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

作　　者：Cristobal A.Navarro Nancy Hitschfeld-Kahler Luis Mateu 

机构地区：[1]Department of Computer Science(DCC),Universidad de Chile,Santiago,Chile [2]Centro de Estudios Cient´ıficos(CECS),Valdivia,Chile.

出　　处：《Communications in Computational Physics》2014年第2期285-329,共45页计算物理通讯（英文）

基　　金：supported by Fondecyt Project No.1120495.Finally,thanks to Renato Cerro for improving the English of this manuscript.

摘　　要：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.

关 键 词：GPU computing parallel computing computing models algorithms data parallel massive parallelism Potts model Ising Model collision detection N-BODY Cellular Automata. 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]