In
computer science
Computer science is the study of computation, information, and automation. Computer science spans Theoretical computer science, theoretical disciplines (such as algorithms, theory of computation, and information theory) to Applied science, ...
, program optimization, code optimization, or software optimization is the process of modifying a software system to make some aspect of it work more
efficiently or use fewer resources. In general, a
computer program
A computer program is a sequence or set of instructions in a programming language for a computer to Execution (computing), execute. It is one component of software, which also includes software documentation, documentation and other intangibl ...
may be optimized so that it executes more rapidly, or to make it capable of operating with less
memory storage or other resources, or draw less power.
Overview
Although the term "optimization" is derived from "optimum", achieving a truly optimal system is rare in practice, which is referred to as
superoptimization. Optimization typically focuses on improving a system with respect to a specific quality metric rather than making it universally optimal. This often leads to trade-offs, where enhancing one metric may come at the expense of another. One popular example is
space-time tradeoff, reducing a program’s execution time by increasing its memory consumption. Conversely, in scenarios where memory is limited, engineers might prioritize a slower
algorithm
In mathematics and computer science, an algorithm () is a finite sequence of Rigour#Mathematics, mathematically rigorous instructions, typically used to solve a class of specific Computational problem, problems or to perform a computation. Algo ...
to conserve space. There is rarely a single design that can excel in all situations, requiring
engineers
Engineers, as practitioners of engineering, are professionals who invent, design, build, maintain and test machines, complex systems, structures, gadgets and materials. They aim to fulfill functional objectives and requirements while consider ...
to prioritize attributes most relevant to the application at hand.
Furthermore, achieving absolute optimization often demands disproportionate effort relative to the benefits gained. Consequently, optimization processes usually stop once sufficient improvements are achieved, without striving for perfection. Fortunately, significant gains often occur early in the optimization process, making it practical to stop before reaching
diminishing returns.
Levels of optimization
Optimization can occur at a number of levels. Typically the higher levels have greater impact, and are harder to change later on in a project, requiring significant changes or a complete rewrite if they need to be changed. Thus optimization can typically proceed via refinement from higher to lower, with initial gains being larger and achieved with less work, and later gains being smaller and requiring more work. However, in some cases overall performance depends on performance of very low-level portions of a program, and small changes at a late stage or early consideration of low-level details can have outsized impact. Typically some consideration is given to efficiency throughout a project though this varies significantly but major optimization is often considered a refinement to be done late, if ever. On longer-running projects there are typically cycles of optimization, where improving one area reveals limitations in another, and these are typically curtailed when performance is acceptable or gains become too small or costly.
As performance is part of the specification of a program a program that is unusably slow is not fit for purpose: a video game with 60 Hz (frames-per-second) is acceptable, but 6 frames-per-second is unacceptably choppy performance is a consideration from the start, to ensure that the system is able to deliver sufficient performance, and early prototypes need to have roughly acceptable performance for there to be confidence that the final system will (with optimization) achieve acceptable performance. This is sometimes omitted in the belief that optimization can always be done later, resulting in prototype systems that are far too slow often by an
order of magnitude or more and systems that ultimately are failures because they architecturally cannot achieve their performance goals, such as the
Intel 432 (1981); or ones that take years of work to achieve acceptable performance, such as Java (1995), which only achieved acceptable performance with
HotSpot (1999). The degree to which performance changes between prototype and production system, and how amenable it is to optimization, can be a significant source of uncertainty and risk.
Design level
At the highest level, the design may be optimized to make best use of the available resources, given goals, constraints, and expected use/load. The architectural design of a system overwhelmingly affects its performance. For example, a system that is network latency-bound (where network latency is the main constraint on overall performance) would be optimized to minimize network trips, ideally making a single request (or no requests, as in a
push protocol) rather than multiple roundtrips. Choice of design depends on the goals: when designing a
compiler
In computing, a compiler is a computer program that Translator (computing), translates computer code written in one programming language (the ''source'' language) into another language (the ''target'' language). The name "compiler" is primaril ...
