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.
Python’s support for types is unusual in three major ways:
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. Common Lisp 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:
Source level optimization eliminates the need for macros to optimize their expansion, and also increases the effectiveness of inline expansion. See sections 5.4 and 5.8.
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:
The sort of symbolic programs generally written in Common Lisp 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 Common Lisp 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:
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:
See section 5.6.
Sometimes traditional Common Lisp implementation techniques compare so poorly to the techniques used in other languages that Common Lisp can become an impractical language choice. Terrible inefficiencies appear in number-crunching programs, since Common Lisp 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 5.11.2 for more about object representations and numeric types. Also see section 5.13 about efficiency notes.
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:
The best way to get efficient code that is still worth using, is to separate coding from tuning. During coding, you should:
During tuning, you should:
This section goes into more detail describing what types and declarations are recognized by Python. The area where Python differs most radically from previous Common Lisp compilers is in its support for types:
Common Lisp has a very powerful type system, but conventional Common Lisp 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 fixnum will be recognized by the compiler, but a highly specific declaration such as (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 or, member, function, array and number type specifiers. Understanding means that:
For related information, see section 5.11 for numeric types, and section 5.10.3 for array 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:
(or list (member :end)) <==> (or cons (member nil :end))
This has two implications for the user:
The member type specifier can be used to represent “symbolic” values, analogous to the enumerated types of Pascal. For example, the second value of find-symbol has this type:
(member :internal :external :inherited nil)
Member types are very useful for expressing consistency constraints on data structures, for example:
(defstruct ice-cream (flavor :vanilla :type (member :vanilla :chocolate :strawberry)))
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.
The or (union) type specifier is understood, and is meaningfully applied in many contexts. The use of or allows assertions to be made about types in dynamically typed programs. For example:
(defstruct box (next nil :type (or box null)) (top :removed :type (or box-top (member :removed))))
The type assertion on the top slot ensures that an error will be signaled when there is an attempt to store an illegal value (such as :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:
(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))))
Union types are also useful in type inference for representing types that are partially constrained. For example, the result of this expression:
(if foo (logior x y) (list x y))
can be expressed as (or integer cons).
The type nil is also called the empty type, since no object is of type nil. The union of no types, (or), is also empty. Python’s interpretation of an expression whose type is nil 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 error or a use of return is nil. 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 nil, but does in fact return, then (in safe compilation policies) a “NIL Function returned” error will be signaled. See also the function required-argument.
function types are understood in the restrictive sense, specifying:
This is consistent with the new interpretation of function types and the 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 ftype declaration, then Python will compute a function type from the definition, providing a degree of inter-routine type inference, see section 5.3.3.
cmucl supports the values declaration as an extension to Common Lisp. The syntax of the declaration is (values type1 type2…typen). This declaration is semantically equivalent to a the form wrapped around the body of the special form in which the values declaration appears. The advantage of values over the is purely syntactic—it doesn’t introduce more indentation. For example:
(defun foo (x) (declare (values single-float)) (ecase x (:this ...) (:that ...) (:the-other ...)))
is equivalent to:
(defun foo (x) (the single-float (ecase x (:this ...) (:that ...) (:the-other ...))))
(defun floor (number &optional (divisor 1)) (declare (values integer real)) ...)
is equivalent to:
(defun floor (number &optional (divisor 1)) (the (values integer real) ...))
In addition to being recognized by lambda (and hence by defun), the values declaration is recognized by all the other special forms with bodies and declarations: let, let*, labels and 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 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 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 (see section 5.2.6.) This provides a way to declare the types of functions that is much less syntactically awkward than using the ftype declaration with a function type specifier.
Although the 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 declaration proclamation.
Because of precise type checking, structure types are much better supported by Python than by conventional compilers:
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:
(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)))
make-it and use-it are compiled with no checking on the types of the float slots, yet use-it can use single-float arithmetic with perfect safety. Note that make-coordinate must still check the values of x and y unless the call is block compiled or inline expanded (see section 5.6.) 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.
The 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 declaim or proclaim). Currently freeze-type only affects structure type testing done by typep, typecase, etc. Here is an example:
(declaim (freeze-type foo bar))
This asserts that the types foo and 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.
Avoid use of the and, not and 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. and types are effective as long as the intersection can be canonicalized to a type that doesn’t use and. For example:
(and fixnum unsigned-byte)
is fine, since it is the same as:
(integer 0 most-positive-fixnum)
but this type:
(and symbol (not (member :end)))
will not be fully understood by type interference since the and can’t be removed by canonicalization.
Using any of these type specifiers in a type test with typep or typecase is fine, since as tests, these types can be translated into the and macro, the not function or a call to the satisfies predicate.
Python provides good support for some currently unconventional ways of using the Common Lisp 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:
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. See section 5.10.3.(simple-array single-float (1024 1024))
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 Common Lisp compilers, remember that the more precise and comprehensive type declarations are, the more type inference will be able to do.
