Mars: A MapReduce Framework on graphics processors

We design and implement Mars, a MapReduce framework, on graphics processors (GPUs). MapReduce is a distributed programming framework originally proposed by Google for the ease of development of web search applications on a large number of commodity CPUs. Compared with CPUs, GPUs have an order of mag...

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Vydáno v:PACT'08 : proceedings of the Seventeenth International Conference on Parallel Architectures and Compilation Techniques : Toronto, Ontario, Canada, October 25-29, 2008 s. 260 - 269
Hlavní autoři: He, Bingsheng, Fang, Wenbin, Luo, Qiong, Govindaraju, Naga K., Wang, Tuyong
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: ACM 01.10.2008
Témata:
Computer architecture
Data parallelism
GPGPU
Graphics processing units
Graphics Processor
Instruction sets
MapReduce
Multi-core processors
Parallel processing
Programming
Runtime
Web Analysis
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Abstract We design and implement Mars, a MapReduce framework, on graphics processors (GPUs). MapReduce is a distributed programming framework originally proposed by Google for the ease of development of web search applications on a large number of commodity CPUs. Compared with CPUs, GPUs have an order of magnitude higher computation power and memory bandwidth, but are harder to program since their architectures are designed as a special-purpose co-processor and their programming interfaces are typically for graphics applications. As the first attempt to harness GPU's power for MapReduce, we developed Mars on an NVIDIA G80 GPU, which contains over one hundred processors, and evaluated it in comparison with Phoenix, the state-of-the-art MapReduce framework on multi-core CPUs. Mars hides the programming complexity of the GPU behind the simple and familiar MapReduce interface. It is up to 16 times faster than its CPU-based counterpart for six common web applications on a quad-core machine.
AbstractList We design and implement Mars, a MapReduce framework, on graphics processors (GPUs). MapReduce is a distributed programming framework originally proposed by Google for the ease of development of web search applications on a large number of commodity CPUs. Compared with CPUs, GPUs have an order of magnitude higher computation power and memory bandwidth, but are harder to program since their architectures are designed as a special-purpose co-processor and their programming interfaces are typically for graphics applications. As the first attempt to harness GPU's power for MapReduce, we developed Mars on an NVIDIA G80 GPU, which contains over one hundred processors, and evaluated it in comparison with Phoenix, the state-of-the-art MapReduce framework on multi-core CPUs. Mars hides the programming complexity of the GPU behind the simple and familiar MapReduce interface. It is up to 16 times faster than its CPU-based counterpart for six common web applications on a quad-core machine.
Author Govindaraju, Naga K.
He, Bingsheng
Fang, Wenbin
Luo, Qiong
Wang, Tuyong
Author_xml – sequence: 1
  givenname: Bingsheng
  surname: He
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  organization: Microsoft Corp., USA
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  givenname: Tuyong
  surname: Wang
  fullname: Wang, Tuyong
  organization: Sina Corp., China
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Snippet We design and implement Mars, a MapReduce framework, on graphics processors (GPUs). MapReduce is a distributed programming framework originally proposed by...
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SubjectTerms Computer architecture
Data parallelism
GPGPU
Graphics processing units
Graphics Processor
Instruction sets
MapReduce
Multi-core processors
Parallel processing
Programming
Runtime
Web Analysis
Title Mars: A MapReduce Framework on graphics processors
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