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Graph analytics in the exascale era

Published: 11 May 2021 Publication History

Abstract

Emergence of large-scale data sets has ushered in a new era of data-driven discovery in science and beyond that is enabled by advances in artificial intelligence techniques and high-performance computing. Graph analytics is a rapidly emerging area of research and application that enables several classes of applications. Generalization of graph algorithms in the form of combinatorial optimization has numerous applications in scientific computing and data-driven discovery. Despite widespread use, efficient parallel tools for graph analytics are hard to come by, especially when targeting the hybrid CPU-Graphics Processing Unit architectures at extreme scales. In this talk, we will present our ongoing work on distributed multi-GPU systems for two prototypical graph problems: graph clustering and influence maximization. We will demonstrate substantial gains in performance not only on PNNL systems but also on the current # 2 supercomputer, Summit. We will also present case studies from several scientific domains of importance to the DOE.

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cover image ACM Conferences
CF '21: Proceedings of the 18th ACM International Conference on Computing Frontiers
May 2021
254 pages
ISBN:9781450384049
DOI:10.1145/3457388
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: 11 May 2021

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  • Invited-talk

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CF '21
Sponsor:
CF '21: Computing Frontiers Conference
May 11 - 13, 2021
Virtual Event, Italy

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Overall Acceptance Rate 273 of 785 submissions, 35%

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CF '25

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