Extending Skel to Support the Development and Optimization of Next Generation I/O Systems

Jeremy Logan; Jong Youl Choi; Matthew Wolf; George Ostrouchov; Lipeng Wan; Norbert Podhorszki; William Godoy; Scott Klasky; Erich Lohrmann; Greg Eisenhauer; Chad Wood; Kevin Huck

doi:10.1109/CLUSTER.2017.30

Extending Skel to Support the Development and Optimization of Next Generation I/O Systems

Jeremy Logan, Jong Youl Choi, Matthew Wolf, George Ostrouchov, Lipeng Wan, Norbert Podhorszki, William Godoy, Scott Klasky, Erich Lohrmann, Greg Eisenhauer, Chad Wood, Kevin Huck

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

As the memory and storage hierarchy get deeper and more complex, it is important to have new benchmarks and evaluation tools that allow us to explore the emerging middleware solutions to use this hierarchy. Skel is a tool aimed at automating and refining this process of studying HPC I/O performance. It works by generating application I/O kernel/benchmarks as determined by a domain-specific model. This paper provides some techniques for extending Skel to address new situations and to answer new research questions. For example, we document use cases as diverse as using Skel to troubleshoot I/O performance issues for remote users, refining an I/O system model, and facilitating the development and testing of a mechanism for runtime monitoring and performance analytics. We also discuss data oriented extensions to Skel to support the study of compression techniques for Exascale scientific data management.

Original language	English
Title of host publication	Proceedings - 2017 IEEE International Conference on Cluster Computing, CLUSTER 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	563-571
Number of pages	9
ISBN (Electronic)	9781538623268
DOIs	https://doi.org/10.1109/CLUSTER.2017.30
State	Published - Sep 22 2017
Event	2017 IEEE International Conference on Cluster Computing, CLUSTER 2017 - Honolulu, United States Duration: Sep 5 2017 → Sep 8 2017

Publication series

Name	Proceedings - IEEE International Conference on Cluster Computing, ICCC
Volume	2017-September
ISSN (Print)	1552-5244

Conference

Conference	2017 IEEE International Conference on Cluster Computing, CLUSTER 2017
Country/Territory	United States
City	Honolulu
Period	09/5/17 → 09/8/17

Funding

Support was also provided by the National Science Foundation under Grant Number 1265403. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Program Manager Dr. Lucy Nowell, under Award Number DE-SC0012537. Also, this research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility.

Funders	Funder number
National Science Foundation	1265403
U.S. Department of Energy
Office of Science
Advanced Scientific Computing Research	DE-SC0012537

Keywords

Adios
Data Compression
Generative Programming
High Performance I/O
I/O benchmarking
I/O performance
Mini Applications
Runtime performance analytics
Runtime performance monitoring
Scientific Data Management
Skel

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10.1109/CLUSTER.2017.30

Cite this

Logan, J., Choi, J. Y., Wolf, M., Ostrouchov, G., Wan, L., Podhorszki, N., Godoy, W., Klasky, S., Lohrmann, E., Eisenhauer, G., Wood, C., & Huck, K. (2017). Extending Skel to Support the Development and Optimization of Next Generation I/O Systems. In Proceedings - 2017 IEEE International Conference on Cluster Computing, CLUSTER 2017 (pp. 563-571). Article 8048970 (Proceedings - IEEE International Conference on Cluster Computing, ICCC; Vol. 2017-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CLUSTER.2017.30

Logan, Jeremy ; Choi, Jong Youl ; Wolf, Matthew et al. / Extending Skel to Support the Development and Optimization of Next Generation I/O Systems. Proceedings - 2017 IEEE International Conference on Cluster Computing, CLUSTER 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 563-571 (Proceedings - IEEE International Conference on Cluster Computing, ICCC).

@inproceedings{2952d78daf1d45229efaed1d7f6d005a,

title = "Extending Skel to Support the Development and Optimization of Next Generation I/O Systems",

abstract = "As the memory and storage hierarchy get deeper and more complex, it is important to have new benchmarks and evaluation tools that allow us to explore the emerging middleware solutions to use this hierarchy. Skel is a tool aimed at automating and refining this process of studying HPC I/O performance. It works by generating application I/O kernel/benchmarks as determined by a domain-specific model. This paper provides some techniques for extending Skel to address new situations and to answer new research questions. For example, we document use cases as diverse as using Skel to troubleshoot I/O performance issues for remote users, refining an I/O system model, and facilitating the development and testing of a mechanism for runtime monitoring and performance analytics. We also discuss data oriented extensions to Skel to support the study of compression techniques for Exascale scientific data management.",

