NDLI: MapCG: writing parallel program portable between CPU and GPU

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MapCG: writing parallel program portable between CPU and GPU

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MapCG: writing parallel program portable between CPU and GPU

Content Provider	ACM Digital Library
Author	Hong, Chuntao Zheng, Weimin Lin, Haibo Chen, Dehao Chen, Wenguang
Abstract	Graphics Processing Units (GPU) have been playing an important role in the general purpose computing market recently. The common approach to program GPU today is to write GPU specific code with low level GPU APIs such as CUDA. Although this approach can achieve very good performance, it raises serious portability issues: programmers are required to write a specific version of code for each potential target architecture. It results in high development and maintenance cost. We believe it is desired to have a programming model which provides source code portability between CPUs and GPUs, and different GPUs: Programmers only need to write one version of code and can be compiled and executed on either CPUs or GPUs efficiently without modification. In this paper, we propose MapCG, a MapReduce framework to provide source code level portability between CPU and GPU. Different from OpenCL, our framework is based on MapReduce, which provides a high level programming model, making programming much easier. We describe the design of the MapReduce-based high-level programming language and the underlying runtime system to enable portability between CPU and GPU. A prototype of MapCG runtime was implemented, supporting multi-core CPU and NVIDIA GPUs. Experiments show that our implementation can execute the same source code efficiently on multi-core CPU platforms and GPUs, achieving an average of 1.6-2.5x speedup over previous implementations of MapReduce on eight commonly used applications.
Starting Page	217
Ending Page	226
Page Count	10
File Format	PDF
ISBN	9781450301787
DOI	10.1145/1854273.1854303
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-09-11
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Gpu programming Portability Parallel
Content Type	Text
Resource Type	Article

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