この分野（特にGPU関連）はスピード勝負です。特に以下の IPDPS 2011 (5月) の論文は必見。
Multi-GPU MapReduce on GPU Clusters, IPDPS 2011
http://www.idav.ucdavis.edu/publications/print_pub?pub_id=1051
We present GPMR, our MapReduce library that leverages the power of GPU clusters for large-scale computing. To better utilize the GPU, we modify MapReduce by combining large amounts of map and reduce items into chunks and using partial reductions and accumulation. We use persistent map and reduce tasks and stress aspects of GPMR with a set of standard MapReduce benchmarks. We run these benchmarks on a GPU cluster and achieve desirable speedup and efficiency for all benchmarks. We compare our implementation to the current-best GPU-MapReduce library (runs only on a solo GPU) and a highly-optimized multi-core MapReduce to show the power of GPMR. We demonstrate how typical MapReduce tasks are easily modified to fit into GPMR and leverage a GPU cluster. We highlight how total and relative amounts of communication affect GPMR. We conclude with an exposition on the types of MapReduce tasks well-suited to GPMR, and why some tasks need more modifications than others to work well with GPMR.
Designing efficient sorting algorithms for manycore GPUs, IPDPS 2009
http://portal.acm.org/citation.cfm?id=1587667
Tiled-MapReduce: optimizing resource usages of data-parallel applications on multicore with tiling, PACT 2010,
http://portal.acm.org/citation.cfm?id=1854337&CFID=14942240&CFTOKEN=86587012
gnuplotでeps
13 年前
