This dump represents the generated code as sequences of ``Virtual
  OPerations'' (VOPs.)  This representation is intermediate between
  the source and the assembly code\dash{}each VOP corresponds fairly
  directly to some primitive function or construct, but a given VOP
  also has a fairly predictable instruction sequence.  An operation
  (such as \code{+}) may have multiple implementations with different
  cost and applicability.  The choice of a particular VOP such as
  \code{+/fixnum} or \code{+/single-float} represents this choice of
  implementation.  Once you are familiar with it, the VM
  representation is probably the most useful for determining what
  implementation has been used.
  
\item An assembly listing, annotated with the VOP responsible for
  generating the instructions.  This listing is useful for figuring
  out what a VOP does and how it is implemented in a particular
  context, but its large size makes it more difficult to read.
  
\item A disassembly of the generated code, which has all
  pseudo-operations expanded out, but is not annotated with VOPs.
\end{itemize}


Note that trace file generation takes much space and time, since the trace file
is tens of times larger than the source file.  To avoid huge confusing trace
files and much wasted time, it is best to separate the critical program portion
into its own file and then generate the trace file from this small file.


\section{Efficiency Notes}
\label{efficiency-notes}
\cindex{efficiency notes}
\cpsubindex{notes}{efficiency}
\cindex{tuning}

Efficiency notes are messages that warn the user that the compiler has
chosen a relatively inefficient implementation for some operation.
Usually an efficiency note reflects the compiler's desire for more
type information.  If the type of the values concerned is known to the
programmer, then additional declarations can be used to get a more
efficient implementation.

Efficiency notes are controlled by the
\code{extensions:inhibit-warnings} (\pxlref{optimize-declaration})
optimization quality. When \code{speed} is greater than
\code{extensions:inhibit-warnings}, efficiency notes are enabled.
Note that this implicitly enables efficiency notes whenever
\code{speed} is increased from its default of \code{1}.

Consider this program with an obscure missing declaration:

\begin{lisp}
(defun eff-note (x y z)
  (declare (fixnum x y z))
  (the fixnum (+ x y z)))
\end{lisp}

If compiled with \code{\w{(speed 3) (safety 0)}}, this note is given:

\begin{example}
In: DEFUN EFF-NOTE
  (+ X Y Z)
==>
  (+ (+ X Y) Z)
Note: Forced to do inline (signed-byte 32) arithmetic (cost 3).
      Unable to do inline fixnum arithmetic (cost 2) because:
      The first argument is a (INTEGER -1073741824 1073741822),
      not a FIXNUM.
\end{example}

This efficiency note tells us that the result of the intermediate
computation \code{\w{(+ x y)}} is not known to be a \code{fixnum}, so
the addition of the intermediate sum to \code{z} must be done less
efficiently.  This can be fixed by changing the definition of
\code{eff-note}:

\begin{lisp}
(defun eff-note (x y z)
  (declare (fixnum x y z))
  (the fixnum (+ (the fixnum (+ x y)) z)))
\end{lisp}


\subsection{Type Uncertainty}
\cpsubindex{types}{uncertainty}
\cindex{uncertainty of types}

The main cause of inefficiency is the compiler's lack of adequate
information about the types of function argument and result values.
Many important operations (such as arithmetic) have an inefficient
general (generic) case, but have efficient implementations that can
usually be used if there is sufficient argument type information.

Type efficiency notes are given when a value's type is uncertain.
There is an important distinction between values that are {\em not
known} to be of a good type (uncertain) and values that are {\em known
not} to be of a good type. Efficiency notes are given mainly for the
first case (uncertain types.) If it is clear to the compiler that that
there is not an efficient implementation for a particular function
call, then an efficiency note will only be given if the
\code{extensions:inhibit-warnings} optimization quality is \code{0}
(\pxlref{optimize-declaration}.)

In other words, the default efficiency notes only suggest that you add
declarations, not that you change the semantics of your program so
that an efficient implementation will apply.  For example, compilation
of this form will not give an efficiency note:
\begin{lisp}
(elt (the list l) i)
\end{lisp}
even though a vector access is more efficient than indexing a list.