, if fast compilation is the key priority, a
one-pass compiler is faster than a
multi-pass compiler (assuming same work), but if speed of output code is the goal, a slower multi-pass compiler fulfills the goal better, even though it takes longer itself. Choice of platform and programming language occur at this level, and changing them frequently requires a complete rewrite, though a modular system may allow rewrite of only some component for example, for a Python program one may rewrite performance-critical sections in C. In a distributed system, choice of architecture (
client-server,
peer-to-peer
Peer-to-peer (P2P) computing or networking is a distributed application architecture that partitions tasks or workloads between peers. Peers are equally privileged, equipotent participants in the network, forming a peer-to-peer network of Node ...
, etc.) occurs at the design level, and may be difficult to change, particularly if all components cannot be replaced in sync (e.g., old clients).
Algorithms and data structures
Given an overall design, a good choice of
efficient algorithms and
data structure
In computer science, a data structure is a data organization and storage format that is usually chosen for Efficiency, efficient Data access, access to data. More precisely, a data structure is a collection of data values, the relationships amo ...
s, and efficient implementation of these algorithms and data structures comes next. After design, the choice of
algorithm
In mathematics and computer science, an algorithm () is a finite sequence of Rigour#Mathematics, mathematically rigorous instructions, typically used to solve a class of specific Computational problem, problems or to perform a computation. Algo ...
s and data structures affects efficiency more than any other aspect of the program. Generally data structures are more difficult to change than algorithms, as a data structure assumption and its performance assumptions are used throughout the program, though this can be minimized by the use of
abstract data types in function definitions, and keeping the concrete data structure definitions restricted to a few places.
For algorithms, this primarily consists of ensuring that algorithms are constant O(1), logarithmic O(log ''n''), linear O(''n''), or in some cases log-linear O(''n'' log ''n'') in the input (both in space and time). Algorithms with quadratic complexity O(''n''
2) fail to scale, and even linear algorithms cause problems if repeatedly called, and are typically replaced with constant or logarithmic if possible.
Beyond asymptotic order of growth, the constant factors matter: an asymptotically slower algorithm may be faster or smaller (because simpler) than an asymptotically faster algorithm when they are both faced with small input, which may be the case that occurs in reality. Often a
hybrid algorithm will provide the best performance, due to this tradeoff changing with size.
A general technique to improve performance is to avoid work. A good example is the use of a
fast path for common cases, improving performance by avoiding unnecessary work. For example, using a simple text layout algorithm for Latin text, only switching to a complex layout algorithm for complex scripts, such as
Devanagari
Devanagari ( ; in script: , , ) is an Indic script used in the Indian subcontinent. It is a left-to-right abugida (a type of segmental Writing systems#Segmental systems: alphabets, writing system), based on the ancient ''Brāhmī script, Brā ...
. Another important technique is caching, particularly
memoization, which avoids redundant computations. Because of the importance of caching, there are often many levels of caching in a system, which can cause problems from memory use, and correctness issues from stale caches.
Source code level
Beyond general algorithms and their implementation on an abstract machine, concrete source code level choices can make a significant difference. For example, on early C compilers,
while(1)
was slower than
for(;;)
for an unconditional loop, because
while(1)
evaluated 1 and then had a conditional jump which tested if it was true, while
for (;;)
had an unconditional jump . Some optimizations (such as this one) can nowadays be performed by
optimizing compilers. This depends on the source language, the target machine language, and the compiler, and can be both difficult to understand or predict and changes over time; this is a key place where understanding of compilers and machine code can improve performance.
Loop-invariant code motion and
return value optimization
Return may refer to:
In business, economics, and finance
* Return on investment (ROI), the financial gain after an expense.
