Loop optimizationIn compiler theory, loop optimization is the process of increasing execution speed and reducing the overheads associated with loops. It plays an important role in improving cache performance and making effective use of parallel processing capabilities. Most execution time of a scientific program is spent on loops; as such, many compiler optimization techniques have been developed to make them faster. Since instructions inside loops can be executed repeatedly, it is frequently not possible to give a bound on the number of instruction executions that will be impacted by a loop optimization.
Grande écoleA grande école (ɡʁɑ̃d ekɔl; great school) is a specialized top level educational institution in France. Grandes écoles are part of an alternative educational system that operates alongside the mainstream French public university system and are dedicated to teaching, research and professional training in single academic fields such as engineering, architecture, business administration, academic research, or public policy and administration.
Compiler-compilerIn computer science, a compiler-compiler or compiler generator is a programming tool that creates a parser, interpreter, or compiler from some form of formal description of a programming language and machine. The most common type of compiler-compiler is more precisely called a parser generator. It only handles syntactic analysis. The input of a parser generator is a grammar file, typically written in Backus–Naur form (BNF) or extended Backus–Naur form (EBNF) that defines the syntax of a target programming language.
64-bit computingIn computer architecture, 64-bit integers, memory addresses, or other data units are those that are 64 bits wide. Also, 64-bit CPUs and ALUs are those that are based on processor registers, address buses, or data buses of that size. A computer that uses such a processor is a 64-bit computer. From the software perspective, 64-bit computing means the use of machine code with 64-bit virtual memory addresses.
Pipeline (computing)In computing, a pipeline, also known as a data pipeline, is a set of data processing elements connected in series, where the output of one element is the input of the next one. The elements of a pipeline are often executed in parallel or in time-sliced fashion. Some amount of buffer storage is often inserted between elements. Computer-related pipelines include: Instruction pipelines, such as the classic RISC pipeline, which are used in central processing units (CPUs) and other microprocessors to allow overlapping execution of multiple instructions with the same circuitry.
Cross compilerA cross compiler is a compiler capable of creating executable code for a platform other than the one on which the compiler is running. For example, a compiler that runs on a PC but generates code that runs on an Android smartphone is a cross compiler. A cross compiler is useful to compile code for multiple platforms from one development host. Direct compilation on the target platform might be infeasible, for example on embedded systems with limited computing resources. Cross compilers are distinct from source-to-source compilers.
IA-32IA-32 (short for "Intel Architecture, 32-bit", commonly called i386) is the 32-bit version of the x86 instruction set architecture, designed by Intel and first implemented in the 80386 microprocessor in 1985. IA-32 is the first incarnation of x86 that supports 32-bit computing; as a result, the "IA-32" term may be used as a metonym to refer to all x86 versions that support 32-bit computing. Within various programming language directives, IA-32 is still sometimes referred to as the "i386" architecture.
TransmetaTransmeta Corporation was an American fabless semiconductor company based in Santa Clara, California. It developed low power x86 compatible microprocessors based on a VLIW core and a software layer called Code Morphing Software. Code Morphing Software (CMS) consisted of an interpreter, a runtime system and a dynamic binary translator. x86 instructions were first interpreted one instruction at a time and profiled, then depending upon the frequency of execution of a code block, CMS would progressively generate more optimized translations.
Data parallelismData parallelism is parallelization across multiple processors in parallel computing environments. It focuses on distributing the data across different nodes, which operate on the data in parallel. It can be applied on regular data structures like arrays and matrices by working on each element in parallel. It contrasts to task parallelism as another form of parallelism. A data parallel job on an array of n elements can be divided equally among all the processors.
Classic RISC pipelineIn the history of computer hardware, some early reduced instruction set computer central processing units (RISC CPUs) used a very similar architectural solution, now called a classic RISC pipeline. Those CPUs were: MIPS, SPARC, Motorola 88000, and later the notional CPU DLX invented for education. Each of these classic scalar RISC designs fetches and tries to execute one instruction per cycle. The main common concept of each design is a five-stage execution instruction pipeline.
Loop nest optimizationIn computer science and particularly in compiler design, loop nest optimization (LNO) is an optimization technique that applies a set of loop transformations for the purpose of locality optimization or parallelization or another loop overhead reduction of the loop nests. (Nested loops occur when one loop is inside of another loop.) One classical usage is to reduce memory access latency or the cache bandwidth necessary due to cache reuse for some common linear algebra algorithms.
Task parallelismTask parallelism (also known as function parallelism and control parallelism) is a form of parallelization of computer code across multiple processors in parallel computing environments. Task parallelism focuses on distributing tasks—concurrently performed by processes or threads—across different processors. In contrast to data parallelism which involves running the same task on different components of data, task parallelism is distinguished by running many different tasks at the same time on the same data.
