C Code Optimization Techniques Pdf' title='C Code Optimization Techniques Pdf' />Program optimization Wikipedia. In computer science, program 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 is capable of operating with less memory storage or other resources, or draw less power. GeneraleditAlthough the word optimization shares the same root as optimal, it is rare for the process of optimization to produce a truly optimal system. The optimized system will typically only be optimal in one application or for one audience. One might reduce the amount of time that a program takes to perform some task at the price of making it consume more memory. In an application where memory space is at a premium, one might deliberately choose a slower algorithm in order to use less memory. Often there is no one size fits all design which works well in all cases, so engineers make trade offs to optimize the attributes of greatest interest. Additionally, the effort required to make a piece of software completely optimal  incapable of any further improvement  is almost always more than is reasonable for the benefits that would be accrued so the process of optimization may be halted before a completely optimal solution has been reached. Fortunately, it is often the case that the greatest improvements come early in the process. Levels of optimizationeditOptimization 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. C Code Optimization Techniques Pdf' title='C Code Optimization Techniques Pdf' />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. Federal government web portal for veteran owned businesses. Supports the implementation of the Veterans Entrepreneurship and Small Business Development Act of 1999. Organization. See OpenCL Performance and Optimization is a discussion of general performance and optimization considerations when programming for AMD Accelerated. 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 6. 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 factor of 1. Intel 4. 32 1. 98. Install Microsoft Certificate Enrollment Control. Java 1. 99. 5, which only achieved acceptable performance with Hot. C Code Optimization Techniques Pdf' title='C Code Optimization Techniques Pdf' />Spot 1. 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. At the highest level, the design may be optimized to make best use of the available resources, given goals, constraints, and expected useload. 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 for example, a Python program may rewrite performance critical sections in C. In a distributed system, choice of architecture client server, peer to peer, etc. 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. C Code Optimization Techniques Pdf' title='C Code Optimization Techniques Pdf' />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. For algorithms, this primarily consists of ensuring that algorithms are constant O1, logarithmic Olog n, linear On, or in some cases log linear On log n in the input both in space and time. Algorithms with quadratic complexity On. 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. Bmw E46 M3 Manual Transmission Fluid. 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. Landscape Design Computer Programs. 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. 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, while1 was slower than for for an unconditional loop, because while1 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. Free ANSI C compilers and developers tools. Last updated on August 1. Freebyte. com. Almere, the Netherlands. 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