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 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 and type specifier. In this 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)) ...)
the two declarations for i are intersected, so 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:
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 (see section 5.5) 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 integer:
(defun ! (n res) (declare (integer n res)) (if (zerop n) res (! (1- n) (* n res))))
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 5.6.5.
As described in section 5.2.6, a global function type (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 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:
(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)))
Presumably, the programmer really meant to return res from foo-p, but he seems to have forgotten. When he tries to call do (frob (list ’foo nil)), frob will flame out when it tries to add to a cons. Realizing his error, he fixes 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 foo-p is null, and then proceeded to optimize away the setf in frob.
Fortunately, in this example, the error is detected at compile time due to notes about unreachable code (see section 5.4.5.) 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.
If true (the default), argument and result type information derived from compilation of defuns is used when compiling calls to that function. If false, only information from ftype proclamations will be used.
Many of the standard Common Lisp functions have special type inference procedures that determine the result type as a function of the argument types. For example, the result type of aref is the array element type. Here are some other examples of type inferences:
(logand x #xFF) ==> (unsigned-byte 8) (+ (the (integer 0 12) x) (the (integer 0 1) y)) ==> (integer 0 13) (ash (the (unsigned-byte 16) x) -8) ==> (unsigned-byte 8)
Python uses flow analysis to infer types in dynamically typed programs. For example:
(ecase x (list (length x)) ...)
Here, the compiler knows the argument to length is a list, because the call to length is only done when 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 ifs 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 eq.
If there is an eq (or eql) test, then the compiler will actually substitute one argument for the other in the true branch. For example:
(when (eq x :yow!) (return x))
(when (eq x :yow!) (return :yow!))
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 (not (member :yow!)) in the example above. This can eliminate redundant tests, for example:
(if (eq x nil) ... (if x a b))
is transformed to this:
(if (eq x nil) ... a)
Variables appearing as if tests are interpreted as (not (eq var nil)) tests. The compiler also converts = into eql where possible. It is difficult to do inference directly on = since it does implicit coercions.
When there is an explicit < or > 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 dotimes with a 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:
(defun foo (x) (+ (car x) x))
results in this warning:
In: DEFUN FOO (+ (CAR X) X) ==> X Warning: Result is a LIST, not a NUMBER.
Note that Common Lisp’s dynamic type checking semantics make dynamic type inference useful even in programs that aren’t really dynamically typed, for example:
(+ (car x) (length x))
Here, x presumably always holds a list, but in the absence of a declaration the compiler cannot assume 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 length is always a list because the call to length is only done after the list-specific car operation.
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:
(defstruct foo a b c) (defstruct link (foo (required-argument) :type foo) (next nil :type (or link null))) (foo-a (link-foo x))
Here, there is no need to check that the result of link-foo is a foo, since it always is. Even when some type checks are necessary, type inference can often reduce the number:
(defun test (x) (let ((a (foo-a x)) (b (foo-b x)) (c (foo-c x))) ...))
In this example, only one (foo-p x) check is needed. This applies to a lesser degree in list operations, such as:
(if (eql (car x) 3) (cdr x) y)
Here, we only have to check that 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 car and cdr:
(defun memq (e l) (do ((current l (cdr current))) ((atom current) nil) (when (eq (car current) e) (return current))))
Python reduces the cost of checks that must be done through an optimization called complementing. A complemented check for type is simply a check that the value is not of the type (not type). This is only interesting when something is known about the actual type, in which case we can test for the complement of (and known-type (not type)), or the difference between the known type and the assertion. An example:
(link-foo (link-next x))
Here, we change the type check for link-foo from a test for foo to a test for:
(not (and (or foo null) (not foo)))
or more simply (not null). This is probably the most important use of complementing, since the situation is fairly common, and a 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:
(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)))))
This loop can be compiled with no type checks. The link test for link-foo and link-next is complemented to (not null), and then deleted because of the explicit null test. As before, no check is necessary for foo-a, since the link-foo is always a foo. This sort of situation shows how precise type checking combined with precise declarations can actually result in reduced type checking.
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.
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:
(let ((a #’(lambda (x) (zow x)))) (funcall a 3))
effectively transforms to:
This transformation is done even when the function is a closure, as in:
(let ((a (let ((y (zug))) #’(lambda (x) (zow x y))))) (funcall a 3))
(zow 3 (zug))
A constant variable is a lexical variable that is never assigned to, always keeping its initial value. Whenever possible, avoid setting lexical variables—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:
(let ((x (f x))) ...)
is more efficient than this form:
(setq x (f x)) ...
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 Common Lisp 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.1 For example, this expansion of incf:
(let ((#:g3 (+ x 1))) (setq x #:G3))
(setq x (+ x 1))
The type semantics of this transformation are more important than the elimination of the variable itself. Consider what happens when x is declared to be a fixnum; after the transformation, the compiler can compile the addition knowing that the result is a 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 (see section 5.6.2), 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.
