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Using FPGAs to Accelerate HPC and Data Analytics on Intel-Based Systems

  • Thomas SteinkeEmail author
  • Estela Suarez
  • Taisuke Boku
  • Nalini Kumar
  • David E. Martin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11887)

Abstract

FPGAs can improve performance, energy efficiency and throughput by boosting computation, I/O and communication operations in HPC, data analytics (DA), and machine learning (ML) workloads and thus complement general-purpose CPUs and GPUs. Recent innovations in hardware and software technologies make FPGAs increasingly attractive for HPC and DA workloads. This first FPGA-focused workshop organized by the IXPUG community gathered experts in the design, programming and usage of reconfigurable systems for HPC and DA workloads to share there experiences with the community.

Keywords

FPGA Reconfigurable computing High-performance computing Data analytics Machine learning Intel FPGA ecosystem 

References

  1. 1.
    IXPUG: The Intel eXtreme Performance User’s Group. http://www.ixpug.org

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Zuse Institute Berlin (ZIB)BerlinGermany
  2. 2.Jülich Supercomputing Centre (JSC) - Forschungszentrum Jülich GmbHJülichGermany
  3. 3.University of TsukubaTsukubaJapan
  4. 4.Intel CorporationSanta ClaraUSA
  5. 5.Argonne National LaboratoryArgonneUSA

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