Energy-Efficient Stochastic Matrix Function Estimator for Graph Analytics on FPGA

Big Data applications require efficient processing of large graphs to unveil information that is hidden in the structural relationships among objects. In order to cope with the growing complexity of data sets many graph algorithms can be expressed to apply linear algebra operations for which highly efficient algorithms exist. In this paper we present an FPGA implementation of a stochastic matrix function estimator, a powerful framework for statistical approximation of general matrix functions. We apply the accelerator to the subgraph centrality method for ranking nodes in complex networks. Performance and energy consumption results are based on actual measurements of a POWERS hybrid compute platform. A single FPGA co-processor improves the runtime by more than 50% compared to multi-threaded software while delivering the same estimation quality. In terms of energy consumption the FPGA outperforms CPU and GPU solutions by a factor of 13x and 3x, respectively. Our results show that FPGA co-processors can provide significant gains for graph analytics applications and are a promising solution for energy efficient computing in the data center.