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Title
Japanese:汎用グラフ処理モデルGIM-Vの複数GPUによる大規模計算とデータ転送の最適化 
English:A Multi GPU Implementation of Generalized Graph Processing Model GIM-V with Data Transfer Optimization 
Author
Japanese: 白幡晃一, 佐藤仁, 鈴村豊太郎, 松岡聡.  
English: Koichi Shirahata, Hitoshi Sato, Toyotaro Suzumura, SATOSHI MATSUOKA.  
Language Japanese 
Journal/Book name
Japanese:情報処理学会研究報告2012-HPC-133 
English: 
Volume, Number, Page        
Published date Mar. 2012 
Publisher
Japanese:情報処理学会 
English: 
Conference name
Japanese:第133回 ハイパフォーマンスコンピューティング研究発表会 
English: 
Conference site
Japanese:兵庫県神戸市 
English: 
Official URL http://sighpc.hpcc.jp/sighpc-133/
 
Abstract In recent years, fast processing for extremely large-scale graph, consisting of millions to trillions of vertices and billions to hundreds of trillions of edges, is becoming increasingly important. GIM-V graph processing algorithm based on MapReduce, which automatically manages petabyte-scale data, is designed as general graph processing method. Besides, recent large-scale computing systems tend to employ GPUs to gain good peak performance and high memory bandwidth. However, acceleration factor using GPU for large graph processing and the way of efficient data distribution are not clear. We implemented multi GPU based GIM-V system and investigated the effect of graph partitioning, which is a method to reduce inter-node data transfer. Our experiment showed that GPU performed 7.17x faster than CPU on Map but not on Shuffle and Reduce, and graph partitioning reduced data transfer time by 54% but total elapsed time increased due to workload imbalance.

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