Parallel I/O Evaluation Techniques and Emerging HPC Workloads: A Perspective Article Swipe

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YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.1109/cluster48925.2021.00100

Emerging workloads such as artificial intelligence, big data analytics and complex multi-step workflows alongside future exascale applications are anticipated future HPC workloads, which will result in a more diverse I/O system workload and even less predictable I/O behavior and access patterns. Along with the ever increasing gap between the compute and storage performance capabilities, the in-depth understanding of extreme-scale I/O behavior and the I/O performance modeling and prediction are essential tools of the large-scale I/O evaluation process for addressing the needs of extreme-scale hybrid workloads. In this survey article, we focus on the state-of-the-art of the I/O behavior and performance analysis process for HPC systems in a 5-year time window and identify future research challenges.

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Computer science Big data Workload Workflow Analytics Process (computing) Supercomputer Perspective (graphical) Data science Scale (ratio) Focus (optics) Distributed computing Operating system Database Artificial intelligence Optics Physics Quantum mechanics

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Type: article
Language: en
Landing Page: https://doi.org/10.1109/cluster48925.2021.00100
OA Status: green
Cited By: 22
References: 81
Related Works: 10
OpenAlex ID: https://openalex.org/W3206719874

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OpenAlex ID: https://openalex.org/W3206719874

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DOI: https://doi.org/10.1109/cluster48925.2021.00100

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Title: Parallel I/O Evaluation Techniques and Emerging HPC Workloads: A Perspective

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Type: article

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Language: en

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Publication year: 2021

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Publication date: 2021-09-01

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Authors: Sarah Neuwirth, Arnab K. Paul

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Landing page: https://doi.org/10.1109/cluster48925.2021.00100

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OA status: green

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OA URL: https://www.osti.gov/biblio/1973311

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Concepts: Computer science, Big data, Workload, Workflow, Analytics, Process (computing), Supercomputer, Perspective (graphical), Data science, Scale (ratio), Focus (optics), Distributed computing, Operating system, Database, Artificial intelligence, Optics, Physics, Quantum mechanics

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Cited by: 22

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References (count): 81

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Related works (count): 10

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abstract_inverted_index.analytics	8
abstract_inverted_index.essential	69
abstract_inverted_index.patterns.	40
abstract_inverted_index.workflows	12
abstract_inverted_index.workloads	1
abstract_inverted_index.addressing	78
abstract_inverted_index.artificial	4
abstract_inverted_index.evaluation	75
abstract_inverted_index.increasing	45
abstract_inverted_index.multi-step	11
abstract_inverted_index.prediction	67
abstract_inverted_index.workloads,	21
abstract_inverted_index.workloads.	84
abstract_inverted_index.anticipated	18
abstract_inverted_index.challenges.	114
abstract_inverted_index.large-scale	73
abstract_inverted_index.performance	52, 64, 99
abstract_inverted_index.predictable	35
abstract_inverted_index.applications	16
abstract_inverted_index.capabilities,	53
abstract_inverted_index.extreme-scale	58, 82
abstract_inverted_index.intelligence,	5
abstract_inverted_index.understanding	56
abstract_inverted_index.state-of-the-art	93
cited_by_percentile_year.max	99
cited_by_percentile_year.min	96
countries_distinct_count	2
institutions_distinct_count	2
sustainable_development_goals[0].id	https://metadata.un.org/sdg/9
sustainable_development_goals[0].score	0.49000000953674316
sustainable_development_goals[0].display_name	Industry, innovation and infrastructure
citation_normalized_percentile.value	0.91524034
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	True