Distributed RankBoost Acceleration Using FPGA and MPI for Web Relevance Ranking

Li, Zhijun; Xu, Ning; Hsu, Feng Hsiung; X, Cai; Gao, Rui; Xia, Zenglin

doi:10.1109/icpads.2008.106

2008 14th IEEE International Conference on Parallel and Distributed Systems 2008

DOI: 10.1109/icpads.2008.106

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Distributed RankBoost Acceleration Using FPGA and MPI for Web Relevance Ranking

Zhijun Li

Ning Xu

Feng Hsiung Hsu

et al.

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2009

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“…The FPGA based accelerator [9] implemented a modified RankBoost algorithm (M3.int) to explore the feature-level parallelism. Later, this parallelism is further explored on a combination of hardware accelerators (FPGA) and computer cluster with MPI [6].…”

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RankBoost Acceleration on both NVIDIA CUDA and ATI Stream Platforms

Wang

Feng

et al. 2009

2009 15th International Conference on Parallel and Distributed Systems

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Abstract-NVIDIA CUDA and ATI Stream are the two major general-purpose GPU (GPGPU) computing technologies. We implemented RankBoost, a web relevance ranking algorithm, on both NVIDIA CUDA and ATI Stream platforms to accelerate the algorithm and illustrate the differences between these two technologies. It shows that the performances of GPU programs are highly dependent on the utilization of GPU's hardware memory architectural features. In this work, we accelerated RankBoost algorithm on both platforms, and we achieved 22.9X speedup on CUDA and 9.2X speedup on ATI Stream respectively. Then we made a comparison on the differences of memory architecture between NVIDIA CUDA and ATI Stream.

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mentioning

confidence: 99%

RankBoost Acceleration on both NVIDIA CUDA and ATI Stream Platforms

Wang

Feng

et al. 2009

2009 15th International Conference on Parallel and Distributed Systems

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Distributed RankBoost Acceleration Using FPGA and MPI for Web Relevance Ranking

Cited by 1 publication

References 13 publications

RankBoost Acceleration on both NVIDIA CUDA and ATI Stream Platforms

RankBoost Acceleration on both NVIDIA CUDA and ATI Stream Platforms

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