\subsection{Efficiency Notes and Type Checking}
\cpsubindex{type checking}{efficiency of}
\cpsubindex{efficiency}{of type checking}
\cpsubindex{optimization}{type check}

It is important that the \code{eff-note} example above used
\w{\code{(safety 0)}}.  When type checking is enabled, you may get apparently
spurious efficiency notes.  With \w{\code{(safety 1)}}, the note has this extra
line on the end:

\begin{example}
The result is a (INTEGER -1610612736 1610612733), not a FIXNUM.
\end{example}

This seems strange, since there is a \code{the} declaration on the result of that
second addition.

In fact, the inefficiency is real, and is a consequence of \python{}'s
treating declarations as assertions to be verified.  The compiler
can't assume that the result type declaration is true\dash{}it must
generate the result and then test whether it is of the appropriate
type.

In practice, this means that when you are tuning a program to run
without type checks, you should work from the efficiency notes
generated by unsafe compilation.  If you want code to run efficiently
with type checking, then you should pay attention to all the
efficiency notes that you get during safe compilation.  Since user
supplied output type assertions (e.g., from \code{the}) are
disregarded when selecting operation implementations for safe code,
you must somehow give the compiler information that allows it to prove
that the result truly must be of a good type.  In our example, it
could be done by constraining the argument types more:

\begin{lisp}
(defun eff-note (x y z)
  (declare (type (unsigned-byte 18) x y z))
  (+ x y z))
\end{lisp}

Of course, this declaration is acceptable only if the arguments to \code{eff-note}
always \var{are} \w{\code{(unsigned-byte 18)}} integers.


\subsection{Representation Efficiency Notes}
\label{representation-eff-note}
\cindex{representation efficiency notes}
\cpsubindex{efficiency notes}{for representation}
\cindex{object representation efficiency notes}
\cindex{stack numbers}
\cindex{non-descriptor representations}
\cpsubindex{descriptor representations}{forcing of}

When operating on values that have non-descriptor representations
(\pxlref{non-descriptor}), there can be a substantial time and consing
penalty for converting to and from descriptor representations.  For
this reason, the compiler gives an efficiency note whenever it is
forced to do a representation coercion more expensive than
\varref{efficiency-note-cost-threshold}.

Inefficient representation coercions may be due to type uncertainty,
as in this example:

\begin{lisp}
(defun set-flo (x)
  (declare (single-float x))
  (prog ((var 0.0))
    (setq var (gorp))
    (setq var x)
    (return var)))
\end{lisp}

which produces this efficiency note:

\begin{example}
In: DEFUN SET-FLO
  (SETQ VAR X)
Note: Doing float to pointer coercion (cost 13) from X to VAR.
\end{example}

The variable \code{var} is not known to always hold values of type
\code{single-float}, so a descriptor representation must be used for its value.
In sort of situation, and adding a declaration will eliminate the inefficiency.

Often inefficient representation conversions are not due to type
uncertainty\dash{}instead, they result from evaluating a
non-descriptor expression in a context that requires a descriptor
result:

\begin{itemize} 
\item Assignment to or initialization of any data structure other than
  a specialized array (\pxlref{specialized-array-types}), or
  
\item Assignment to a \code{special} variable, or
  
\item Passing as an argument or returning as a value in any function
  call that is not a local call (\pxlref{number-local-call}.)
\end{itemize}

If such inefficient coercions appear in a ``hot spot'' in the program, data
structures redesign or program reorganization may be necessary to improve
efficiency.  See sections \ref{block-compilation}, \ref{numeric-types} and
\ref{profiling}.

Because representation selection is done rather late in compilation,
the source context in these efficiency notes is somewhat vague, making
interpretation more difficult.  This is a fairly straightforward
example:

\begin{lisp}
(defun cf+ (x y)
  (declare (single-float x y))
  (cons (+ x y) t))
\end{lisp}

which gives this efficiency note:

\begin{example}
In: DEFUN CF+
  (CONS (+ X Y) T)
Note: Doing float to pointer coercion (cost 13), for:
      The first argument of CONS.
\end{example}

The source context form is almost always the form that receives the value being
coerced (as it is in the preceding example), but can also be the source form
which generates the coerced value.  Compiling this example:

\begin{lisp}
(defun if-cf+ (x y)
  (declare (single-float x y))
  (cons (if (grue) (+ x y) (snoc)) t))
\end{lisp}

produces this note:

\begin{example}
In: DEFUN IF-CF+
  (+ X Y)
Note: Doing float to pointer coercion (cost 13).
\end{example}

In either case, the note's text explanation attempts to include
additional information about what locations are the source and
destination of the coercion.  Here are some example notes:
\begin{example}
  (IF (GRUE) X (SNOC))
Note: Doing float to pointer coercion (cost 13) from X.

