Hits:

Indexed by:期刊论文

Date of Publication:2015-04-01

Journal:Journal of Computational Information Systems

Included Journals:EI、Scopus

Volume:11

Issue:7

Page Number:2615-2622

ISSN No.:15539105

Abstract:GPU programming model for general purpose computing is complex and difficult to be maintained. A MapReduce acceleration framework named MRCUDA is designed and implemented in this paper. There are four loosely coupled stages in MRCUDA, including Pre-Processing, Map, Group and Reduce, which can support flexible configurations for different applications. In order to take full advantage of GPU parallelism, a bitonic sorting algorithm is designed and implemented in the Group stage, and its performance is superior to general GPU sorting algorithms. Finally, according to five kinds of typical application tests, it is demonstrated that MRCUDA computing platform can reduce code scale and achieve ideal running speedup ratio. ?, 2015, Binary Information Press. All right reserved.