Exploring The Performance Benefits Of Heterogeneity And Reconfigurable Architectures In A Commodity Cloud

in this paper we evaluate the potential of running a compute-intensive simulation on a heterogeneous cluster built from CPU, GPU and FPGA devices. We do so by augmenting a commercially available cluster of CPUs and GPUs with an FPGA device and running a distributed n-body simulation on top of Spark for unconventional cores (SparkCL) on the three different types of computing architectures. We show that given an algorithm with a sufficiently high compute intensity, such as pairwise additive n-body, we can significantly increase performance and performance per watt in comparison to running the same algorithm on a homogeneous CPU based cluster. In addition, we show the potential of using FPGAs in future commodity heterogeneous clusters alongside CPUs and GPUs