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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Bibliographic Details
Published in:International journal of parallel programming 2016-06, Vol.44 (3), p.620-643
Main Authors: Andión, José M., Arenaz, Manuel, Bodin, François, Rodríguez, Gabriel, Touriño, Juan
Format: Article
Language:English
Subjects:
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
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Summary: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.
ISSN:0885-7458
1573-7640
DOI:10.1007/s10766-015-0362-9