The HPCG benchmark represents a modern complement to the HPL benchmark in the performance evaluation of HPC systems, as it has been recognized as a more representative benchmark to reflect real-world applications. While typical workloads become more and more challenging, the semiconductor industry is battling with performance scaling and power efficiency on next-generation technology nodes. As a result, the industry is turning towards more customized compute architectures to help meet the latest performance requirements. In this paper, we present the details of the first FPGA-based implementation of HPCG that takes advantage of such customized compute architectures. Our results show that our high-performance multi-FPGA implementation, using 1 and 4 Xilinx Alveo U280 achieves up to 108.3 GFlops and 346.5 GFlops respectively, representing speed-ups of 104.1x and 333.2x over software running on a server with an Intel Xeon processor with no loss of accuracy. We also demonstrate that the FPGA-based solution achieves comparable performance with respect to modern GPUs and an up to 2.7x improvement in terms of power efficiency compared to an NVIDIA Tesla V100. Finally, a theoretical evaluation, based on Berkeley's Roofline model demonstrates that our implementation is near optimally tuned on the Xilinx Alveo U280.

Optimized Implementation of the HPCG Benchmark on Reconfigurable Hardware

Alberto Zeni;Marco D. Santambrogio
2021-01-01

Abstract

The HPCG benchmark represents a modern complement to the HPL benchmark in the performance evaluation of HPC systems, as it has been recognized as a more representative benchmark to reflect real-world applications. While typical workloads become more and more challenging, the semiconductor industry is battling with performance scaling and power efficiency on next-generation technology nodes. As a result, the industry is turning towards more customized compute architectures to help meet the latest performance requirements. In this paper, we present the details of the first FPGA-based implementation of HPCG that takes advantage of such customized compute architectures. Our results show that our high-performance multi-FPGA implementation, using 1 and 4 Xilinx Alveo U280 achieves up to 108.3 GFlops and 346.5 GFlops respectively, representing speed-ups of 104.1x and 333.2x over software running on a server with an Intel Xeon processor with no loss of accuracy. We also demonstrate that the FPGA-based solution achieves comparable performance with respect to modern GPUs and an up to 2.7x improvement in terms of power efficiency compared to an NVIDIA Tesla V100. Finally, a theoretical evaluation, based on Berkeley's Roofline model demonstrates that our implementation is near optimally tuned on the Xilinx Alveo U280.
2021
Euro-Par 2021: Parallel Processing
9783030856649
9783030856656
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1183092
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 11
  • ???jsp.display-item.citation.isi??? 6
social impact