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:
(defconstant limit 42) (defun foo () (... (1- limit) ...))
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.
Constant folding of a user defined function is enabled by the extensions:constant-function proclamation. In this example:
(declaim (ext:constant-function myfun)) (defun myexp (x y) (declare (single-float x y)) (exp (* (log x) y))) ... (myexp 3.0 1.3) ...
The call to myexp is constant-folded to 4.1711674.
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 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:
(let ((a (list (foo) (bar)))) (if t (zow) (raz a)))
(progn (foo) (bar)) (zow)
The most important optimization of control is recognizing when an if test is known at compile time, then deleting the if, the test expression, and the unreachable branch of the 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 nil. Note also, that type inference propagates the result of an if test to the true and false branches, see section 5.3.5.
A related if optimization is this transformation:2
(if (if a b c) x y)
(if a (if b x y) (if c x y))
The opportunity for this sort of optimization usually results from a conditional macro. For example:
(if (not a) x y)
is actually implemented as this:
(if (if a nil t) x y)
which is transformed to this:
(if a (if nil x y) (if t x y))
which is then optimized to this:
(if a y x)
Note that due to Python’s internal representations, the if—if situation will be recognized even if other forms are wrapped around the inner if, like:
(if (let ((g ...)) (loop ... (return (not g)) ...)) x y)
In Python, all the Common Lisp macros really are macros, written in terms of if, block and 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 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.
Python will delete code whenever it can prove that the code can never be executed. Code becomes unreachable when:
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:
(defun foo () (if t (write-line "True.") (write-line "False.")))
will result in this note:
In: DEFUN FOO (WRITE-LINE "False.") Note: Deleting unreachable code.
It is important to pay attention to unreachable code notes, since they often indicate a subtle type error. For example:
(defstruct foo a b) (defun lose (x) (let ((a (foo-a x)) (b (if x (foo-b x) :none))) ...))
results in this note:
In: DEFUN LOSE (IF X (FOO-B X) :NONE) ==> :NONE Note: Deleting unreachable code.
The :none is unreachable, because type inference knows that the argument to foo-a must be a foo, and thus can’t be nil. Presumably the programmer forgot that x could be nil when he wrote the binding for a.
Here is an example with an incorrect declaration:
(defun count-a (string) (do ((pos 0 (position #\a string :start (1+ pos))) (count 0 (1+ count))) ((null pos) count) (declare (fixnum pos))))
This time our note is:
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.
The problem here is that pos can never be null since it is declared a 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:
Somewhat spurious unreachable code notes can also result when a macro inserts multiple copies of its arguments in different contexts, for example:
(defmacro t-and-f (var form) ‘(if ,var ,form ,form)) (defun foo (x) (t-and-f x (if x "True." "False.")))
results in these notes:
In: DEFUN FOO (IF X "True." "False.") ==> "False." Note: Deleting unreachable code. ==> "True." Note: Deleting unreachable code.
It seems like it has deleted both branches of the 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 some code being deleted when a unreachable code note is printed.
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:
(let ((a (if x (foo x) u)) (b (if x (bar x) v))) ...)
is actually more efficient written this way:
(multiple-value-bind (a b) (if x (values (foo x) (bar x)) (values u v)) ...)
Also, see section 5.6.5 for information on how local call provides efficient support for multiple function return values.
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:
(defun my-zerop () (zerop x))
gives this warning:
In: DEFUN MY-ZEROP (ZEROP X) ==> (= X 0) Warning: Undefined variable: X
The original zerop has been transformed into a call to =. This transformation is indicated with the same ==> 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:
(defun foo (x) (logand 1 x))
gives this efficiency note:
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.
Here, the compiler commuted the call to logand, introducing temporaries. The note complains that the first argument is not a fixnum, when in the original call, it was the second argument. To make things more confusing, the compiler introduced temporaries called c::x and c::y that are bound to y and 1, respectively.
You will also notice source-to-source optimizations when efficiency notes are enabled (see section 5.13.) 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, my-zerop above will also give this efficiency note:
In: DEFUN FOO (ZEROP X) ==> (= X 0) Note: Unable to optimize because: Operands might not be the same type, so can’t open code.
Source level optimization makes possible a clearer and more relaxed programming style:
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 myfun is tail-recursive:
(defun myfun (x) (if (oddp (random x)) (isqrt x) (myfun (1- x))))
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:
(defun fun1 (x) something-that-uses-x) (defun fun2 (y) something-that-uses-y) (do ((x something (fun2 (fun1 x)))) (nil))
You can write:
(defun fun1 (x) (fun2 something-that-uses-x)) (defun fun2 (y) (fun1 something-that-uses-y)) (fun1 something)
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 prog.