  (SETQ VAR X)
Note: Doing float to pointer coercion (cost 13) from X to VAR.
\end{example}
Note that the return value of a function is also a place to which coercions may
have to be done:
\begin{example}
  (DEFUN F+ (X Y) (DECLARE (SINGLE-FLOAT X Y)) (+ X Y))
Note: Doing float to pointer coercion (cost 13) to "<return value>".
\end{example}
Sometimes the compiler is unable to determine a name for the source or
destination, in which case the source context is the only clue.


\subsection{Verbosity Control}
\cpsubindex{verbosity}{of efficiency notes}
\cpsubindex{efficiency notes}{verbosity}

These variables control the verbosity of efficiency notes:

\begin{defvar}{}{efficiency-note-cost-threshold}
  
  Before printing some efficiency notes, the compiler compares the
  value of this variable to the difference in cost between the chosen
  implementation and the best potential implementation.  If the
  difference is not greater than this limit, then no note is printed.
  The units are implementation dependent; the initial value suppresses
  notes about ``trivial'' inefficiencies.  A value of \code{1} will
  note any inefficiency.
\end{defvar}

\begin{defvar}{}{efficiency-note-limit}
  
  When printing some efficiency notes, the compiler reports possible
  efficient implementations.  The initial value of \code{2} prevents
  excessively long efficiency notes in the common case where there is
  no type information, so all implementations are possible.
\end{defvar}


\section{Profiling}
\cindex{profiling}
\cindex{timing}
\cindex{consing}
\cindex{tuning}
\label{profiling}

The first step in improving a program's performance is to profile the
activity of the program to find where it spends its time.  The best
way to do this is to use the profiling utility found in the
\code{profile} package.  This package provides a macro \code{profile}
that encapsulates functions with statistics gathering code.


\subsection{Profile Interface}

\begin{defvar}{profile:}{timed-functions}
  
  This variable holds a list of all functions that are currently being
  profiled.
\end{defvar}

\begin{defmac}{profile:}{profile}{%
    \args{\mstar{\var{name} \mor \kwd{callers} \code{t}}}}
  
  This macro wraps profiling code around the named functions.  As in
  \code{trace}, the \var{name}s are not evaluated.  If a function is
  already profiled, then the function is unprofiled and reprofiled
  (useful to notice function redefinition.)  A warning is printed for
  each name that is not a defined function.
  
  If \kwd{callers \var{t}} is specified, then each function that calls
  this function is recorded along with the number of calls made.
\end{defmac}

\begin{defmac}{profile:}{unprofile}{%
    \args{\mstar{\var{name}}}}
  
  This macro removes profiling code from the named functions.  If no
  \var{name}s are supplied, all currently profiled functions are
  unprofiled.
\end{defmac}

\begin{defmac}{profile:}{profile-all}{%
    \args{\keys{\kwd{package} \kwd{callers-p}}}}
  
  This macro in effect calls \code{profile:profile} for each
  function in the specified package which defaults to
  \code{*package*}.  \kwd{callers-p} has the same meaning as in
  \code{profile:profile}.
\end{defmac}

\begin{defmac}{profile:}{report-time}{\args{\mstar{\var{name}}}}
  
  This macro prints a report for each \var{name}d function of the
  following information:
  \begin{itemize}
  \item The total CPU time used in that function for all calls,
  
  \item the total number of bytes consed in that function for all
    calls,
  
  \item the total number of calls,
  
  \item the average amount of CPU time per call.
  \end{itemize}
  Summary totals of the CPU time, consing and calls columns are
  printed.  An estimate of the profiling overhead is also printed (see
  below).  If no \var{name}s are supplied, then the times for all
  currently profiled functions are printed.
\end{defmac}

\begin{defmac}{}{reset-time}{\args{\mstar{\var{name}}}}
  
  This macro resets the profiling counters associated with the
  \var{name}d functions.  If no \var{name}s are supplied, then all
  currently profiled functions are reset.
\end{defmac}


\subsection{Profiling Techniques}

Start by profiling big pieces of a program, then carefully choose which
functions close to, but not in, the inner loop are to be profiled next.
Avoid profiling functions that are called by other profiled functions, since
this opens the possibility of profiling overhead being included in the reported
times.

