Locality-Aware Automatic Parallelization for GPGPU with OpenHMPP Directives

The use of GPUs for general purpose computation has increased dramatically in the past years due to the rising demands of computing power and their tremendous computing capacity at low cost. Hence, new programming models have been developed to integrate these accelerators with high-level programming...

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Vydané v:	International journal of parallel programming Ročník 44; číslo 3; s. 620 - 643
Hlavní autori:	Andión, José M., Arenaz, Manuel, Bodin, François, Rodríguez, Gabriel, Touriño, Juan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.06.2016 Springer Nature B.V
Predmet:	Analysis Automation Case studies Computation Computer programming Computer Science Computing costs Demand Heterogeneity Optimization techniques Parallel processing Performance evaluation Processor Architectures Programming languages Software Software Engineering/Programming and Operating Systems Source code Studies Theory of Computation Three dimensional Transformations Heterogeneous systems Automatic parallelization Domain-independent kernel Locality GPGPU OpenHMPP
ISSN:	0885-7458, 1573-7640
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Abstract	The use of GPUs for general purpose computation has increased dramatically in the past years due to the rising demands of computing power and their tremendous computing capacity at low cost. Hence, new programming models have been developed to integrate these accelerators with high-level programming languages, giving place to heterogeneous computing systems. Unfortunately, this heterogeneity is also exposed to the programmer complicating its exploitation. This paper presents a new technique to automatically rewrite sequential programs into a parallel counterpart targeting GPU-based heterogeneous systems. The original source code is analyzed through domain-independent computational kernels, which hide the complexity of the implementation details by presenting a non-statement-based, high-level, hierarchical representation of the application. Next, a locality-aware technique based on standard compiler transformations is applied to the original code through OpenHMPP directives. Two representative case studies from scientific applications have been selected: the three-dimensional discrete convolution and the simple-precision general matrix multiplication. The effectiveness of our technique is corroborated by a performance evaluation on NVIDIA GPUs.
AbstractList	The use of GPUs for general purpose computation has increased dramatically in the past years due to the rising demands of computing power and their tremendous computing capacity at low cost. Hence, new programming models have been developed to integrate these accelerators with high-level programming languages, giving place to heterogeneous computing systems. Unfortunately, this heterogeneity is also exposed to the programmer complicating its exploitation. This paper presents a new technique to automatically rewrite sequential programs into a parallel counterpart targeting GPU-based heterogeneous systems. The original source code is analyzed through domain-independent computational kernels, which hide the complexity of the implementation details by presenting a non-statement-based, high-level, hierarchical representation of the application. Next, a locality-aware technique based on standard compiler transformations is applied to the original code through OpenHMPP directives. Two representative case studies from scientific applications have been selected: the three-dimensional discrete convolution and the simple-precision general matrix multiplication. The effectiveness of our technique is corroborated by a performance evaluation on NVIDIA GPUs. Issue Title: Special Issue on High-Level Parallel Programming and Applications The use of GPUs for general purpose computation has increased dramatically in the past years due to the rising demands of computing power and their tremendous computing capacity at low cost. Hence, new programming models have been developed to integrate these accelerators with high-level programming languages, giving place to heterogeneous computing systems. Unfortunately, this heterogeneity is also exposed to the programmer complicating its exploitation. This paper presents a new technique to automatically rewrite sequential programs into a parallel counterpart targeting GPU-based heterogeneous systems. The original source code is analyzed through domain-independent computational kernels, which hide the complexity of the implementation details by presenting a non-statement-based, high-level, hierarchical representation of the application. Next, a locality-aware technique based on standard compiler transformations is applied to the original code through OpenHMPP directives. Two representative case studies from scientific applications have been selected: the three-dimensional discrete convolution and the simple-precision general matrix multiplication. The effectiveness of our technique is corroborated by a performance evaluation on NVIDIA GPUs.
Author	Touriño, Juan Bodin, François Andión, José M. Arenaz, Manuel Rodríguez, Gabriel
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Cites_doi	10.1145/1391956.1391959 10.1109/TPDS.2010.62 10.1007/s00450-011-0160-6 10.1002/cpe.2917 10.1002/cpe.1173 10.1145/2400682.2400713 10.1109/TPDS.2011.311 10.1109/TPDS.2012.160 10.1109/JPROC.2008.917757 10.1016/j.parco.2013.04.003 10.1145/582034.582089 10.1109/SC.2012.51 10.1145/220346.220352 10.1145/1375581.1375595 10.1145/1735688.1735697 10.1145/2259016.2259038 10.1109/InPar.2012.6339595 10.1007/BFb0038658 10.1007/978-3-642-11970-5_14 10.1109/SC.2010.36 10.1145/1993498.1993516 10.1109/IPDPS.2004.1303015
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Title	Locality-Aware Automatic Parallelization for GPGPU with OpenHMPP Directives
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