TY - GEN
T1 - MGMR
T2 - 8th International Conference on Grid and Pervasive Computing, GPC 2013
AU - Chen, Yi
AU - Qiao, Zhi
AU - Jiang, Hai
AU - Li, Kuan Ching
AU - Ro, Won Woo
PY - 2013
Y1 - 2013
N2 - MapReduce is a programming model introduced by Google for large-scale data processing. Several studies have implemented MapReduce model on Graphic Processing Unit (GPU). However, most of them are based on the single GPU and bounded by GPU memory with inefficient atomic operations. This paper intends to develop a standalone MapReduce system, called MGMR, to utilize multiple GPUs, handle large-scale data processing beyond GPU memory limit, and eliminate serial atomic operations. Experimental results have demonstrated MGMR's effectiveness in handling large data set.
AB - MapReduce is a programming model introduced by Google for large-scale data processing. Several studies have implemented MapReduce model on Graphic Processing Unit (GPU). However, most of them are based on the single GPU and bounded by GPU memory with inefficient atomic operations. This paper intends to develop a standalone MapReduce system, called MGMR, to utilize multiple GPUs, handle large-scale data processing beyond GPU memory limit, and eliminate serial atomic operations. Experimental results have demonstrated MGMR's effectiveness in handling large data set.
UR - http://www.scopus.com/inward/record.url?scp=84883407352&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84883407352&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-38027-3_46
DO - 10.1007/978-3-642-38027-3_46
M3 - Conference contribution
AN - SCOPUS:84883407352
SN - 9783642380266
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 433
EP - 442
BT - Grid and Pervasive Computing - 8th International Conference, GPC 2013 and Colocated Workshops, Proceedings
Y2 - 9 May 2013 through 11 May 2013
ER -