MITHRA: Multiple data independent tasks on a heterogeneous resource architecture

Reza Farivar, Abhishek Verma, Ellick M. Chan, R H Campbell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the advent of high-performance COTS clusters, there is a need for a simple, scalable and faulttolerant parallel programming and execution paradigm. In this paper, we show that the popular MapReduce programming model can be utilized to solve many interesting scientific simulation problems with much higher performance than regular cluster computers by leveraging GPGPU accelerators in cluster nodes. We use the Massive Unordered Distributed (MUD) formalism and establish a one-to-one correspondence between it and general Monte Carlo simulation methods. Our architecture, MITHRA, leverages NVIDIA CUDA technology along with Apache Hadoop to produce scalable performance gains using the MapReduce programming model. The evaluation of our proposed architecture using the Black Scholes option pricing model shows that a MITHRA cluster of 4 GPUs can outperform a regular cluster of 62 nodes, achieving a speedup of about 254 times in our testbed, while providing scalable near linear performance with additional nodes.

Original languageEnglish (US)
Title of host publication2009 IEEE International Conference on Cluster Computing and Workshops, CLUSTER '09
DOIs
StatePublished - Dec 21 2009
Event2009 IEEE International Conference on Cluster Computing and Workshops, CLUSTER '09 - New Orleans, LA, United States
Duration: Aug 31 2009Sep 4 2009

Publication series

NameProceedings - IEEE International Conference on Cluster Computing, ICCC
ISSN (Print)1552-5244

Other

Other2009 IEEE International Conference on Cluster Computing and Workshops, CLUSTER '09
CountryUnited States
CityNew Orleans, LA
Period8/31/099/4/09

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Signal Processing

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