* Rate of return, the financial term for the profit or loss derived from an investment
* Tax return, a blank document or ...
are examples of optimizations that reduce the need for auxiliary variables and can even result in faster performance by avoiding round-about optimizations.
Build level
Between the source and compile level,
directives and
build flags can be used to tune performance options in the source code and compiler respectively, such as using
preprocessor
In computer science, a preprocessor (or precompiler) is a Computer program, program that processes its input data to produce output that is used as input in another program. The output is said to be a preprocessed form of the input data, which i ...
defines to disable unneeded software features, optimizing for specific processor models or hardware capabilities, or predicting
branching, for instance. Source-based software distribution systems such as
BSD's
Ports Ports collections (or ports trees, or just ports) are the sets of makefiles and Patch (Unix), patches provided by the BSD-based operating systems, FreeBSD, NetBSD, and OpenBSD, as a simple method of installing software or creating binary packages. T ...
and
Gentoo's
Portage can take advantage of this form of optimization.
Compile level
Use of an
optimizing compiler tends to ensure that the
executable program is optimized at least as much as the compiler can predict.
Assembly level
At the lowest level, writing code using an
assembly language
In computing, assembly language (alternatively assembler language or symbolic machine code), often referred to simply as assembly and commonly abbreviated as ASM or asm, is any low-level programming language with a very strong correspondence bet ...
, designed for a particular hardware platform can produce the most efficient and compact code if the programmer takes advantage of the full repertoire of
machine instructions. Many
operating system
An operating system (OS) is system software that manages computer hardware and software resources, and provides common daemon (computing), services for computer programs.
Time-sharing operating systems scheduler (computing), schedule tasks for ...
s used on
embedded systems have been traditionally written in assembler code for this reason. Programs (other than very small programs) are seldom written from start to finish in assembly due to the time and cost involved. Most are compiled down from a high level language to assembly and hand optimized from there. When efficiency and size are less important large parts may be written in a high-level language.
With more modern
optimizing compilers and the greater complexity of recent
CPUs, it is harder to write more efficient code than what the compiler generates, and few projects need this "ultimate" optimization step.
Much of the code written today is intended to run on as many machines as possible. As a consequence, programmers and compilers don't always take advantage of the more efficient instructions provided by newer CPUs or quirks of older models. Additionally, assembly code tuned for a particular processor without using such instructions might still be suboptimal on a different processor, expecting a different tuning of the code.
Typically today rather than writing in assembly language, programmers will use a
disassembler to analyze the output of a compiler and change the high-level source code so that it can be compiled more efficiently, or understand why it is inefficient.
Run time
Just-in-time compilers can produce customized machine code based on run-time data, at the cost of compilation overhead. This technique dates to the earliest
regular expression
A regular expression (shortened as regex or regexp), sometimes referred to as rational expression, is a sequence of characters that specifies a match pattern in text. Usually such patterns are used by string-searching algorithms for "find" ...
engines, and has become widespread with Java HotSpot and V8 for JavaScript. In some cases
adaptive optimization may be able to perform
run time optimization exceeding the capability of static compilers by dynamically adjusting parameters according to the actual input or other factors.
Profile-guided optimization is an ahead-of-time (AOT) compilation optimization technique based on run time profiles, and is similar to a static "average case" analog of the dynamic technique of adaptive optimization.
Self-modifying code
In computer science, self-modifying code (SMC or SMoC) is source code, code that alters its own instruction (computer science), instructions while it is execution (computing), executing – usually to reduce the instruction path length and imp ...
can alter itself in response to run time conditions in order to optimize code; this was more common in assembly language programs.
Some
CPU designs can perform some optimizations at run time. Some examples include
out-of-order execution
In computer engineering, out-of-order execution (or more formally dynamic execution) is an instruction scheduling paradigm used in high-performance central processing units to make use of instruction cycles that would otherwise be wasted. In t ...
