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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.
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.
Sequential vs. data-parallel job execution. Data parallelism is parallelization across multiple processors in parallel computing environments. It focuses on distributing the data across different nodes, which operate on the data in parallel. It can be applied on regular data structures like arrays and matrices by working on each element in ...
"Embarrassingly" is used here to refer to parallelization problems which are "embarrassingly easy". [4] The term may imply embarrassment on the part of developers or compilers: "Because so many important problems remain unsolved mainly due to their intrinsic computational complexity, it would be embarrassing not to develop parallel implementations of polynomial homotopy continuation methods."
Bit-level parallelism is a form of parallel computing based on increasing processor word size.Increasing the word size reduces the number of instructions the processor must execute in order to perform an operation on variables whose sizes are greater than the length of the word.
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.
In parallel computing this approach is considered suitable for data-intensive computing and problems which are “embarrassingly parallel”, i.e. where it is relatively easy to separate the problem into a number of parallel tasks and there is no dependency or communication required between the tasks other than overall management of the tasks.