The MIT Supercloud Workload Classification Challenge Article Swipe
Benny J. Tang
,
Qiqi Chen
,
Matthew L. Weiss
,
Nathan C. Frey
,
Joseph McDonald
,
David Bestor
,
Yee, Charles
,
William Arcand
,
Chansup Byun
,
Daniel C. Edelman
,
Matthew Hubbell
,
Michael Jones
,
Jeremy Kepner
,
Anna Klein
,
Adam Michaleas
,
Michaleas, Peter
,
Lauren Milechin
,
Julia Mullen
,
Andrew Prout
,
Albert Reuther
,
Antonio De Rosa
,
Andrew Bowne
,
Lindsey McEvoy
,
Li, Baolin
,
Diwakar Tiwari
,
Vijay Gadepally
,
Devesh Tiwari
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/ipdpsw55747.2022.00122
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/ipdpsw55747.2022.00122
High-Performance Computing (HPC) centers and cloud providers support an increasingly diverse set of applications on heterogenous hardware. As Artificial Intelligence (AI) and Machine Learning (ML) workloads have become an increasingly larger share of the compute workloads, new approaches to optimized resource usage, allocation, and deployment of new AI frameworks are needed. By identifying compute workloads and their utilization characteristics, HPC systems may be able to better match available resources with the application demand. By leveraging datacenter instrumentation, it may be possible to develop AI-based approaches that can identify workloads and provide feedback to researchers and datacenter operators for improving operational efficiency. To enable this research, we released the MIT Supercloud Dataset, which provides detailed monitoring logs from the MIT Supercloud cluster. This dataset includes CPU and GPU usage by jobs, memory usage, and file system logs. In this paper, we present a workload classification challenge based on this dataset. We introduce a labelled dataset that can be used to develop new approaches to workload classification and present initial results based on existing approaches. The goal of this challenge is to foster algorithmic innovations in the analysis of compute workloads that can achieve higher accuracy than existing methods. Data and code will be made publicly available via the Datacenter Challenge website : https://dcc.mit.edu.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/ipdpsw55747.2022.00122
- OA Status
- green
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4223979420
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4223979420Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/ipdpsw55747.2022.00122Digital Object Identifier
- Title
-
The MIT Supercloud Workload Classification ChallengeWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-05-01Full publication date if available
- Authors
-
Benny J. Tang, Qiqi Chen, Matthew L. Weiss, Nathan C. Frey, Joseph McDonald, David Bestor, Yee, Charles, William Arcand, Chansup Byun, Daniel C. Edelman, Matthew Hubbell, Michael Jones, Jeremy Kepner, Anna Klein, Adam Michaleas, Michaleas, Peter, Lauren Milechin, Julia Mullen, Andrew Prout, Albert Reuther, Antonio De Rosa, Andrew Bowne, Lindsey McEvoy, Li, Baolin, Diwakar Tiwari, Vijay Gadepally, Devesh TiwariList of authors in order
- Landing page
-
https://doi.org/10.1109/ipdpsw55747.2022.00122Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2204.05839Direct OA link when available
- Concepts
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Computer science, Workload, Software deployment, Cloud computing, Set (abstract data type), Distributed computing, Resource (disambiguation), Instrumentation (computer programming), Machine learning, Artificial intelligence, Data mining, Operating system, Computer network, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
22Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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