Program optimization: Difference between revisions

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==Overview==

Although the term "optimization" is derived from "optimum",<ref>{{Cite book |lastlast1=Antoniou |firstfirst1=Andreas |url=https://link.springer.com/content/pdf/10.1007/978-1-0716-0843-2.pdf |title=Practical Optimization |last2=Lu |first2=Wu-Sheng |series=Texts in Computer Science |publisher=[[Springer Publishing|Springer]] |year=2021 |edition=2nd |pages=1 |doi=10.1007/978-1-0716-0843-2 |isbn=978-1-0716-0841-8 |language=en}}</ref> 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 popularfrequently cited example is the [[space-time tradeoff]], where reducing a program’s execution time bycan increasingincrease its memory consumption. Conversely, in scenarios where memory is limited, engineers might prioritize a slower [[algorithm]] to conserve space. There is rarely a single design that can excel in all situations, requiring [[software engineers|programmers]] to prioritize attributes most relevant to the application at hand. Metrics for software include throughput, [[Frames per second|latency]], [[RAM|volatile memory usage]], [[Disk storage|persistent storage]], [[internet usage]], [[energy consumption]], and hardware [[wear and tear]]. The most common metric is speed.

Furthermore, achieving absolute optimization often demands disproportionate effort relative to the benefits gained. Consequently, optimization processes usually stopslow 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{{snd}} though this varies significantly{{snd}} 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. Best practices for optimization during iterative development cycles include continuous monitoring for performance issues coupled with regular performance testing.<ref>{{cite web |title= Performance Optimization in Software Development: Speeding Up Your Applications|url=https://senlainc.com/blog/performance-optimization-in-software-development/#best-practices-for-performance-optimization |access-date=12 July 2025}}</ref><ref>{{cite web |author=Agrawal, Amit |title= Maximizing Efficiency: Implementing a Performance Monitoring System |url=https://www.developers.dev/tech-talk/implement-a-system-for-monitoring-application.html |access-date=12 July 2025}}</ref>

As performance is part of the specification of a program{{snd}} a program that is unusably slow is not fit for purpose: a video game with 60&nbsp;Hz (frames-per-second) is acceptable, but 6 frames-per-second is unacceptably choppy{{snd}} 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{{snd}} often by an [[order of magnitude]] or more{{snd}} 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 comparable with native code only with [[HotSpot (virtual machine)|HotSpot]] (1999).<ref>{{cite web |author=Düppe, Ingo |title= Hitchhiker’s Guide to Java Performance: The Past, the Present, and the Future |url=https://javapro.io/2025/04/07/hitchhikers-guide-to-java-performance |access-date=12 July 2025}}</ref> 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]], 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{{snd}} 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]], 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 [[algorithmic efficiency|efficient algorithms]] and [[data structure]]s, and efficient implementation of these algorithms and data structures comes next. After design, the choice of [[algorithm]]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 type]]s in function definitions, and keeping the concrete data structure definitions restricted to a few places. Changes in data structures mapped to a database may require schema migration and other complex software or infrastructure changes.<ref>{{cite web |author=Mullins, Craig S. |title=The Impact of Change on Database Structures |url=https://www.dbta.com/Columns/DBA-Corner/The-Impact-of-Change-on-Database-Structures-101931.aspx |access-date=12 July 2025}}</ref>

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.

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===Compile level===

Use of an [[optimizing compiler]] with optimizations enabled tends to ensure that the [[executable program]] is optimized at least as much as the compiler can predictreasonable perform. See [[Optimizing compiler]] for more details.

===Assembly level===

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In computer science, resource consumption often follows a form of [[power law]] 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.<ref>{{cite book | last = Wescott | first = Bob | title = The Every Computer Performance Book, Chapter 3: Useful laws | publisher = [[CreateSpace]] | date = 2013 | isbn = 978-1482657753}}</ref> 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.<ref>{{cite web |url=http://www.developforperformance.com/PerformanceProfilingWithAFocus.html#FittingTheSituation |author=Krauss, Kirk J. |title=Performance Profiling with a Focus |access-date=15 August 2017}}</ref>

Performance profiling therefore provides not only bottleneck detection but rather a variety of methods for optimization guidance. [[Empirical algorithmics]] is the practice of using empirical methods, typically performance profiling, to study the behavior of algorithms, for developer understanding that may lead to human-planned optimizations. [[Profile-guided optimization]] is the machine-driven use of profiling data as input to an optimizing compiler or interpreter. Some programming languages are associated with tools for profile-guided optimization.<ref>{{cite web |url=https://doc.rust-lang.org/beta/rustc/profile-guided-optimization.html |title=Profile-guided Optimization |access-date=12 July 2025}}</ref> Some performance profiling methods emphasize enhancements based on [[cache (computing)|cache]] utilization.<ref>{{Cite book |last=The Valgrind Developers |url=https://www.cs.cmu.edu/afs/cs.cmu.edu/project/cmt-40/Nice/RuleRefinement/bin/valgrind-3.2.0/docs/html/cl-manual.html#cl-manual.tools |title=Valgrind User Manual |section=5.2.2 |publisher=Network Theory Ltd. |year=2006 |language=en}}</ref> Other benefits of performance profiling may include improved resource management and an enhanced user experience.<ref>{{cite web |author= Kodlekere, Ranjana |title= Performance Profiling: Explained with Stages| url=https://testsigma.com/blog/performance-profiling/#benefits-of-performance-profiling |access-date=12 July 2025}}</ref>

In some cases, adding more [[main memory|memory]] 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.

