@inproceedings{Stuart:2011:MMO,
| title | = | "Multi-GPU MapReduce on GPU Clusters", |
| booktitle | = | "Proceedings of the 25th IEEE International Parallel and Distributed Processing Symposium", |
| author | = | "Jeff
A. Stuart AND John
D. Owens ", |
| year | = | "2011", |
| month | = | may, |
| keywords | = | "MultiGPU, MapReduce, GPU Cluster", |
| organization | = | "IEEE", |
| location | = | "Anchorage, Alaska", |
| abstract | = | "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.
", |