If the per-call time reported is less than 1/10 second, then consider the clock
resolution and profiling overhead before you believe the time.  It may be that
you will need to run your program many times in order to average out to a
higher resolution.


\subsection{Nested or Recursive Calls}

The profiler attempts to compensate for nested or recursive calls.  Time and
consing overhead will be charged to the dynamically innermost (most recent)
call to a profiled function.  So profiling a subfunction of a profiled function
will cause the reported time for the outer function to decrease.  However if an
inner function has a large number of calls, some of the profiling overhead may
``leak'' into the reported time for the outer function.  In general, be wary of
profiling short functions that are called many times.


\subsection{Clock resolution}

Unless you are very lucky, the length of your machine's clock ``tick'' is
probably much longer than the time it takes simple function to run.  For
example, on the IBM RT, the clock resolution is 1/50 second.  This means that
if a function is only called a few times, then only the first couple decimal
places are really meaningful.  

Note however, that if a function is called many times, then the statistical
averaging across all calls should result in increased resolution.  For example,
on the IBM RT, if a function is called a thousand times, then a resolution of
tens of microseconds can be expected.

\subsection{Profiling overhead}

The added profiling code takes time to run every time that the profiled
function is called, which can disrupt the attempt to collect timing
information.  In order to avoid serious inflation of the times for functions
that take little time to run, an estimate of the overhead due to profiling is
subtracted from the times reported for each function.

Although this correction works fairly well, it is not totally accurate,
resulting in times that become increasingly meaningless for functions with
short runtimes.  This is only a concern when the estimated profiling overhead
is many times larger than reported total CPU time.

The estimated profiling overhead is not represented in the reported total CPU
time.  The sum of total CPU time and the estimated profiling overhead should be
close to the total CPU time for the entire profiling run (as determined by the
\code{time} macro.)  Time unaccounted for is probably being used by functions that
you forgot to profile.

\subsection{Additional Timing Utilities}

\begin{defmac}{}{time}{ \args{\var{form}}}

  This macro evaluates \var{form}, prints some timing and memory
  allocation information to \code{*trace-output*}, and returns any
  values that \var{form} returns.  The timing information includes
  real time, user run time, and system run time.  This macro executes
  a form and reports the time and consing overhead.  If the
  \code{time} form is not compiled (e.g. it was typed at top-level),
  then \code{compile} will be called on the form to give more accurate
  timing information.  If you really want to time interpreted speed,
  you can say:
\begin{lisp}
(time (eval '\var{form}))
\end{lisp}
Things that execute fairly quickly should be timed more than once,
since there may be more paging overhead in the first timing.  To
increase the accuracy of very short times, you can time multiple
evaluations:
\begin{lisp}
(time (dotimes (i 100) \var{form}))
\end{lisp}
\end{defmac}

\begin{defun}{extensions:}{get-bytes-consed}{}
  
  This function returns the number of bytes allocated since the first
  time you called it.  The first time it is called it returns zero.
  The above profiling routines use this to report consing information.
\end{defun}

\begin{defvar}{extensions:}{gc-run-time}
  
  This variable accumulates the run-time consumed by garbage
  collection, in the units returned by
  \findexed{get-internal-run-time}.
\end{defvar}

\begin{defconst}{}{internal-time-units-per-second}
The value of internal-time-units-per-second is 100.
\end{defconst}

\subsection{A Note on Timing}
\cpsubindex{CPU time}{interpretation of}
\cpsubindex{run time}{interpretation of}
\cindex{interpretation of run time}

There are two general kinds of timing information provided by the
\code{time} macro and other profiling utilities: real time and run
time.  Real time is elapsed, wall clock time.  It will be affected in
a fairly obvious way by any other activity on the machine.  The more
other processes contending for CPU and memory, the more real time will
increase.  This means that real time measurements are difficult to
replicate, though this is less true on a dedicated workstation.  The
advantage of real time is that it is real.  It tells you really how
long the program took to run under the benchmarking conditions.  The
problem is that you don't know exactly what those conditions were.