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Typically, optimization involves choosing the best overall algorithms and data structures. <ref>{{cite web|url=https://ubiquity.acm.org/article.cfm?id=1513451|title=The Fallacy of Premature Optimization}}</ref> Frequently, algorithmic improvements can cause performance improvements of several orders of magnitude instead of micro-optimizations, which rarely improve performance by more than a few percent. <ref>{{cite web|url=https://ubiquity.acm.org/article.cfm?id=1513451|title=The Fallacy of Premature Optimization}}</ref> If one waits to optimize until the end of the development cycle, then changing the algorithm requires a complete rewrite.

Optimization can reduce [[readability]] and add code that is used only to improve the [[Computer performance|performance]]. 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]].

Frequently, micro-optimization can reduce [[readability]] and complicate programs or systems. That can make programs more difficult to maintain and debug.

[[Donald Knuth]] made the following two statements on optimization:

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<blockquote> "In established engineering disciplines a 12% improvement, easily obtained, is never considered marginal and I believe the same viewpoint should prevail in software engineering"<ref name="autogenerated268"/></blockquote>

"Premature optimization" is often used as a rallying cry against all optimization in all situations for all purposes. <ref>{{cite web|url=https://ubiquity.acm.org/article.cfm?id=1513451|title=The Fallacy of Premature Optimization}}</ref><ref>{{cite web|url=https://www.javacodegeeks.com/2012/11/not-all-optimization-is-premature.html|title=Not All Optimization is Premature}}</ref><ref>{{cite web|url=https://www.infoworld.com/article/2165382/when-premature-optimization-isn-t.html|title=When Premature Optimization Is'nt}}</ref><ref>{{cite web|url=https://prog21.dadgum.com/106.html|title="Avoid Premature Optimization" Does Not Mean "Write Dump Code"}}</ref> Frequently, [[SOLID|Clean Code]] causes code to be more complicated than simpler more efficient code. <ref>{{cite web|url=https://devshift.substack.com/p/premature-abstractions|title=Premature Abstractions}}</ref>

"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 whetherwhat to optimize a specific part of the program, [[Amdahl's Law]] should always be considered:used theto impactproritize on the overall program depends veryparts muchbased on howthe muchactual time is actually spent in thata specificcertain part, which is not always clear from looking at the code without a [[Profiling (computer programming)|performance analysis]].

In practice, it is often necessary to keep performance goals in mind when first designing software, butyet theprogrammers programmermust balancesbalance thevarious goalstradeoffs. ofDevelopment designcost is significant, and optimizationhardware is fast.

A better approach is therefore to design first, code from the design and then [[profiling (computer programming)|profile]]/[[Benchmark (computing)|benchmark]] the resulting code to see which parts should be optimized. A simple and elegant design <!-- how is this produced, if not prematurely? -->is often easier to optimize at this stage, and profiling may reveal unexpected performance problems that would not have been addressed by premature optimization.

Modern compilers are efficient enough that the intended performance increases sometimes fail to materialize. Since compilers perform many automatic optimizations, some optimizations may yield an identical executable. Also, sometimes hardware may reduce the impact of micro-optimization. For example, hardware may cache data that is cached at a software level.

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==

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In particular, for [[just-in-time compiler]]s the performance of the [[Run time environment|run time]] compile component, executing together with its target code, is the key to improving overall execution speed.

==False optimization==

Sometimes, "optimizations" may hurt performance. Parallelism and concurrency causes a significant overhead performance cost, especially energy usage. Keep in mind that C code rarely uses explicit multiprocessing, yet it typically runs faster than any other programming language. Disk caching, paging, and swapping often cause significant increases to energy usage and hardware wear and tear. Running processes in the background to improve startup time slows down all other processes.

==See also==

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{{div col|small=yes|colwidth=20em}}

* {{annotated link|Benchmark (computing)|Benchmark}}

* {{annotated link|Cache (computing)}}

* {{annotated link|Empirical algorithmics}}

* {{annotated link|Optimizing compiler}}

* {{annotated link|Performance engineering}}

* {{annotated link|Performance prediction}}

* {{annotated link|Performance tuning}}

* {{annotated link|Profile-guided optimization}}

* {{annotated link|Software development}}

* {{annotated link|Software performance testing}}

* {{annotated link|Static code analysis}}

{{div col end}}

<!-- please keep entries in alphabetical order -->

==References==