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Advisor(s)
Abstract(s)
Sparse matrix-vector multiplication (SMVM) is a fundamental operation in many scientific and engineering applications. In many cases sparse matrices have thousands of rows and columns where most of the entries are zero, while non-zero data is spread over the matrix. This sparsity of data locality reduces the effectiveness of data cache in general-purpose processors quite reducing their performance efficiency when compared to what is achieved with dense matrix multiplication. In this paper, we propose a parallel processing solution for SMVM in a many-core architecture. The architecture is tested with known benchmarks using a ZYNQ-7020 FPGA. The architecture is scalable in the number of core elements and limited only by the available memory bandwidth. It achieves performance efficiencies up to almost 70% and better performances than previous FPGA designs.
Description
Keywords
Sparse matrix FPGA Many-core Matrix multiplication
Citation
PINHÃO, João; [et al] - Sparse matrix multiplication on a reconfigurable many-core architecture. In 18th Euromicro Conference on Digital System Design (DSD). Funchal, Portugal: IEEE, 2015. ISBN 978-1-4673-8035-5. Pp. 330-336
Publisher
IEEE-Institute Electrical Electronics Engineers INC