Run time is the amount of time that the processor supposedly spent
running the program, as opposed to waiting for I/O or running other
processes.  ``User run time'' and ``system run time'' are numbers
reported by the Unix kernel.  They are supposed to be a measure of how
much time the processor spent running your ``user'' program (which
will include GC overhead, etc.), and the amount of time that the
kernel spent running ``on your behalf.''

Ideally, user time should be totally unaffected by benchmarking
conditions; in reality user time does depend on other system activity,
though in rather non-obvious ways.

System time will clearly depend on benchmarking conditions.  In Lisp
benchmarking, paging activity increases system run time (but not by as much
as it increases real time, since the kernel spends some time waiting for
the disk, and this is not run time, kernel or otherwise.)

In my experience, the biggest trap in interpreting kernel/user run time is
to look only at user time.  In reality, it seems that the \var{sum} of kernel
and user time is more reproducible.  The problem is that as system activity
increases, there is a spurious \var{decrease} in user run time.  In effect, as
paging, etc., increases, user time leaks into system time.

So, in practice, the only way to get truly reproducible results is to run
with the same competing activity on the system.  Try to run on a machine
with nobody else logged in, and check with ``ps aux'' to see if there are any
system processes munching large amounts of CPU or memory.  If the ratio
between real time and the sum of user and system time varies much between
runs, then you have a problem.


\subsection{Benchmarking Techniques}
\cindex{benchmarking techniques}

Given these imperfect timing tools, how do should you do benchmarking?  The
answer depends on whether you are trying to measure improvements in the
performance of a single program on the same hardware, or if you are trying to
compare the performance of different programs and/or different hardware.

For the first use (measuring the effect of program modifications with
constant hardware), you should look at \var{both} system+user and real time to
understand what effect the change had on CPU use, and on I/O (including
paging.)  If you are working on a CPU intensive program, the change in
system+user time will give you a moderately reproducible measure of
performance across a fairly wide range of system conditions.  For a CPU
intensive program, you can think of system+user as ``how long it would have
taken to run if I had my own machine.''  So in the case of comparing CPU
intensive programs, system+user time is relatively real, and reasonable to
use.

For programs that spend a substantial amount of their time paging, you
really can't predict elapsed time under a given operating condition without
benchmarking in that condition.  User or system+user time may be fairly
reproducible, but it is also relatively meaningless, since in a paging or
I/O intensive program, the program is spending its time waiting, not
running, and system time and user time are both measures of run time.
A change that reduces run time might increase real time by increasing
paging.

Another common use for benchmarking is comparing the performance of
the same program on different hardware.  You want to know which
machine to run your program on.  For comparing different machines
(operating systems, etc.), the only way to compare that makes sense is
to set up the machines in \var{exactly} the way that they will
\var{normally} be run, and then measure \var{real} time.  If the
program will normally be run along with X, then run X.  If the program
will normally be run on a dedicated workstation, then be sure nobody
else is on the benchmarking machine.  If the program will normally be
run on a machine with three other Lisp jobs, then run three other Lisp
jobs.  If the program will normally be run on a machine with 64MB of
memory, then run with 64MB.  Here, ``normal'' means ``normal for that
machine''.  

If you have a program you believe to be CPU intensive, then you might be
tempted to compare ``run'' times across systems, hoping to get a meaningful
result even if the benchmarking isn't done under the expected running
condition.  Don't to this, for two reasons:

\begin{itemize}  
\item The operating systems might not compute run time in the same
  way.
  
\item Under the real running condition, the program might not be CPU
  intensive after all.
\end{itemize}


In the end, only real time means anything\dash{}it is the amount of time you
have to wait for the result.  The only valid uses for run time are:

\begin{itemize}
\item To develop insight into the program.  For example, if run time
  is much less than elapsed time, then you are probably spending lots
  of time paging.
  
\item To evaluate the relative performance of CPU intensive programs
  in the same environment.
\end{itemize}
