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First Impressions of the Sapphire Rapids Processor with HBM for Scientific Workloads

  • Eva Siegmann
  • , Robert J. Harrison
  • , David Carlson
  • , Smeet Chheda
  • , Anthony Curtis
  • , Firat Coskun
  • , Raul Gonzalez
  • , Daniel Wood
  • , Nikolay A. Simakov
  • Stony Brook University

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

The landscape of high performance computing (HPC) has witnessed exponential growth in processor diversity, architectural complexity, and performance scalability. With an ever-increasing demand for faster and more efficient computing solutions to address an array of scientific, engineering, and societal challenges, the selection of processors for specific applications becomes paramount. Achieving optimal performance requires a deep understanding of how diverse processors interact with diverse workloads, making benchmarking a fundamental practice in the field of HPC. Here, we present preliminary results observed over such benchmarks and applications and a comparison of Intel Sapphire Rapids and Skylake-X, AMD Milan, and Fujitsu A64FX processors in terms of runtime performance, memory bandwidth utilization, and energy consumption. The examples focus specifically on the Sapphire Rapids processor with and without high-bandwidth memory (HBM). An additional case study reports the performance gains from using Intel’s Advanced Matrix Extensions (AMX) instructions, and how they along with HBM can be leveraged to accelerate AI workloads. These initial results aim to give a rough comparison of the processors rather than a detailed analysis and should prove timely and relevant for researchers who may be interested in using Sapphire Rapids for their scientific workloads.

Original languageEnglish
Article number623
JournalSN Computer Science
Volume5
Issue number5
DOIs
StatePublished - Jun 2024

Keywords

  • AMD Milan
  • AMX
  • Benchmarking
  • Energy usage
  • Fujitsu A64FX
  • HBM
  • Intel Sapphire rapids
  • Intel Skylake
  • Performance
  • TMUL

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