In-depth optimization with the OpenACC-to-FPGA framework on an arria 10 FPGA

Jacob Lambert, Seyong Lee, Jeffrey S. Vetter, Allen Malony

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

The reconfigurable computing paradigm that uses field programmable gate arrays (FPGAs) has received renewed interest in the high-performance computing field due to FPGAs' unique combination of performance and energy efficiency. However, difficulties in programming and optimizing FPGAs have prevented them from being widely accepted as general-purpose computing devices. In accelerator-based heterogeneous computing, portability across diverse heterogeneous devices is also an important issue, but the unique architectural features in FPGAs make this difficult to achieve. To address these issues, a directive-based, high-level FPGA programming and optimization framework was previously developed. In this work, developed optimizations were combined holistically using the directive-based approach to show that each individual benchmark requires a unique set of optimizations to maximize performance. The relationships between FPGA resource usages and runtime performance were also explored.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 34th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages460-470
Number of pages11
ISBN (Electronic)9781728174457
DOIs
StatePublished - May 2020
Event34th IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2020 - New Orleans, United States
Duration: May 18 2020May 22 2020

Publication series

NameProceedings - 2020 IEEE 34th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2020

Conference

Conference34th IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2020
Country/TerritoryUnited States
CityNew Orleans
Period05/18/2005/22/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Funding

This research was supported in part by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the US Department of Energy (DOE) Office of Science (OS) and the National Nuclear Security Administration. This material is based upon work supported by the DOE OS Advanced Scientific Computing Research under contract number DE-AC05-00OR22725.The US government retains— and the publisher, by accepting the article for publication, acknowledges that the US government retains—a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

FundersFunder number
US Department of Energy
U.S. Department of Energy
Office of Science
National Nuclear Security Administration
Advanced Scientific Computing ResearchDE-AC05-00OR22725

    Keywords

    • Compiler optimization
    • Directive-based programming
    • FPGA
    • OpenACC
    • OpenARC

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