It helps to understand how to use tail recursion if you think of a tail-recursive call as a psetq that assigns the argument values to the called function’s variables, followed by a 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—in fact it isn’t really clearly worse, just different. See section 3.3.5 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 fun2 will always be compiled as a tail-recursive call:
(defun fun1 (x) (fun2 x))
So tail recursion doesn’t necessarily have anything to do with recursion as it is normally thought of. See section 5.6.4 for more discussion of using tail recursion to implement loops.
Although Python is claimed to be “properly” tail-recursive, some might dispute this, since there are situations where tail recursion is inhibited:
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 debug optimization quality is greater than 2 (see section 3.6.)
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 Common Lisp 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:
Local call is used when a function defined by defun calls itself. For example:
(defun fact (n) (if (zerop n) 1 (* n (fact (1- n)))))
This use of local call speeds recursion, but can also complicate debugging, since trace will only show the first call to 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 symbol-function. Self-recursive local call is inhibited when the :block-compile argument to compile-file is nil (see section 5.7.3.)
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 let:
(let ((a (foo)) (b (bar))) ...)
is internally represented as though it was macroexpanded into:
(funcall #’(lambda (a b) ...) (foo) (bar))
This implementation is acceptable because the simple cases of local call (equivalent to a let) result in good code. This doesn’t make let any more efficient, but does make local calls that are semantically the same as let much more efficient than full calls. For example, these definitions are all the same as far as the compiler is concerned:
(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)))
Although local call is most efficient when the function is called only once, a call doesn’t have to be equivalent to a 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. See section 5.4.1 for a discussion of the optimizations done on let calls.
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 localfun referencing a and b:
(defun foo (a b) (flet ((localfun (x) (1+ (* a b x)))) (if (= a b) (localfun (- x)) (localfun x))))
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 (see section 5.4.1.)
Note: indirect value cells are currently always allocated on the heap when a variable is both assigned to (with setq or 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.
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 go and setq. This has not caught on in the Common Lisp community, since conventional Common Lisp compilers don’t implement local call. In Python, users can choose to write loops such as:
(defun ! (n) (labels ((loop (n total) (if (zerop n) total (loop (1- n) (* n total))))) (loop n 1)))
This macro provides syntactic sugar for using labels to do iteration. It creates a local function name with the specified vars as its arguments and the declarations and forms as its body. This function is then called with the initial-values, and the result of the call is return from the macro.
Here is our factorial example rewritten using iterate:(defun ! (n) (iterate loop ((n n) (total 1)) (if (zerop n) total (loop (1- n) (* n total)))))
The main advantage of using iterate over do is that iterate naturally allows stepping to be done differently depending on conditionals in the body of the loop. 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:(iterate fact ((n n)) (if (zerop n) 1 (* n (fact (1- n)))))
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 nil arguments to values in some cases, but the result is more efficient, not less so.
When efficiency notes are enabled (see section 5.13), 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:
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)
In order to implement proper tail recursion in the presence of known values return (see section 5.5), 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:
In: DEFUN BLUE (DEFUN BLUE (X) (DECLARE (FIXNUM X)) (COND (# #) (# #) (# #) (T #))) Note: Return value count mismatch prevents known return from these functions: BLUE SNOO
See section 5.11.10 for the interaction between local call and the representation of numeric types.
Block compilation allows calls to global functions defined by defun to be compiled as local calls. The function call can be in a different top-level form than the defun, or even in a different file.
In addition, block compilation allows the declaration of the 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 (see section 5.11.10).
The effect of block compilation can be envisioned as the compiler turning all the defuns in the block compilation into a single labels form:
(declaim (start-block fun1 fun3)) (defun fun1 () ...) (defun fun2 () ... (fun1) ...) (defun fun3 (x) (if x (fun1) (fun2))) (declaim (end-block))
(labels ((fun1 () ...) (fun2 () ... (fun1) ...) (fun3 (x) (if x (fun1) (fun2)))) (setf (fdefinition ’fun1) #’fun1) (setf (fdefinition ’fun3) #’fun3))
Calls between the block compiled functions are local calls, so changing the global definition of fun1 will have no effect on what fun2 does; fun2 will keep calling the old fun1.
The entry points fun1 and fun3 are still installed in the symbol-function as the global definitions of the functions, so a full call to an entry point works just as before. However, fun2 is not an entry point, so it is not globally defined. In addition, fun2 is only called in one place, so it will be let converted.
The extensions:start-block and extensions:end-block declarations allow fine-grained control of block compilation. These declarations are only legal as a global declarations (declaim or proclaim).
The start-block declaration has this syntax:
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 all functions are made into entry points. If already block compiling, then the compiler ends the current block and starts a new one.
The end-block declaration has no arguments:
The 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.
The :block-compile and :entry-points arguments to extensions:compile-from-stream and 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 :block-compile argument:
The :entry-points argument can be used in conjunction with :block-compile t 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 :block-compile is not t, this argument is ignored.
