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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.
Exploitation of the concept of data parallelism started in 1960s with the development of the Solomon machine. [1] The Solomon machine, also called a vector processor, was developed to expedite the performance of mathematical operations by working on a large data array (operating on multiple data in consecutive time steps).
The time taken by this algorithm is (+) (). [2] Here t s {\displaystyle t_{s}} is the startup cost for a communication, and t w {\displaystyle t_{w}} is the cost of transmitting a unit of data. This term can further be improved when half of the messages are sent in one and the other half in the other direction.
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]
Task 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 parallel computing, granularity (or grain size) of a task is a measure of the amount of work (or computation) which is performed by that task. [1] Another definition of granularity takes into account the communication overhead between multiple processors or processing elements. It defines granularity as the ratio of computation time to ...
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 ...
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.