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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.
Single instruction, multiple threads (SIMT) is an execution model used in parallel computing where single instruction, multiple data (SIMD) is combined with multithreading. It is different from SPMD in that all instructions in all "threads" are executed in lock-step.
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]
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)
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 opportunity for loop-level parallelism often arises in computing programs where data is stored in random access data structures. Where a sequential program will iterate over the data structure and operate on indices one at a time, a program exploiting loop-level parallelism will use multiple threads or processes which operate on some or all ...
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 inclusion of the suppressed information is guided by the proof of a scheduling theorem due to Brent, [2] which is explained later in this article. The WT framework is useful since while it can greatly simplify the initial description of a parallel algorithm, inserting the details suppressed by that initial description is often not very ...