keywords = "Adios, Data Compression, Generative Programming, High Performance I/O, I/O benchmarking, I/O performance, Mini Applications, Runtime performance analytics, Runtime performance monitoring, Scientific Data Management, Skel",

author = "Jeremy Logan and Choi, {Jong Youl} and Matthew Wolf and George Ostrouchov and Lipeng Wan and Norbert Podhorszki and William Godoy and Scott Klasky and Erich Lohrmann and Greg Eisenhauer and Chad Wood and Kevin Huck",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Cluster Computing, CLUSTER 2017 ; Conference date: 05-09-2017 Through 08-09-2017",

year = "2017",

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doi = "10.1109/CLUSTER.2017.30",

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Logan, J , Choi, JY, Wolf, M, Ostrouchov, G, Wan, L, Podhorszki, N , Godoy, W , Klasky, S, Lohrmann, E, Eisenhauer, G, Wood, C & Huck, K 2017, Extending Skel to Support the Development and Optimization of Next Generation I/O Systems. in Proceedings - 2017 IEEE International Conference on Cluster Computing, CLUSTER 2017., 8048970, Proceedings - IEEE International Conference on Cluster Computing, ICCC, vol. 2017-September, Institute of Electrical and Electronics Engineers Inc., pp. 563-571, 2017 IEEE International Conference on Cluster Computing, CLUSTER 2017, Honolulu, United States, 09/5/17. https://doi.org/10.1109/CLUSTER.2017.30

Extending Skel to Support the Development and Optimization of Next Generation I/O Systems. / Logan, Jeremy ; Choi, Jong Youl; Wolf, Matthew et al.
Proceedings - 2017 IEEE International Conference on Cluster Computing, CLUSTER 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 563-571 8048970 (Proceedings - IEEE International Conference on Cluster Computing, ICCC; Vol. 2017-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Choi, Jong Youl

AU - Wolf, Matthew

AU - Ostrouchov, George

AU - Wan, Lipeng

AU - Podhorszki, Norbert

AU - Godoy, William

AU - Klasky, Scott

AU - Lohrmann, Erich

AU - Eisenhauer, Greg

AU - Wood, Chad

AU - Huck, Kevin

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N2 - As the memory and storage hierarchy get deeper and more complex, it is important to have new benchmarks and evaluation tools that allow us to explore the emerging middleware solutions to use this hierarchy. Skel is a tool aimed at automating and refining this process of studying HPC I/O performance. It works by generating application I/O kernel/benchmarks as determined by a domain-specific model. This paper provides some techniques for extending Skel to address new situations and to answer new research questions. For example, we document use cases as diverse as using Skel to troubleshoot I/O performance issues for remote users, refining an I/O system model, and facilitating the development and testing of a mechanism for runtime monitoring and performance analytics. We also discuss data oriented extensions to Skel to support the study of compression techniques for Exascale scientific data management.

AB - As the memory and storage hierarchy get deeper and more complex, it is important to have new benchmarks and evaluation tools that allow us to explore the emerging middleware solutions to use this hierarchy. Skel is a tool aimed at automating and refining this process of studying HPC I/O performance. It works by generating application I/O kernel/benchmarks as determined by a domain-specific model. This paper provides some techniques for extending Skel to address new situations and to answer new research questions. For example, we document use cases as diverse as using Skel to troubleshoot I/O performance issues for remote users, refining an I/O system model, and facilitating the development and testing of a mechanism for runtime monitoring and performance analytics. We also discuss data oriented extensions to Skel to support the study of compression techniques for Exascale scientific data management.

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KW - High Performance I/O

KW - I/O benchmarking

KW - I/O performance

KW - Mini Applications

KW - Runtime performance analytics

KW - Runtime performance monitoring

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M3 - Conference contribution

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EP - 571

BT - Proceedings - 2017 IEEE International Conference on Cluster Computing, CLUSTER 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE International Conference on Cluster Computing, CLUSTER 2017

Y2 - 5 September 2017 through 8 September 2017

ER -

Logan J , Choi JY, Wolf M, Ostrouchov G, Wan L, Podhorszki N et al. Extending Skel to Support the Development and Optimization of Next Generation I/O Systems. In Proceedings - 2017 IEEE International Conference on Cluster Computing, CLUSTER 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 563-571. 8048970. (Proceedings - IEEE International Conference on Cluster Computing, ICCC). doi: 10.1109/CLUSTER.2017.30

Extending Skel to Support the Development and Optimization of Next Generation I/O Systems

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