,
speculative execution
Speculative execution is an optimization (computer science), optimization technique where a computer system performs some task that may not be needed. Work is done before it is known whether it is actually needed, so as to prevent a delay that woul ...
,
instruction pipeline
In computer engineering, instruction pipelining is a technique for implementing instruction-level parallelism within a single processor. Pipelining attempts to keep every part of the processor busy with some instruction by dividing incoming Mac ...
s, and
branch predictors. Compilers can help the program take advantage of these CPU features, for example through
instruction scheduling.
Platform dependent and independent optimizations
Code optimization can be also broadly categorized as
platform-dependent and platform-independent techniques. While the latter ones are effective on most or all platforms, platform-dependent techniques use specific properties of one platform, or rely on parameters depending on the single platform or even on the single processor. Writing or producing different versions of the same code for different processors might therefore be needed. For instance, in the case of compile-level optimization, platform-independent techniques are generic techniques (such as
loop unrolling, reduction in function calls, memory efficient routines, reduction in conditions, etc.), that impact most CPU architectures in a similar way. A great example of platform-independent optimization has been shown with inner for loop, where it was observed that a loop with an inner for loop performs more computations per unit time than a loop without it or one with an inner while loop. Generally, these serve to reduce the total
instruction path length
In computer performance, the instruction path length is the number of machine code instructions required to execute a section of a computer program. The total path length for the entire program could be deemed a measure of the algorithm's perfor ...
required to complete the program and/or reduce total memory usage during the process. On the other hand, platform-dependent techniques involve instruction scheduling,
instruction-level parallelism, data-level parallelism, cache optimization techniques (i.e., parameters that differ among various platforms) and the optimal instruction scheduling might be different even on different processors of the same architecture.
Strength reduction
Computational tasks can be performed in several different ways with varying efficiency. A more efficient version with equivalent functionality is known as a
strength reduction. For example, consider the following
C code snippet whose intention is to obtain the sum of all integers from 1 to :
int i, sum = 0;
for (i = 1; i <= N; ++i)
printf("sum: %d\n", sum);
This code can (assuming no
arithmetic overflow) be rewritten using a mathematical formula like:
int sum = N * (1 + N) / 2;
printf("sum: %d\n", sum);
The optimization, sometimes performed automatically by an optimizing compiler, is to select a method (
algorithm
In mathematics and computer science, an algorithm () is a finite sequence of Rigour#Mathematics, mathematically rigorous instructions, typically used to solve a class of specific Computational problem, problems or to perform a computation. Algo ...
) that is more computationally efficient, while retaining the same functionality. See
algorithmic efficiency for a discussion of some of these techniques. However, a significant improvement in performance can often be achieved by removing extraneous functionality.
Optimization is not always an obvious or intuitive process. In the example above, the "optimized" version might actually be slower than the original version if were sufficiently small and the particular hardware happens to be much faster at performing addition and
looping operations than multiplication and division.
Trade-offs
In some cases, however, optimization relies on using more elaborate algorithms, making use of "special cases" and special "tricks" and performing complex trade-offs. A "fully optimized" program might be more difficult to comprehend and hence may contain more
faults than unoptimized versions. Beyond eliminating obvious antipatterns, some code level optimizations decrease maintainability.
Optimization will generally focus on improving just one or two aspects of performance: execution time, memory usage, disk space, bandwidth, power consumption or some other resource. This will usually require a trade-off where one factor is optimized at the expense of others. For example, increasing the size of
cache improves run time performance, but also increases the memory consumption. Other common trade-offs include code clarity and conciseness.
There are instances where the programmer performing the optimization must decide to make the software better for some operations but at the cost of making other operations less efficient. These trade-offs may sometimes be of a non-technical nature such as when a competitor has published a
benchmark result that must be beaten in order to improve commercial success but comes perhaps with the burden of making normal usage of the software less efficient. Such changes are sometimes jokingly referred to as ''pessimizations''.
