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Title:Using the High Productivity Language Chapel to Target GPGPU Architectures
Author(s):Sidelnik, Albert; Chamberlain, Bradford L.; Garzaran, Maria J.; Padua, David
Subject(s):Chapel, HPC, GPGPU, Cuda, PGAS, Languages, Compilers
Abstract:It has been widely shown that GPGPU architectures offer large performance gains compared to their traditional CPU counterparts for many applications. The downside to these architectures is that the current programming models present numerous challenges to the programmer: lower-level languages, explicit data movement, loss of portability, and challenges in performance optimization. In this paper, we present novel methods and compiler transformations that increase productivity by enabling users to easily program GPGPU architectures using the high productivity programming language Chapel. Rather than resorting to different parallel libraries or annotations for a given parallel platform, we leverage a language that has been designed from first principles to address the challenge of programming for parallelism and locality. This also has the advantage of being portable across distinct classes of parallel architectures, including desktop multicores, distributed memory clusters, large-scale shared memory, and now CPU-GPU hybrids. We present experimental results from the Parboil benchmark suite which demonstrate that codes written in Chapel achieve performance comparable to the original versions implemented in CUDA.
Issue Date:2011-04-25
Genre:Technical Report
Publication Status:unpublished
Peer Reviewed:not peer reviewed
Sponsor:NSF CCF 0702260
Cray Inc. Cray-SRA-2010-01696
2010-2011 Nvidia Research Fellowship
Date Available in IDEALS:2011-04-25

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