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A Survey on Parallel Computing and its Applications in Data-Parallel Problems Using GPU Architectures

Published online by Cambridge University Press:  03 June 2015

Cristóbal A. Navarro ,
Nancy Hitschfeld-Kahler  and
Luis Mateu
Cristóbal A. Navarro*
Affiliation:
Department of Computer Science (DCC), Universidad de Chile, Santiago, Chile Centro de Estudios Científicos (CECS), Valdivia, Chile
Nancy Hitschfeld-Kahler*
Affiliation:
Department of Computer Science (DCC), Universidad de Chile, Santiago, Chile
Luis Mateu*
Affiliation:
Department of Computer Science (DCC), Universidad de Chile, Santiago, Chile
*
Corresponding author.Email:crinavar@dcc.uchile.cl
Email:nancy@dcc.uchile.cl
Email:lmateu@dcc.uchile.cl

Abstract

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Parallel computing has become an important subject in the field of computer science and has proven to be critical when researching high performance solutions. The evolution of computer architectures (multi-core and many-core) towards a higher number of cores can only confirm that parallelism is the method of choice for speeding up an algorithm. In the last decade, the graphics processing unit, or GPU, has gained an important place in the field of high performance computing (HPC) because of its low cost and massive parallel processing power. Super-computing has become, for the first time, available to anyone at the price of a desktop computer. In this paper, we survey the concept of parallel computing and especially GPU computing. Achieving efficient parallel algorithms for the GPU is not a trivial task, there are several technical restrictions that must be satisfied in order to achieve the expected performance. Some of these limitations are consequences of the underlying architecture of the GPU and the theoretical models behind it. Our goal is to present a set of theoretical and technical concepts that are often required to understand the GPU and its massive parallelism model. In particular, we show how this new technology can help the field of computational physics, especially when the problem is data-parallel. We present four examples of computational physics problems; n-body, collision detection, Potts model and cellular automata simulations. These examples well represent the kind of problems that are suitable for GPU computing. By understanding the GPU architecture and its massive parallelism programming model, one can overcome many of the technical limitations found along the way, design better GPU-based algorithms for computational physics problems and achieve speedups that can reach up to two orders of magnitude when compared to sequential implementations.

Keywords

68W1065Y0568Q1068N1968M1400A7981VxxGPU computingparallel computingcomputing modelsalgorithmsdata parallelmassive parallelismPotts modelIsing Modelcollision detectionn-bodyCellular Automata.
Type
Review Article
Information
Communications in Computational Physics , Volume 15 , Issue 2 , February 2014 , pp. 285 - 329
Copyright
Copyright © Global Science Press Limited 2014

References

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