Bottlenecks
Optimization may include finding a
bottleneck in a system a component that is the limiting factor on performance. In terms of code, this will often be a
hot spot a critical part of the code that is the primary consumer of the needed resource though it can be another factor, such as I/O latency or network bandwidth.
In computer science, resource consumption often follows a form of
power law
In statistics, a power law is a Function (mathematics), functional relationship between two quantities, where a Relative change and difference, relative change in one quantity results in a relative change in the other quantity proportional to the ...
distribution, and the
Pareto principle can be applied to resource optimization by observing that 80% of the resources are typically used by 20% of the operations. In software engineering, it is often a better approximation that 90% of the execution time of a computer program is spent executing 10% of the code (known as the 90/10 law in this context).
More complex algorithms and data structures perform well with many items, while simple algorithms are more suitable for small amounts of data — the setup, initialization time, and constant factors of the more complex algorithm can outweigh the benefit, and thus a
hybrid algorithm or
adaptive algorithm may be faster than any single algorithm. A performance profiler can be used to narrow down decisions about which functionality fits which conditions.
In some cases, adding more
memory
Memory is the faculty of the mind by which data or information is encoded, stored, and retrieved when needed. It is the retention of information over time for the purpose of influencing future action. If past events could not be remembe ...
can help to make a program run faster. For example, a filtering program will commonly read each line and filter and output that line immediately. This only uses enough memory for one line, but performance is typically poor, due to the latency of each disk read. Caching the result is similarly effective, though also requiring larger memory use.
When to optimize
Optimization can reduce
readability and add code that is used only to improve the
performance
A performance is an act or process of staging or presenting a play, concert, or other form of entertainment. It is also defined as the action or process of carrying out or accomplishing an action, task, or function.
Performance has evolved glo ...
. This may complicate programs or systems, making them harder to maintain and debug. As a result, optimization or performance tuning is often performed at the end of the
development stage.
Donald Knuth made the following two statements on optimization:
"We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. Yet we should not pass up our opportunities in that critical 3%"
(He also attributed the quote to
Tony Hoare several years later, although this might have been an error as Hoare disclaims having coined the phrase.)
"In established engineering disciplines a 12% improvement, easily obtained, is never considered marginal and I believe the same viewpoint should prevail in software engineering"
"Premature optimization" is a phrase used to describe a situation where a programmer lets performance considerations affect the design of a piece of code. This can result in a design that is not as clean as it could have been or code that is incorrect, because the code is complicated by the optimization and the programmer is distracted by optimizing.
When deciding whether to optimize a specific part of the program,
Amdahl's Law should always be considered: the impact on the overall program depends very much on how much time is actually spent in that specific part, which is not always clear from looking at the code without a
performance analysis.
A better approach is therefore to design first, code from the design and then
profile/
benchmark the resulting code to see which parts should be optimized. A simple and elegant design is often easier to optimize at this stage, and profiling may reveal unexpected performance problems that would not have been addressed by premature optimization.
In practice, it is often necessary to keep performance goals in mind when first designing software, but the programmer balances the goals of design and optimization.
Modern compilers and operating systems are so efficient that the intended performance increases often fail to materialize. As an example, caching data at the application level that is again cached at the operating system level does not yield improvements in execution. Even so, it is a rare case when the programmer will remove failed optimizations from production code. It is also true that advances in hardware will more often than not obviate any potential improvements, yet the obscuring code will persist into the future long after its purpose has been negated.
Macros
Optimization during code development using
macros takes on different forms in different languages.
In some procedural languages, such as
C and
C++, macros are implemented using token substitution. Nowadays,
inline function
In the C (programming language), C and C++ programming languages, an inline function is one qualified with the Keyword (computer programming), keyword inline; this serves two purposes:
# It serves as a compiler directive that suggests (but doe ...
s can be used as a
type safe alternative in many cases. In both cases, the inlined function body can then undergo further compile-time optimizations by the compiler, including
constant folding, which may move some computations to compile time.
