research-article

Bootstrapping in-situ workflow auto-tuning via combining performance models of component applications

Authors:

Justin Wozniak,

Tahsin KurcAuthors Info & Claims

SC '21: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

Article No.: 28, Pages 1 - 15

https://doi.org/10.1145/3458817.3476197

Published: 13 November 2021 Publication History

Abstract

In an in-situ workflow, multiple components such as simulation and analysis applications are coupled with streaming data transfers. The multiplicity of possible configurations necessitates an auto-tuner for workflow optimization. Existing auto-tuning approaches are computationally expensive because many configurations must be sampled by running the whole workflow repeatedly in order to train the auto-tuner surrogate model or otherwise explore the configuration space. To reduce these costs, we instead combine the performance models of component applications by exploiting the analytical workflow structure, selectively generating test configurations to measure and guide the training of a machine learning workflow surrogate model. Because the training can focus on well-performing configurations, the resulting surrogate model can achieve high prediction accuracy for good configurations despite training with fewer total configurations. Experiments with real applications demonstrate that our approach can identify significantly better configurations than other approaches for a fixed computer time budget.

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Index Terms

Bootstrapping in-situ workflow auto-tuning via combining performance models of component applications
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

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cover image ACM Conferences

SC '21: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

November 2021

1493 pages

ISBN:9781450384421

DOI:10.1145/3458817

General Chair:
Bronis R. de Supinski,
Program Chairs:
Mary Hall,
Todd Gamblin

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© 2021 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Published: 13 November 2021

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https://dl.acm.org/doi/10.1145/3624062.3624260
Li YBaik JRahman MAnagnostopoulos ILi RShu T(2023)Pareto Optimization of CNN Models via Hardware-Aware Neural Architecture Search for Drainage Crossing Classification on Resource-Limited DevicesProceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis10.1145/3624062.3624258(1767-1775)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3624062.3624258
Timothy Tawose ODai JYang LZhao D(2023)Toward Efficient Homomorphic Encryption for Outsourced Databases through Parallel CachingProceedings of the ACM on Management of Data10.1145/35889201:1(1-23)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.1145/3588920
Tawose OYang LZhao D(2023)TopoCommit: A Topological Commit Protocol for Cross-Ledger Transactions in Scientific Computing2023 IEEE International Conference on Cluster Computing (CLUSTER)10.1109/CLUSTER52292.2023.00038(365-375)Online publication date: 31-Oct-2023
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Wang ZDorier MSubedi PDavis PParashar M(2023)Adaptive elasticity policies for staging-based in situ visualizationFuture Generation Computer Systems10.1016/j.future.2022.12.010142:C(75-89)Online publication date: 1-May-2023
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Wu YWu HLuo DXu YHu YZhang WZhong HGavrilovska AAltınbüken DBinnig C(2022)Serving unseen deep learning models with near-optimal configurationsProceedings of the 13th Symposium on Cloud Computing10.1145/3542929.3563485(461-476)Online publication date: 7-Nov-2022
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Foster IAinsworth MBessac JCappello FChoi JDi SDi ZGok AGuo HHuck KKelly CKlasky SKleese van Dam KLiang XMehta KParashar MPeterka TPouchard LShu TTugluk Ovan Dam HWan LWolf MWozniak JXu WYakushin IYoo SMunson T(2021)Online data analysis and reduction: An important Co-design motif for extreme-scale computersThe International Journal of High Performance Computing Applications10.1177/10943420211023549(109434202110235)Online publication date: 12-Jun-2021
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