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Register Allocation
In compiler optimization, register allocation is the process of assigning local automatic variables and Expression (computer science), expression results to a limited number of processor registers. Register allocation can happen over a basic block (''local register allocation''), over a whole function/Subroutine, procedure (''global register allocation''), or across function boundaries traversed via call-graph (''interprocedural register allocation''). When done per function/procedure the calling convention may require insertion of save/restore around each Call site, call-site. Context Principle {, class="wikitable floatright" , + Different number of general-purpose registers in the most common architectures , - ! Architecture ! scope="col" , 32 bit ! scope="col" , 64 bit , - ! scope="row" , ARM , 15 , 31 , - ! scope="row" , Intel x86 , 8 , 16 , - ! scope="row" , MIPS , 32 , 32 , - ! scope="row" , POWER/PowerPC , 32 , 32 , - ! scope ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Compiler Optimization
An optimizing compiler is a compiler designed to generate code that is optimized in aspects such as minimizing program execution time, memory usage, storage size, and power consumption. Optimization is generally implemented as a sequence of optimizing transformations, a.k.a. compiler optimizations algorithms that transform code to produce semantically equivalent code optimized for some aspect. Optimization is limited by a number of factors. Theoretical analysis indicates that some optimization problems are NP-complete, or even undecidable. Also, producing perfectly ''optimal'' code is not possible since optimizing for one aspect often degrades performance for another. Optimization is a collection of heuristic methods for improving resource usage in typical programs. Categorization Local vs. global scope Scope describes how much of the input code is considered to apply optimizations. Local scope optimizations use information local to a basic block. Since basic blocks cont ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cache (computing)
In computing, a cache ( ) is a hardware or software component that stores data so that future requests for that data can be served faster; the data stored in a cache might be the result of an earlier computation or a copy of data stored elsewhere. A cache hit occurs when the requested data can be found in a cache, while a cache miss occurs when it cannot. Cache hits are served by reading data from the cache, which is faster than recomputing a result or reading from a slower data store; thus, the more requests that can be served from the cache, the faster the system performs. To be cost-effective, caches must be relatively small. Nevertheless, caches are effective in many areas of computing because typical Application software, computer applications access data with a high degree of locality of reference. Such access patterns exhibit temporal locality, where data is requested that has been recently requested, and spatial locality, where data is requested that is stored near dat ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Overhead (computing)
Overhead in computer systems consists of shared functions that benefit all users or processes but are not directly attributable to any specific task. It is thus similar to overhead in organizations. Computer system overhead shows up as slower processing, less memory, less storage capacity, less network bandwidth, or bigger latency than would be expected from reading the system specifications. It is a special case of engineering overhead. Overhead can be a deciding factor in software design, with regard to structure, error correction, and feature inclusion. Examples of computing overhead may be found in object-oriented programming (OOP), functional programming, data transfer, data structures, and file systems on data storage devices. Software design Choice of implementation A programmer/software engineer may have a choice of several algorithms, encodings, data types or data structures, each of which have known characteristics. When choosing among them, their respective overhead ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Time Complexity
In theoretical computer science, the time complexity is the computational complexity that describes the amount of computer time it takes to run an algorithm. Time complexity is commonly estimated by counting the number of elementary operations performed by the algorithm, supposing that each elementary operation takes a fixed amount of time to perform. Thus, the amount of time taken and the number of elementary operations performed by the algorithm are taken to be related by a constant factor. Since an algorithm's running time may vary among different inputs of the same size, one commonly considers the worst-case time complexity, which is the maximum amount of time required for inputs of a given size. Less common, and usually specified explicitly, is the average-case complexity, which is the average of the time taken on inputs of a given size (this makes sense because there are only a finite number of possible inputs of a given size). In both cases, the time complexity is gene ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Chaitin Et Al
Gregory John Chaitin ( ; born 25 June 1947) is an Argentine-American mathematician and computer scientist. Beginning in the late 1960s, Chaitin made contributions to algorithmic information theory and metamathematics, in particular a computer-theoretic result equivalent to Gödel's incompleteness theorem. He is considered to be one of the founders of what is today known as algorithmic (Solomonoff–Kolmogorov–Chaitin, Kolmogorov or program-size) complexity together with Andrei Kolmogorov and Ray Solomonoff. Along with the works of e.g. Solomonoff, Kolmogorov, Martin-Löf, and Leonid Levin, algorithmic information theory became a foundational part of theoretical computer science, information theory, and mathematical logic. It is a common subject in several computer science curricula. Besides computer scientists, Chaitin's work draws attention of many philosophers and mathematicians to fundamental problems in mathematical creativity and digital philosophy. Mathematics and co ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Live Variable Analysis