In many
functional programming
In computer science, functional programming is a programming paradigm where programs are constructed by Function application, applying and Function composition (computer science), composing Function (computer science), functions. It is a declarat ...
languages, macros are implemented using parse-time substitution of parse trees/abstract syntax trees, which it is claimed makes them safer to use. Since in many cases interpretation is used, that is one way to ensure that such computations are only performed at parse-time, and sometimes the only way.
Lisp
Lisp (historically LISP, an abbreviation of "list processing") is a family of programming languages with a long history and a distinctive, fully parenthesized Polish notation#Explanation, prefix notation.
Originally specified in the late 1950s, ...
originated this style of macro, and such macros are often called "Lisp-like macros". A similar effect can be achieved by using
template metaprogramming in
C++.
In both cases, work is moved to compile-time. The difference between
C macros on one side, and Lisp-like macros and
C++ template metaprogramming on the other side, is that the latter tools allow performing arbitrary computations at compile-time/parse-time, while expansion of
C macros does not perform any computation, and relies on the optimizer ability to perform it. Additionally,
C macros do not directly support
recursion
Recursion occurs when the definition of a concept or process depends on a simpler or previous version of itself. Recursion is used in a variety of disciplines ranging from linguistics to logic. The most common application of recursion is in m ...
or
iteration
Iteration is the repetition of a process in order to generate a (possibly unbounded) sequence of outcomes. Each repetition of the process is a single iteration, and the outcome of each iteration is then the starting point of the next iteration.
...
, so are not
Turing complete.
As with any optimization, however, it is often difficult to predict where such tools will have the most impact before a project is complete.
Automated and manual optimization
''See also
:Compiler optimizations''
Optimization can be automated by compilers or performed by programmers. Gains are usually limited for local optimization, and larger for global optimizations. Usually, the most powerful optimization is to find a superior
algorithm
In mathematics and computer science, an algorithm () is a finite sequence of Rigour#Mathematics, mathematically rigorous instructions, typically used to solve a class of specific Computational problem, problems or to perform a computation. Algo ...
.
Optimizing a whole system is usually undertaken by programmers because it is too complex for automated optimizers. In this situation, programmers or
system administrators explicitly change code so that the overall system performs better. Although it can produce better efficiency, it is far more expensive than automated optimizations. Since many parameters influence the program performance, the program optimization space is large. Meta-heuristics and machine learning are used to address the complexity of program optimization.
Use a
profiler (or
performance analyzer) to find the sections of the program that are taking the most resources the ''bottleneck''. Programmers sometimes believe they have a clear idea of where the bottleneck is, but intuition is frequently wrong. Optimizing an unimportant piece of code will typically do little to help the overall performance.
When the bottleneck is localized, optimization usually starts with a rethinking of the algorithm used in the program. More often than not, a particular algorithm can be specifically tailored to a particular problem, yielding better performance than a generic algorithm. For example, the task of sorting a huge list of items is usually done with a
quicksort routine, which is one of the most efficient generic algorithms. But if some characteristic of the items is exploitable (for example, they are already arranged in some particular order), a different method can be used, or even a custom-made sort routine.
After the programmer is reasonably sure that the best algorithm is selected, code optimization can start. Loops can be unrolled (for lower loop overhead, although this can often lead to ''lower'' speed if it overloads the
CPU cache
A CPU cache is a hardware cache used by the central processing unit (CPU) of a computer to reduce the average cost (time or energy) to access data from the main memory. A cache is a smaller, faster memory, located closer to a processor core, whi ...
), data types as small as possible can be used, integer arithmetic can be used instead of floating-point, and so on. (See
algorithmic efficiency article for these and other techniques.)
Performance bottlenecks can be due to language limitations rather than algorithms or data structures used in the program. Sometimes, a critical part of the program can be re-written in a different
programming language
A programming language is a system of notation for writing computer programs.
