A. Sudarsanam, T. Hauser, A. Dasu, and S. Young
FPGA, linear algebra, right-hand side solver
This paper presents an approach to explore a commercial multifield programmable gate array (FPGA) system as high performance accelerator and the problem of solving an LU decomposed linear system of equations using forward and back substitution is addressed. Block-based right-hand-side solver algorithm is described and a novel data flow and memory architectures that can support arbitrary data types, block sizes and matrix sizes is proposed. These architectures have been implemented on a multi-FPGA system. Capabilities of the accelerator system are pushed to its limits by implementing the problem for double precision complex floating-point data. Detailed timing data is presented and augmented with data from a performance model proposed in this paper. Performance of the accelerator system is evaluated against that of a state of the art low power Beowulf cluster node running an optimized LAPACK implementation. Both systems are compared using the power efficiency (performance/watt) metric. FPGA system is about eleven times more power efficient than the compute node of a cluster.
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