Energy-efficient algebra kernels in FPGA for High Performance Computing

Federico Favaro; Ernesto Dufrechou; Pablo Ezzatti; Juan Pablo Oliver

doi:10.24215/16666038.21.e09

Authors

Federico Favaro Instituto de Ingeniería Eléctrica, Universidad de la República, Uruguay
Ernesto Dufrechou Instituto de Computación, Universidad de la República, Uruguay
Pablo Ezzatti Instituto de Computación, Universidad de la República, Uruguay
Juan Pablo Oliver Instituto de Ingeniería Eléctrica, Universidad de la República, Uruguay

DOI:

https://doi.org/10.24215/16666038.21.e09

Keywords:

dense and sparse NLA, FPGA, HLS, energy consumption

Abstract

The dissemination of multi-core architectures and the later irruption of massively parallel devices, led to a revolution in High-Performance Computing (HPC) platforms in the last decades. As a result, Field-Programmable Gate Arrays (FPGAs) are re-emerging as a versatile and more energy-efficient alternative to other platforms. Traditional FPGA design implies using low-level Hardware Description Languages (HDL) such as VHDL or Verilog, which follow an entirely different programming model than standard software languages, and their use requires specialized knowledge of the underlying hardware. In the last years, manufacturers started to make big efforts to provide High-Level Synthesis (HLS) tools, in order to allow a grater adoption of FPGAs in the HPC community.
Our work studies the use of multi-core hardware and different FPGAs to address Numerical Linear Algebra (NLA) kernels such as the general matrix multiplication GEMM and the sparse matrix-vector multiplication SpMV. Specifically, we compare the behavior of fine-tuned kernels in a multi-core CPU processor and HLS implementations on FPGAs. We perform the experimental evaluation of our implementations on a low-end and a cutting-edge FPGA platform, in terms of runtime and energy consumption, and compare the results against the Intel MKL library in CPU.

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Author Biographies

Federico Favaro, Instituto de Ingeniería Eléctrica, Universidad de la República, Uruguay

https://orcid.org0000-0002-5506-0540/

Ernesto Dufrechou, Instituto de Computación, Universidad de la República, Uruguay

https://orcid.org/0000-0003-4971-340X

Pablo Ezzatti, Instituto de Computación, Universidad de la República, Uruguay

https://orcid.org/0000-0002-2368-8907

Juan Pablo Oliver, Instituto de Ingeniería Eléctrica, Universidad de la República, Uruguay

https://orcid.org/0000-0001-7098-674X

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