This variable determines the default value for the :block-compile argument to compile-file and compile-from-stream. The initial value of this variable is :specified, but nil is sometimes useful for totally inhibiting block compilation.
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 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 *block-compile-default* set to nil.
Note if a 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 compile-file. Semi-inline expansion (see section 5.8.2) provides another way to extend block compilation across file boundaries.
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 :context-declarations option to 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:
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:
:context-declarations ’((:external (declare (optimize (safety 2)))))
This will cause all functions that are named by external symbols to be compiled with safety 2.
The full syntax of context specs is:
Here is a more complex example of with-compilation-unit options:
: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)))))
The optimize and extensions:optimize-interface declarations (see section 4.7.1) set up the global compilation policy. The bodies of functions are to be compiled completely unsafe (safety 0), but argument count and weakened argument type checking is to be done when a function is called (speed 2 safety 1).
The first declaration specifies that all functions that are external or whose names contain both “%” and “SET” are to be compiled compiled with completely safe interfaces (safety 2). The reason for this particular :match rule is that setf inverse functions in this system tend to have both strings in their name somewhere. We want 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 “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 :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 :match option depends on naming conventions used in coding.
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 (defun) must be done in a null lexical environment (not nested in a let or other binding form.) Local functions (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 Common Lisp, this is straightforwardly expressed by inserting the lambda corresponding to the original definition:
(proclaim ’(inline my-1+)) (defun my-1+ (x) (+ x 1)) (my-1+ someval) ==> ((lambda (x) (+ x 1)) someval)
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:
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 member function:
(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)))))
After inline expansion, this call is simplified to the obvious code:
(member a l :key #’foo-a :test #’char=) ==> (do ((list list (cdr list))) ((null list) nil) (let ((car (car list))) (if (char= item (foo-a car)) (return list))))
In this example, there could easily be more than an order of magnitude improvement in speed. In addition to eliminating the original call to member, inline expansion also allows the calls to char= and 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.
See section 5.4 for more discussion of source level optimization.
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 inline and extensions:maybe-inline (see section 5.8.3) declarations.
In addition to recording the inline expansion of inline functions at the time the function is compiled, 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:
Note: MYFUN is declared inline, but has no expansion.
When you get this note, check that the 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 defun could not be recorded because the defun was in a non-null lexical environment.
Python supports 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 (see section 5.6.) 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 space optimization quality is 0, and the function has been declared 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 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.
The extensions:maybe-inline declaration is a cmucl extension. It is similar to 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, extensions:maybe-inline causes the expansion for the named functions to be recorded, but the functions aren’t actually inline expanded unless space is 0 or the function is eventually (perhaps locally) declared inline.
Use of the extensions:maybe-inline declaration followed by the defun is preferable to the standard idiom of:
(proclaim ’(inline myfun)) (defun myfun () ...) (proclaim ’(notinline myfun)) ;;; Any calls to myfun here are not inline expanded. (defun somefun () (declare (inline myfun)) ;; ;; Calls to myfun here are inline expanded. ...)
The problem with using notinline in this way is that in Common Lisp it does more than just suppress inline expansion, it also forbids the compiler to use any knowledge of myfun until a later inline declaration overrides the notinline. This prevents compiler warnings about incorrect calls to the function, and also prevents block compilation.
The extensions:maybe-inline declaration is used like this:
(proclaim ’(extensions:maybe-inline myfun)) (defun myfun () ...) ;;; Any calls to myfun here are not inline expanded. (defun somefun () (declare (inline myfun)) ;; ;; Calls to myfun here are inline expanded. ...) (defun someotherfun () (declare (optimize (space 0))) ;; ;; Calls to myfun here are expanded semi-inline. ...)
In this example, the use of extensions:maybe-inline causes the expansion to be recorded when the defun for somefun is compiled, and doesn’t waste space through doing inline expansion by default. Unlike 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 extensions:maybe-inline rather than notinline. The 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.
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 Common Lisp 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 (.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 :byte-compile argument to compile-file has these possible values:
If this variable is true (the default) and the :byte-compile argument to compile-file is :maybe, then byte compile top-level code (code outside of any defun, defmethod, etc.)
This variable determines the default value for the :byte-compile argument to compile-file, initially :maybe.
A somewhat subtle aspect of writing efficient Common Lisp 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 (see section 5.11.2), but is also due to the sheer number of object types that Common Lisp 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.
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 Common Lisp: The Language II. The language has grown since Lisp 1.5—new data types supersede lists for many purposes.
One of the best ways of building complex data structures is to define appropriate structure types using 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:
(rplaca (caddr (cadddr x)) (caddr y))
could have been written using structures in this way:
(setf (beverage-flavor (astronaut-beverage x)) (beverage-flavor y))
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 astronaut will never be confused with a beverage, and the result of beverage-flavor is always a flavor. See sections 5.2.8 and 5.2.9 for more information about structure types. See section 5.3 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.
