Significance of Parallel Computation over Serial Computation Using OpenMP, MPI, and CUDA

Shubhangi Rastogi () and Hira Zaheer ()
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Shubhangi Rastogi: Ajay Kumar Garg Engineering College
Hira Zaheer: Indian Institute of Technology

A chapter in Quality, IT and Business Operations, 2018, pp 359-367 from Springer

Abstract: Abstract The need of fast computers to perform multiple works simultaneously in less time is increasing day by day. In serial computation, tasks are performed one by one which takes more time. In parallel computing various processors work simultaneously to solve a problem. Parallel computing (Segel HJ, Jamieson LH, Guest editors’ introduction parallel processing. Comput IEEE Transact C-33(11):949–951, 1984; Adams NM, Kirby SPJ, Harris P, Clegg DB, A review of parallel processing for statistical computation. Stat Comput Springer 6(1), 1996) is the concurrent use of various processors to solve a single problem. A sequential problem can easily be converted into a parallel if it contains some independent sets of instructions which can be executed on different processors at the same time. For example, if a problem consists of n number of steps independent of each other and there are n processors too. So, it will take O (1) of time while serial means only processor will take O (n) time (assume). There are some factors involved in parallel computation like load balancing, synchronization, communication overhead, etc. which can affect the overall time. So choosing number of processors is a prominent issue. In this paper, three programming models for parallel computation are introduced, namely, OpenMP, MPI, and CUDA. Also it is described in the paper that how parallel programming is different from serial programming and the necessity of parallel computation.

Keywords: Serial and parallel computation; OpenMP; MPI; CUDA (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/978-981-10-5577-5_29

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