An FPGA-Based Application-Specific Processor for Efficient Reduction of Multiple Variable-Length Floating-Point Data Sets

Abstract

Reconfigurable computers (RCs) that combine generalpurpose processors with field-programmable gate arrays (FPGAs) are now available. In these exciting systems, the FPGAs become reconfigurable application-specific processors (ASPs). Specialized high-level language (HLL) to hardware description language (HDL) compilers allow these ASPs to be reconfigured using HLLs. In our research we describe a novel toroidal data structure and scheduling algorithm that allows us to use an HLL-to-HDL environment to implement a high-performance ASP that reduces multiple, variable-length sets of 64-bit floating-point data. We demonstrate the effectiveness of our ASP by using it to accelerate a sparse matrix iterative solver. We compare actual wall clock run times of a production-quality software iterative solver with an ASP-augmented version of the same solver on a current generation RC. Our ASP-augmented solver runs up to 2.4 times faster than software. Estimates show that this same design can run over 6.4 times faster on a next-generation RC.