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14th Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM'06)
Sparse Matrix-Vector Multiplication for Finite Element Method Matrices on FPGAs
Napa, California
April 24-April 26
ISBN: 0-7695-2661-6
Yousef El-Kurdi, McGill University, Canada
Warren J. Gross, McGill University, Canada
Dennis Giannacopoulos, McGill University, Canada
We present an architecture and an implementation of an FPGA-based sparse matrix-vector multiplier (SMVM) for use in the iterative solution of large, sparse systems of equations arising from Finite Element Method (FEM) applications. The architecture is based on a pipelined linear array of processing elements (PEs). A hardware-oriented matrix "striping" scheme is developed which reduces the number of required processing elements. Our current 8 PE prototype achieves a peak performance of 1.76 GFLOPS and a sustained performance of 1.5 GFLOPS with 8 GB/s of memory bandwidth. The SMVM-pipeline uses 30% of the logic resources and 40% of the memory resources of a Stratix S80 FPGA. By virtue of the local interconnect between the PEs, the SMVM-pipeline obtain scalability features that is only limited by FPGA resources instead of the communication overhead.
Citation:
Yousef El-Kurdi, Warren J. Gross, Dennis Giannacopoulos, "Sparse Matrix-Vector Multiplication for Finite Element Method Matrices on FPGAs," fccm, pp.293-294, 14th Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM'06), 2006
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