In compilers, live variable analysis (or simply liveness analysis) is a classic data-flow analysis to calculate the variables that are ''live'' at each point in the program. A variable is ''live'' at some point if it holds a value that may be needed in the future, or equivalently if its value may be read before the next time the variable is written to. Example Consider the following program: b = 3 c = 5 a = f(b * c) The set of live variables between lines 2 and 3 is because both are used in the multiplication on line 3. But the set of live variables after line 1 is only , since variable c is updated later, on line 2. The value of variable a is not used in this code. Note that the assignment to a may be eliminated as a is not used later, but there is insufficient information to justify removing all of line 3 as f may have side effects (printing b * c, perhaps). Expression in terms of dataflow equations Liveness analysis is a "backwards may" analysis. The analysis is done ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Temporary Variable
In computer programming, a temporary variable is a variable with short lifetime, usually to hold data that will soon be discarded, or before it can be placed at a more permanent memory location. Because it is short-lived, it is usually declared as a local variable, i.e., a variable with local scope. There is no formal definition of what makes a variable ''temporary'', but it is an often-used term in programming. A typical example would be that of swapping the contents of two variables. Temporary variables, along with XOR swaps and arithmetic operators, are one of three main ways to exchange the contents of two variables. To swap the contents of variables "a" and "b" one would typically use a temporary variable ''temp'' as follows, so as to preserve the data from ''a'' as it is being overwritten by ''b'': temp := a a := b b := temp Temporary variables are usually named with identifiers that abbreviate the word ''temporary'', such as ''temp'', ''tmp'' or simply ''t'', ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Graph (discrete Mathematics)
In discrete mathematics, particularly in graph theory, a graph is a structure consisting of a Set (mathematics), set of objects where some pairs of the objects are in some sense "related". The objects are represented by abstractions called ''Vertex (graph theory), vertices'' (also called ''nodes'' or ''points'') and each of the related pairs of vertices is called an ''edge'' (also called ''link'' or ''line''). Typically, a graph is depicted in diagrammatic form as a set of dots or circles for the vertices, joined by lines or curves for the edges. The edges may be directed or undirected. For example, if the vertices represent people at a party, and there is an edge between two people if they shake hands, then this graph is undirected because any person ''A'' can shake hands with a person ''B'' only if ''B'' also shakes hands with ''A''. In contrast, if an edge from a person ''A'' to a person ''B'' means that ''A'' owes money to ''B'', then this graph is directed, because owing mon ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Control-flow Graph
In computer science, a control-flow graph (CFG) is a representation, using graph notation, of all paths that might be traversed through a program during its execution. The control-flow graph was conceived by Frances E. Allen, who noted that Reese T. Prosser used boolean connectivity matrices for flow analysis before. The CFG is essential to many compiler optimizations and static-analysis tools. Definition In a control-flow graph each node in the graph represents a basic block, i.e. a straight-line sequence of code with a single entry point and a single exit point, where no branches or jumps occur within the block. Basic blocks start with jump targets and end with jumps or branch instructions. Directed edges are used to represent jumps in the control flow. There are, in most presentations, two specially designated blocks: the ''entry block'', through which control enters into the flow graph, and the ''exit block'', through which all control flow leaves. Because of its c ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Graph Coloring
In graph theory, graph coloring is a methodic assignment of labels traditionally called "colors" to elements of a Graph (discrete mathematics), graph. The assignment is subject to certain constraints, such as that no two adjacent elements have the same color. Graph coloring is a special case of graph labeling. In its simplest form, it is a way of coloring the Vertex (graph theory), vertices of a graph such that no two adjacent vertices are of the same color; this is called a vertex coloring. Similarly, an ''edge coloring'' assigns a color to each Edge (graph theory), edges so that no two adjacent edges are of the same color, and a face coloring of a planar graph assigns a color to each Face (graph theory), face (or region) so that no two faces that share a boundary have the same color. Vertex coloring is often used to introduce graph coloring problems, since other coloring problems can be transformed into a vertex coloring instance. For example, an edge coloring of a graph is just ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
NP-completeness
In computational complexity theory, NP-complete problems are the hardest of the problems to which ''solutions'' can be verified ''quickly''. Somewhat more precisely, a problem is NP-complete when: # It is a decision problem, meaning that for any input to the problem, the output is either "yes" or "no". # When the answer is "yes", this can be demonstrated through the existence of a short (polynomial length) ''solution''. # The correctness of each solution can be verified quickly (namely, in polynomial time) and a brute-force search algorithm can find a solution by trying all possible solutions. # The problem can be used to simulate every other problem for which we can verify quickly that a solution is correct. Hence, if we could find solutions of some NP-complete problem quickly, we could quickly find the solutions of every other problem to which a given solution can be easily verified. The name "NP-complete" is short for "nondeterministic polynomial-time complete". In this name, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
PowerPC
PowerPC (with the backronym Performance Optimization With Enhanced RISC – Performance Computing, sometimes abbreviated as PPC) is a reduced instruction set computer (RISC) instruction set architecture (ISA) created by the 1991 Apple Inc., Apple–IBM–Motorola alliance, known as AIM alliance, AIM. PowerPC, as an evolving instruction set, has been named Power ISA since 2006, while the old name lives on as a trademark for some implementations of Power Architecture–based processors. Originally intended for personal computers, the architecture is well known for being used by Apple's desktop and laptop lines from 1994 until 2006, and in several videogame consoles including Microsoft's Xbox 360, Sony's PlayStation 3, and Nintendo's GameCube, Wii, and Wii U. PowerPC was also used for the Curiosity (rover), Curiosity and Perseverance (rover), Perseverance rovers on Mars and a variety of satellites. It has since become a niche architecture for personal computers, particularly with A ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
