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Perspectives on High-Performance Computing in a Big Data World

Published: 17 June 2019 Publication History

Abstract

High-Performance Computing (HPC) and Cyberinfrastructure have played a leadership role in computational science even since the start of the NSF computing centers program. Thirty years ago parallel computing was a centerpiece of computer science research. Naively Big Data surely requires HPC to be processed, and transformational Big Data technology such as Hadoop and Spark exploit parallelism to success. Nevertheless, the HPC community does not appear to be thriving as a leader in Data Science while parallel computing is no longer a centerpiece. Some reasons for this are the dominant presence of Industry in technology futures and the universal fascination with Artificial Intelligence and Machine Learning. Maybe the pendulum will swing back a bit, but I expect the "AI first" philosophy to dominate in the foreseeable future. Thus I describe a future where HPC thrives in collaboration with Industry and AI. In particular, I discuss the promise of MLforHPC (AI for systems) and HPCforML (systems for AI).

Reference

[1]
Geoffrey Fox, James A. Glazier, JCS Kadupitiya, Vikram Jadhao, Minje Kim, Judy Qiu, James P. Sluka, Endre Somogyi, Madhav Marathe, Abhijin Adiga, Jiangzhuo Chen, Oliver Beckstein, and Shantenu Jha, "Learning Everywhere: Pervasive Machine Learning for Effective High-Performance Computation", HPBDC workshop at IPDPS, Rio de Janeiro, Brazil, Monday, May 20th, 2019

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Published In

cover image ACM Conferences
HPDC '19: Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing
June 2019
278 pages
ISBN:9781450366700
DOI:10.1145/3307681
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 June 2019

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Author Tags

  1. HPC
  2. big data
  3. computational science
  4. data science

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  • Keynote

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HPDC '19
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HPDC '19 Paper Acceptance Rate 22 of 106 submissions, 21%;
Overall Acceptance Rate 166 of 966 submissions, 17%

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