Arrays are often the most efficient representation for collections of objects because:
Access of arrays that are not of type simple-array is less efficient, so declarations are appropriate when an array is of a simple type like simple-string or 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 :displaced-to, :fill-pointer or adjustable arguments to 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 (see section 5.11.4). 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, see section 5.13.
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 simple-string in preference to 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 nth and nthcdr. If you are using these functions you should probably be using a vector.
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 simple-bit-vectors are efficient, since they operate on a word at a time.
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).
assoc is fairly fast when the test argument is eq or 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 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.
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. See section 5.2 for information on types in cmucl.
Numbers are interesting because numbers are one of the few Common Lisp 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 Common Lisp 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 Common Lisp program with efficiency comparable to that obtained in statically typed languages such as C, but not all Common Lisp implementations succeed. There are two main barriers to efficient numerical code in Common Lisp:
Because of its type inference (see section 5.3) and efficiency notes (see section 5.13), Python is better than conventional Common Lisp 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.
Common Lisp’s dynamic typing requires that it be possible to represent any value with a fixed length object, known as a descriptor. This fixed-length requirement is implicit in features such as:
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 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:
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—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 fixnum) or a natural pointer representation (with the attendant consing overhead.)
From the discussion above, we can see that the standard descriptor representation has many problems, the worst being number consing. Common Lisp compilers try to avoid these descriptor efficiency problems by using non-descriptor representations. A compiler that uses non-descriptor representations can compile this function so that it does no number consing:
(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)))))
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 Common Lisp compilers support non-descriptor representations for float types such as single-float and double-float (section 5.11.7.) Python adds support for full word integers (see section 5.11.6), characters (see section 5.11.11) and system-area pointers (unconstrained pointers, see section 6.5.) Many Common Lisp compilers support non-descriptor representations for variables (section 5.11.3) and array elements (section 5.11.8.) Python adds support for non-descriptor arguments and return values in local call (see section 5.11.10) and structure slots (see section 5.11.9).
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 (see section 5.3) often needs some help from user-supplied declarations. The best kind of type declaration is a variable type declaration placed at the binding point:
(let ((x (car l))) (declare (single-float x)) ...)
Use of the, or of variable declarations not at the binding form is insufficient to allow non-descriptor representation of the variable—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:
(etypecase x ((signed-byte 32) (let ((x x)) (declare (type (signed-byte 32) x)) ...)) ...)
The declaration on the inner x is necessary here due to a phase ordering problem. Although the compiler will eventually prove that the outer x is a (signed-byte 32) within that etypecase branch, the inner 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 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 (see section 5.13.3.)
In Common Lisp, arithmetic operations are generic.3 The + function can be passed fixnums, bignums, ratios, and various kinds of floats and complexes, in any combination. In addition to the inherent complexity of bignum and 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 Common Lisp 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:
The “good types” are (signed-byte 32), (unsigned-byte 32), single-float, double-float, (complex single-float), and (complex double-float). See sections 5.11.5, 5.11.6 and 5.11.7 for more discussion of good numeric types.
float is not a good type, since it might mean either single-float or double-float. integer is not a good type, since it might mean bignum. rational is not a good type, since it might mean 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 (see section 5.3). The integer and unsigned-byte (or non-negative integer) types are especially useful in this regard, since they can often be strengthened to a good integer type.
As noted above, cmucl has support for (complex single-float) and (complex double-float). These can be unboxed and, thus, are quite efficient. However, arithmetic with complex types such as:
(complex float) (complex fixnum)
will be significantly slower than the good complex types but is still faster than bignum or ratio arithmetic, since the implementation is much simpler.
Note: don’t use / to divide integers unless you want the overhead of rational arithmetic. Use 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—see section 5.13.
A fixnum is a “FIXed precision NUMber”. In modern Common Lisp 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 fixnum type from the fixnum representation of integers. In Python, there is absolutely nothing magical about the fixnum type in comparison to other finite integer types. fixnum is equivalent to (is defined with deftype to be) (signed-byte 30). fixnum is simply the largest subset of integers that can be represented using an immediate fixnum descriptor.
Unlike in other Common Lisp compilers, it is in no way desirable to use the fixnum type in declarations in preference to more restrictive integer types such as bit, (integer -43 7) and (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 (see section 5.3.4.)
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 prove that all the arguments, intermediate values, and results are fixnums. With bounded integer types such as fixnum, the result type proves to be especially problematical, since these types are not closed under common arithmetic operations such as +, -, * and /. For example, (1+ (the fixnum x)) does not necessarily evaluate to a fixnum. Bignums were added to Common Lisp 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 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: (1+ (the fixnum x)) may not always be a fixnum, but (1+ (the (unsigned-byte 8) x)) always is. Of course, you can also assert the result type by putting in lots of the declarations and then compiling with safety 0.
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:
(signed-byte 32) (unsigned-byte 32)
is reasonably efficient, although subtypes of fixnum remain somewhat more efficient.
