RankBoost Acceleration on both NVIDIA CUDA and ATI Stream Platforms

Authors:
Bo Wang;Tianji Wu;Feng Yan;Ruirui Li;Ningyi Xu;Yu Wang
Affiliations:
-;-;-;-;-;-
Venue:
ICPADS '09 Proceedings of the 2009 15th International Conference on Parallel and Distributed Systems
Year:
2009

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Improving on-demand learning to rank through parallelism

WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering

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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.