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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 computer science, Linda is a coordination model that aids communication in parallel computing environments. Developed by David Gelernter, it is meant to be used alongside a full-fledged computation language like Fortran or C where Linda's role is to "create computational activities and to support communication among them". [3] [4] [5]
CUDA and OpenACC: CUDA and OpenACC (respectively) are parallel computing API platforms designed to allow a software engineer to utilize GPU's computational units for general purpose processing. Threading Building Blocks and RaftLib : Both open source programming environments that enable mixed data/task parallelism in C/C++ environments across ...
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.
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 ...
[1] At the sub-routine (or procedure) level the grain size is typically a few thousand instructions. Medium-grained parallelism is achieved at sub-routine level. [1] At program-level, parallel execution of programs takes place. Granularity can be in the range of tens of thousands of instructions. [1] Coarse-grained parallelism is used at this ...
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.
Broadcast is a collective communication primitive in parallel programming to distribute programming instructions or data to nodes in a cluster. It is the reverse operation of reduction. [1] The broadcast operation is widely used in parallel algorithms, such as matrix-vector multiplication, [1] Gaussian elimination and shortest paths. [2]