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In computing, a parallel programming model is an abstraction of parallel computer architecture, with which it is convenient to express algorithms and their composition in programs. The value of a programming model can be judged on its generality : how well a range of different problems can be expressed for a variety of different architectures ...
Explicitly parallel instruction computing (EPIC) is a term coined in 1997 by the HP–Intel alliance [1] to describe a computing paradigm that researchers had been investigating since the early 1980s. [2] This paradigm is also called Independence architectures.
Due to the inherent difficulties in full automatic parallelization, several easier approaches exist to get a parallel program in higher quality. One of these is to allow programmers to add "hints" to their programs to guide compiler parallelization, such as HPF for distributed memory systems and OpenMP or OpenHMPP for shared memory systems.
Parallel computing is a type of computation in which many calculations or processes are carried out simultaneously. [1] Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism.
An initial version of this model was introduced, under the MapReduce name, in a 2010 paper by Howard Karloff, Siddharth Suri, and Sergei Vassilvitskii. [2] As they and others showed, it is possible to simulate algorithms for other models of parallel computation, including the bulk synchronous parallel model and the parallel RAM, in the massively parallel communication model.
The parallel random-access machine [10] The actor model; Computational bridging models such as the bulk synchronous parallel (BSP) model; Petri nets; Process calculi. Calculus of communicating systems (CCS) Communicating sequential processes (CSP) model; π-calculus; Tuple spaces, e.g., Linda; Simple Concurrent Object-Oriented Programming (SCOOP)
by Michel Auguin (University of Nice Sophia-Antipolis) and François Larbey (Thomson/Sintra), [1] [2] [3] as a “fork-and-join” and data-parallel approach where the parallel tasks (“single program”) are split-up and run simultaneously in lockstep on multiple SIMD processors with different inputs, and
Multiprogramming is a computing technique that enables multiple programs to be concurrently loaded and executed into a computer's memory, allowing the CPU to switch between them swiftly. This optimizes CPU utilization by keeping it engaged with the execution of tasks, particularly useful when one program is waiting for I/O operations to complete.