Platform Agnostic Streaming Data Application Performance Models

Clayton J. Faber; Tom Plano; Samatha Kodali; Zhili Xiao; Abhishek Dwaraki; Jeremy D. Buhler; Roger D. Chamberlain; Anthony M. Cabrera

doi:10.1109/RSDHA54838.2021.00008

Platform Agnostic Streaming Data Application Performance Models

Clayton J. Faber, Tom Plano, Samatha Kodali, Zhili Xiao, Abhishek Dwaraki, Jeremy D. Buhler, Roger D. Chamberlain, Anthony M. Cabrera

Architectures and Performance

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

2 Scopus citations

Abstract

The mapping of computational needs onto execution resources is, by and large, a manual task, and users are frequently guided simply by intuition and past experiences. We present a queueing theory based performance model for streaming data applications that takes steps towards a better understanding of resource mapping decisions, thereby assisting application developers to make good mapping choices. The performance model (and associated cost model) are agnostic to the specific properties of the compute resource and application, simply characterizing them by their achievable data throughput. We illustrate the model with a pair of applications, one chosen from the field of computational biology and the second is a classic machine learning problem.

Original language	English
Title of host publication	Proceedings of RSDHA 2021
Subtitle of host publication	Redefining Scalability for Diversely Heterogeneous Architectures, Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	17-26
Number of pages	10
ISBN (Electronic)	9781665458771
DOIs	https://doi.org/10.1109/RSDHA54838.2021.00008
State	Published - 2021
Event	2021 IEEE/ACM Redefining Scalability for Diversely Heterogeneous Architectures, RSDHA 2021 - St. Louis, United States Duration: Nov 19 2021 → …

Publication series

Name	Proceedings of RSDHA 2021: Redefining Scalability for Diversely Heterogeneous Architectures, Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis

Conference

Conference	2021 IEEE/ACM Redefining Scalability for Diversely Heterogeneous Architectures, RSDHA 2021
Country/Territory	United States
City	St. Louis
Period	11/19/21 → …

Funding

This manuscript has been co-authored by UT-Battelle,LLC under Contract No. DE-AC05-00OR22725with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. This research was supported in part by the following sources: National Science Foundation (NSF) under grant CNS-1763503, Defense Advanced Research Projects Agency (DARPA) Microsystems Technology Office (MTO) Domain-Specific System-on-Chip Program, and the US Department of Energy (DOE) Advanced Scientific Computing Research (ASCR) program.

Funders	Funder number
UT-Battelle,LLC	DE-AC05-00OR22725with
National Science Foundation	CNS-1763503
U.S. Department of Energy
Defense Advanced Research Projects Agency
Advanced Scientific Computing Research

Keywords

data transformation
fpga
gpu

Access to Document

10.1109/RSDHA54838.2021.00008

Cite this

Faber, C. J., Plano, T., Kodali, S., Xiao, Z., Dwaraki, A., Buhler, J. D., Chamberlain, R. D., & Cabrera, A. M. (2021). Platform Agnostic Streaming Data Application Performance Models. In Proceedings of RSDHA 2021: Redefining Scalability for Diversely Heterogeneous Architectures, Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 17-26). (Proceedings of RSDHA 2021: Redefining Scalability for Diversely Heterogeneous Architectures, Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RSDHA54838.2021.00008

Faber, Clayton J. ; Plano, Tom ; Kodali, Samatha et al. / Platform Agnostic Streaming Data Application Performance Models. Proceedings of RSDHA 2021: Redefining Scalability for Diversely Heterogeneous Architectures, Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 17-26 (Proceedings of RSDHA 2021: Redefining Scalability for Diversely Heterogeneous Architectures, Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis).

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abstract = "The mapping of computational needs onto execution resources is, by and large, a manual task, and users are frequently guided simply by intuition and past experiences. We present a queueing theory based performance model for streaming data applications that takes steps towards a better understanding of resource mapping decisions, thereby assisting application developers to make good mapping choices. The performance model (and associated cost model) are agnostic to the specific properties of the compute resource and application, simply characterizing them by their achievable data throughput. We illustrate the model with a pair of applications, one chosen from the field of computational biology and the second is a classic machine learning problem.",

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doi = "10.1109/RSDHA54838.2021.00008",

language = "English",

series = "Proceedings of RSDHA 2021: Redefining Scalability for Diversely Heterogeneous Architectures, Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis",

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Faber, CJ, Plano, T, Kodali, S, Xiao, Z, Dwaraki, A, Buhler, JD, Chamberlain, RD & Cabrera, AM 2021, Platform Agnostic Streaming Data Application Performance Models. in Proceedings of RSDHA 2021: Redefining Scalability for Diversely Heterogeneous Architectures, Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis. Proceedings of RSDHA 2021: Redefining Scalability for Diversely Heterogeneous Architectures, Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis, Institute of Electrical and Electronics Engineers Inc., pp. 17-26, 2021 IEEE/ACM Redefining Scalability for Diversely Heterogeneous Architectures, RSDHA 2021, St. Louis, United States, 11/19/21. https://doi.org/10.1109/RSDHA54838.2021.00008

Platform Agnostic Streaming Data Application Performance Models. / Faber, Clayton J.; Plano, Tom; Kodali, Samatha et al.
Proceedings of RSDHA 2021: Redefining Scalability for Diversely Heterogeneous Architectures, Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis. Institute of Electrical and Electronics Engineers Inc., 2021. p. 17-26 (Proceedings of RSDHA 2021: Redefining Scalability for Diversely Heterogeneous Architectures, Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis).

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

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Faber CJ, Plano T, Kodali S, Xiao Z, Dwaraki A, Buhler JD et al. Platform Agnostic Streaming Data Application Performance Models. In Proceedings of RSDHA 2021: Redefining Scalability for Diversely Heterogeneous Architectures, Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis. Institute of Electrical and Electronics Engineers Inc. 2021. p. 17-26. (Proceedings of RSDHA 2021: Redefining Scalability for Diversely Heterogeneous Architectures, Held in conjunction with SC 2021: The International Conference for High Performance Computing, Networking, Storage and Analysis). doi: 10.1109/RSDHA54838.2021.00008

Platform Agnostic Streaming Data Application Performance Models

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