If a word integer must be represented as a descriptor, then the 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 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 5.11.5 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 fixnums to word integers. Note that mixing (unsigned-byte 32) arguments with arguments of any signed type (such as fixnum) is a no-no, since the result might not be unsigned.
Arithmetic on objects of type single-float and 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. (1+ (the single-float x)) is always a single-float.
Although they are not specially implemented, short-float and long-float are also acceptable in declarations, since they are synonyms for the single-float and double-float types, respectively.
In cmucl, list-style float type specifiers such as (single-float 0.0 1.0) will be used to good effect.
For example, in this function,
(defun square (x) (declare (type (single-float 0f0 10f0))) (* x x))
Python can deduce that the return type of the function square is (single-float 0f0 100f0).
Many union types are also supported so that
(+ (the (or (integer 1 1) (integer 5 5)) x) (the (or (integer 10 10) (integer 20 20)) y))
has the inferred type (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
(defun fun (x) (declare (type (single-float (0f0) 100f0) x)) (values (sqrt x) (log x)))
With this declaration, the compiler can determine that the argument to sqrt and log are always non-negative so that the result is always a single-float. In fact, the return type for this function is derived to be (values (single-float 0f0 10f0) (single-float * 2f0)).
If the declaration were reduced to just (declare (single-float x)), the argument to sqrt and 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.4Thus, 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 (see section 5.13.3) 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 5.11.8, 5.11.9 and 5.11.10.
See section 2.1.2 for information on the extensions to support IEEE floating point.
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
(defun fun (x) (declare (type (single-float 0f0) x)) (log x))
The derived result of the function is (OR SINGLE-FLOAT (COMPLEX SINGLE-FLOAT)) because the declared values for x includes both −0.0 and 0.0 and (log -0.0) is actually a complex number. Because of this, the generic complex log routine is used.
If the declaration for x were (single-float (0f0)) so +0.0 is not included or (or (single-float (0f0)) (member 0f0)) so +0.0 is include but not −0.0, the derived type would be single-float for both cases. By declaring 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 Kahan5.
Common Lisp supports specialized array element types through the :element-type argument to 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:
These are the specialized element types currently supported:
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 ext:double-double-float (complex single-float) (complex double-float) (complex ext:double-double-float)
Although a simple-vector can hold any type of object, t should still be considered a specialized array type, since arrays with element type t 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, see section 5.13. See section 5.10.3.
Structure slots declared by the :type defstruct slot option to have certain known numeric types are also given non-descriptor representations. These types (and subtypes of these types) are supported:
(unsigned-byte 32) single-float double-float (complex single-float) (complex double-float)
The primary advantage of specialized slot representations is a large reduction spurious memory allocation and access overhead of programs that intensively use these types.
Local call has many advantages (see section 5.6); 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:
(defun 2+f (x) (declare (single-float x)) (+ x 2.0))
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 (see section 5.11.2.) This extends the applicative programming styles discussed in section 5.6 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 5.6.5.) 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.
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 base-character.
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 Common Lisp implementations. In each section, there are references to related in-depth discussion.
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 (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 3), 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.
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.
Consing is another name for allocation of storage, as done by the cons function (hence its name.) cons is by no means the only function which conses—so does make-array and many other functions. Arithmetic and function call can also have hidden consing overheads. Consing hurts performance in the following ways:
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 5.1.5 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, then go back and fix the hot spots.
See section 5.11.2 for a discussion of how to avoid number consing in Python.
Common Lisp has very powerful argument passing mechanisms. Unfortunately, two of the most powerful mechanisms, rest arguments and keyword arguments, have a significant performance penalty:
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 Common Lisp programmers to abandon use of optionals except in functions that have obviously simple and immutable interfaces (such as 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 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 (see section 5.8) and block compilation (section 5.7.)
Note: the compiler open-codes most heavily used system functions which have keyword or rest arguments, so that no run-time overhead is involved.
One of the traditional Common Lisp 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:
(apply #’+ (mapcar #’sqrt list-of-numbers))
This programming style is clear and elegant, but unfortunately results in slow code. There are two reasons why:
An example of an iterative version of the same code:
(do ((num list-of-numbers (cdr num)) (sum 0 (+ (sqrt (car num)) sum))) ((null num) sum))
See sections 5.3.1 and 5.4.1 for a discussion of the interactions of iteration constructs with type inference and variable optimization. Also, section 5.6.4 discusses an applicative style of iteration.
In order to write efficient code, you need to know the relative costs of different operations. The main reason why writing efficient Common Lisp 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 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 :trace-file argument to 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 “^L”):
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.
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 extensions:inhibit-warnings (see section 4.7.1) optimization quality. When speed is greater than extensions:inhibit-warnings, efficiency notes are enabled. Note that this implicitly enables efficiency notes whenever speed is increased from its default of 1.
