Title :
RankBoost Acceleration on both NVIDIA CUDA and ATI Stream Platforms
Author :
Wang, Bo ; Wu, Tianji ; Yan, Feng ; Li, Ruirui ; Xu, Ningyi ; Wang, Yu
Author_Institution :
Dept. of Electron. Eng., Tsinghua Univ., Beijing, China
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.
Keywords :
Internet; coprocessors; relevance feedback; ATI Stream platforms; NVIDIA CUDA platforms; RankBoost acceleration; Web relevance ranking; general-purpose GPU computing technology; hardware memory architectural features; Acceleration; Boosting; Clustering algorithms; Concurrent computing; Hardware; Large-scale systems; Machine learning algorithms; Parallel processing; Search engines; Web search; ATI Stream; CUDA; GPGPU; RankBoost acceleration;
Conference_Titel :
Parallel and Distributed Systems (ICPADS), 2009 15th International Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-1-4244-5788-5
DOI :
10.1109/ICPADS.2009.115
