research-article

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GPU Adaptive In-situ Parallel Analytics (GAP)

Authors:

Rajiv RamnathAuthors Info & Claims

PACT '22: Proceedings of the International Conference on Parallel Architectures and Compilation Techniques

Pages 467 - 480

https://doi.org/10.1145/3559009.3569661

Published: 27 January 2023 Publication History

Abstract

Despite the popularity of in-situ analytics in scientific computing, there is only limited work to date on in-situ analytics for simulations running on GPUs. Notably, two unaddressed challenges are 1) performing memory-efficient in-situ analysis on accelerators and 2)automatically choosing the processing resources and suitable data representation for a given query and platform. This paper addresses both problems. First, GAP makes several new contributions toward making bitmap indices suitable, effective, and efficient as a compressed data summary structure for the GPUs - this includes introducing a layout structure, a method for generating multi-attribute bitmaps, and novel techniques for bitmap-based processing of major operators that comprise complex data analytics. Second, this paper presents a performance modeling methodology, aiming to predict the placement (i.e., CPU or GPU) and the data representation choice (summarization or original) that yield the best performance on a given configuration. Our extensive evaluation of complex in-situ queries and real-world simulations shows that with our methods, analytics on GPU using bitmaps almost always outperforms other options, and the GAP performance model predicts the optimal placement and data representation for most scenarios.

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Cited By

Ju YLi MPerez ABellentani LJansson NMarkidis SSchlatter PLaure E(2023)In-Situ Techniques on GPU-Accelerated Data-Intensive Applications2023 IEEE 19th International Conference on e-Science (e-Science)10.1109/e-Science58273.2023.10254865(1-10)Online publication date: 9-Oct-2023
https://doi.org/10.1109/e-Science58273.2023.10254865

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PACT '22: Proceedings of the International Conference on Parallel Architectures and Compilation Techniques

October 2022

569 pages

ISBN:9781450398688

DOI:10.1145/3559009

General Chair:
Andreas Kloeckner
University of Illinois
,
Program Chair:
José Moreira
IBM

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SIGARCH: ACM Special Interest Group on Computer Architecture

In-Cooperation

IFIP WG 10.3: IFIP WG 10.3
IEEE CS

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 January 2023

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Research-article

Funding Sources

National Science Foundation

Conference

PACT '22

Sponsor:

SIGARCH

PACT '22: International Conference on Parallel Architectures and Compilation Techniques

October 8 - 12, 2022

Illinois, Chicago

Acceptance Rates

Overall Acceptance Rate 121 of 471 submissions, 26%

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Cited By

Ju YLi MPerez ABellentani LJansson NMarkidis SSchlatter PLaure E(2023)In-Situ Techniques on GPU-Accelerated Data-Intensive Applications2023 IEEE 19th International Conference on e-Science (e-Science)10.1109/e-Science58273.2023.10254865(1-10)Online publication date: 9-Oct-2023
https://doi.org/10.1109/e-Science58273.2023.10254865

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