Programming languages are described in terms of their Syntax (programming languages), syntax (form) and semantics (computer science), semantics (meaning), usually def ...
that gives more direct access to the underlying machine. For example, it is common for very
high-level languages like
Python to have modules written in
C for greater speed. Programs already written in C can have modules written in
assembly. Programs written in
D can use the
inline assembler
In computer programming, an inline assembler is a feature of some compilers that allows low-level code written in assembly language to be embedded within a program, among code that otherwise has been compiled from a high-level language, higher-leve ...
.
Rewriting sections "pays off" in these circumstances because of a general "
rule of thumb
In English language, English, the phrase ''rule of thumb'' refers to an approximate method for doing something, based on practical experience rather than theory. This usage of the phrase can be traced back to the 17th century and has been associat ...
" known as the 90/10 law, which states that 90% of the time is spent in 10% of the code, and only 10% of the time in the remaining 90% of the code. So, putting intellectual effort into optimizing just a small part of the program can have a huge effect on the overall speed if the correct part(s) can be located.
Manual optimization sometimes has the side effect of undermining readability. Thus code optimizations should be carefully documented (preferably using in-line comments), and their effect on future development evaluated.
The program that performs an automated optimization is called an optimizer. Most optimizers are embedded in compilers and operate during compilation. Optimizers can often tailor the generated code to specific processors.
Today, automated optimizations are almost exclusively limited to
compiler optimization. However, because compiler optimizations are usually limited to a fixed set of rather general optimizations, there is considerable demand for optimizers which can accept descriptions of problem and language-specific optimizations, allowing an engineer to specify custom optimizations. Tools that accept descriptions of optimizations are called
program transformation
A program transformation is any operation that takes a computer program and generates another program. In many cases the transformed program is required to be semantically equivalent to the original, relative to a particular Formal semantics of p ...
systems and are beginning to be applied to real software systems such as C++.
Some high-level languages (
Eiffel,
Esterel) optimize their programs by using an
intermediate language.
Grid computing
Grid computing is the use of widely distributed computer resources to reach a common goal. A computing grid can be thought of as a distributed system with non-interactive workloads that involve many files. Grid computing is distinguished fro ...
or
distributed computing
Distributed computing is a field of computer science that studies distributed systems, defined as computer systems whose inter-communicating components are located on different networked computers.
The components of a distributed system commu ...
aims to optimize the whole system, by moving tasks from computers with high usage to computers with idle time.
Time taken for optimization
Sometimes, the time taken to undertake optimization therein itself may be an issue.
Optimizing existing code usually does not add new features, and worse, it might add new
bugs in previously working code (as any change might). Because manually optimized code might sometimes have less "readability" than unoptimized code, optimization might impact maintainability of it as well. Optimization comes at a price and it is important to be sure that the investment is worthwhile.
An automatic optimizer (or
optimizing compiler, a program that performs code optimization) may itself have to be optimized, either to further improve the efficiency of its target programs or else speed up its own operation. A compilation performed with optimization "turned on" usually takes longer, although this is usually only a problem when programs are quite large.
In particular, for
just-in-time compilers the performance of the
run time compile component, executing together with its target code, is the key to improving overall execution speed.
References
Further reading
*
Jon Bentley: ''Writing Efficient Programs'', .
*
Donald Knuth: ''
The Art of Computer Programming''
How To Write Fast Numerical Code: A Small Introduction"What Every Programmer Should Know About Memory"by Ulrich Drepper explains the structure of modern memory subsystems and suggests how to utilize them efficiently
"Linux Multicore Performance Analysis and Optimization in a Nutshell" presentation slides by Philip Mucci
by Paul Hsieh
by
Jon Bentley
"Performance Anti-Patterns"by Bart Smaalders
{{DEFAULTSORT:Program Optimization
*
Programming language topics
Articles with example C code
*