Program optimization
In computer 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 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 frequently cited example is the space–time tradeoff, where reducing a program’s execution time can increase 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 programmers to prioritize attributes most relevant to the application at hand. Metrics for software include throughput, latency, volatile memory usage, 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 slow once sufficient improvements are achieved. 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. Best practices for optimization during iterative development cycles include continuous monitoring for performance issues coupled with regular performance testing.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 might be 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 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 ; or ones that take years of work to achieve acceptable performance, such as Java, which achieved performance comparable with native code only with HotSpot. 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 would be optimized to minimize network trips, ideally making a single request 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, 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 occurs at the design level, and may be difficult to change, particularly if all components cannot be replaced in sync.Algorithms and data structures
Given an overall design, a good choice of efficient algorithms and data structures, and efficient implementation of these algorithms and data structures comes next. After design, the choice of algorithms 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. Changes in data structures mapped to a database may require schema migration and other complex software or infrastructure changes.For algorithms, this primarily consists of ensuring that algorithms are constant O, logarithmic O, linear O, or in some cases log-linear O in the input. Algorithms with quadratic complexity O 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 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. 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.
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 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 and Gentoo's Portage can take advantage of this form of optimization.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 reasonable perform. See Optimizing compiler for more details.Assembly level
At the lowest level, writing code using an assembly language, 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 systems used on embedded systems have been traditionally written in assembler code for this reason. 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
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 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 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 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, speculative execution, instruction pipelines, and branch predictors. Compilers can help the program take advantage of these CPU features, for example through instruction scheduling.