Consider this program with an obscure missing declaration:
(defun eff-note (x y z) (declare (fixnum x y z)) (the fixnum (+ x y z)))
If compiled with (speed 3) (safety 0), this note is given:
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.
This efficiency note tells us that the result of the intermediate computation (+ x y) is not known to be a fixnum, so the addition of the intermediate sum to z must be done less efficiently. This can be fixed by changing the definition of eff-note:
(defun eff-note (x y z) (declare (fixnum x y z)) (the fixnum (+ (the fixnum (+ x y)) z)))
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 not known to be of a good type (uncertain) and values that are 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 extensions:inhibit-warnings optimization quality is 0 (see section 4.7.1.)
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:
(elt (the list l) i)
even though a vector access is more efficient than indexing a list.
It is important that the eff-note example above used (safety 0). When type checking is enabled, you may get apparently spurious efficiency notes. With (safety 1), the note has this extra line on the end:
The result is a (INTEGER -1610612736 1610612733), not a FIXNUM.
This seems strange, since there is a 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—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 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:
(defun eff-note (x y z) (declare (type (unsigned-byte 18) x y z)) (+ x y z))
Of course, this declaration is acceptable only if the arguments to eff-note always are (unsigned-byte 18) integers.
When operating on values that have non-descriptor representations (see section 5.11.2), 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 *efficiency-note-cost-threshold*.
Inefficient representation coercions may be due to type uncertainty, as in this example:
(defun set-flo (x) (declare (single-float x)) (prog ((var 0.0)) (setq var (gorp)) (setq var x) (return var)))
which produces this efficiency note:
In: DEFUN SET-FLO (SETQ VAR X) Note: Doing float to pointer coercion (cost 13) from X to VAR.
The variable var is not known to always hold values of type single-float, so a descriptor representation must be used for its value. In this sort of situation, adding a declaration will eliminate the inefficiency.
Often inefficient representation conversions are not due to type uncertainty—instead, they result from evaluating a non-descriptor expression in a context that requires a descriptor result:
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 5.7, 5.11 and 5.14.
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:
(defun cf+ (x y) (declare (single-float x y)) (cons (+ x y) t))
which gives this efficiency note:
In: DEFUN CF+ (CONS (+ X Y) T) Note: Doing float to pointer coercion (cost 13), for: The first argument of CONS.
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:
(defun if-cf+ (x y) (declare (single-float x y)) (cons (if (grue) (+ x y) (snoc)) t))
produces this note:
In: DEFUN IF-CF+ (+ X Y) Note: Doing float to pointer coercion (cost 13).
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:
(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.
Note that the return value of a function is also a place to which coercions may have to be done:
(DEFUN F+ (X Y) (DECLARE (SINGLE-FLOAT X Y)) (+ X Y)) Note: Doing float to pointer coercion (cost 13) to "<return value>".
Sometimes the compiler is unable to determine a name for the source or destination, in which case the source context is the only clue.
These variables control the verbosity of efficiency notes:
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 1 will note any inefficiency.
When printing some efficiency notes, the compiler reports possible efficient implementations. The initial value of 2 prevents excessively long efficiency notes in the common case where there is no type information, so all implementations are possible.
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 profile package. This package provides a macro profile that encapsulates functions with statistics gathering code.
This variable holds a list of all functions that are currently being profiled.
This macro wraps profiling code around the named functions. As in trace, the names 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 :callers t is specified, then each function that calls this function is recorded along with the number of calls made.
This macro removes profiling code from the named functions. If no names are supplied, all currently profiled functions are unprofiled.
This macro in effect calls profile:profile for each function in the specified package which defaults to *package*. :callers-p has the same meaning as in profile:profile.
This macro prints a report for each named function of the following information:
- The total CPU time used in that function for all calls,
- the total number of bytes consed in that function for all calls,
- the total number of calls,
- the average amount of CPU time per call.
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 names are supplied, then the times for all currently profiled functions are printed.
This macro resets the profiling counters associated with the named functions. If no names are supplied, then all currently profiled functions are reset.
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.
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.
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.
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 time macro.) Time unaccounted for is probably being used by functions that you forgot to profile.
This macro evaluates form, prints some timing and memory allocation information to *trace-output*, and returns any values that 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 time form is not compiled (e.g. it was typed at top-level), then compile will be called on the form to give more accurate timing information. If you really want to time interpreted speed, you can say:(time (eval ’form))
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:(time (dotimes (i 100) form))
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.
This variable accumulates the run-time consumed by garbage collection, in the units returned by get-internal-run-time.
The value of internal-time-units-per-second is 100.
There are two general kinds of timing information provided by the 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 sum of kernel and user time is more reproducible. The problem is that as system activity increases, there is a spurious 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.
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 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 exactly the way that they will normally be run, and then measure 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:
In the end, only real time means anything—it is the amount of time you have to wait for the result. The only valid uses for run time are: