MITHRA: Multiple data independent tasks on a heterogeneous resource architecture

With the advent of high-performance COTS clusters, there is a need for a simple, scalable and fault-tolerant 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.