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In parallel computing, all-to-all (also known as index operation or total exchange) is a collective operation, where each processor sends an individual message to every other processor. Initially, each processor holds p messages of size m each, and the goal is to exchange the i-th message of processor j with the j-th message of processor i.
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.
In computing, multiple instruction, single data (MISD) is a type of parallel computing architecture where many functional units perform different operations on the same data. Pipeline architectures belong to this type, though a purist might say that the data is different after processing by each stage in the pipeline.
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 ...
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)
In computer science, stream processing (also known as event stream processing, data stream processing, or distributed stream processing) is a programming paradigm which views streams, or sequences of events in time, as the central input and output objects of computation.
Atanasoff–Berry computer, the first computer with parallel processing [1] Instruction-level parallelism (ILP) is the parallel or simultaneous execution of a sequence of instructions in a computer program. More specifically, ILP refers to the average number of instructions run per step of this parallel execution. [2]: 5
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