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Modeling and optimizing NUMA effects and prefetching with machine learning

Authors:

Isaac Sánchez Barrera,

David Black-Schaffer,

Miquel Moretó,

Anastasiia Stupnikova,

Mihail PopovAuthors Info & Claims

ICS '20: Proceedings of the 34th ACM International Conference on Supercomputing

Article No.: 34, Pages 1 - 13

https://doi.org/10.1145/3392717.3392765

Published: 29 June 2020 Publication History

Abstract

Both NUMA thread/data placement and hardware prefetcher configuration have significant impacts on HPC performance. Optimizing both together leads to a large and complex design space that has previously been impractical to explore at runtime.

In this work we deliver the performance benefits of optimizing both NUMA thread/data placement and prefetcher configuration at runtime through careful modeling and online profiling. To address the large design space, we propose a prediction model that reduces the amount of input information needed and the complexity of the prediction required. We do so by selecting a subset of performance counters and application configurations that provide the richest profile information as inputs, and by limiting the output predictions to a subset of configurations that cover most of the performance.

Our model is robust and can choose near-optimal NUMA+Pre-fetcher configurations for applications from only two profile runs. We further demonstrate how to profile online with low overhead, resulting in a technique that delivers an average of 1.68X performance improvement over a locality-optimized NUMA baseline with all prefetchers enabled.

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Cited By

Liu JYu Z(2024)Global-State Aware Automatic NUMA BalancingProceedings of the 15th Asia-Pacific Symposium on Internetware10.1145/3671016.3671380(317-326)Online publication date: 24-Jul-2024
https://dl.acm.org/doi/10.1145/3671016.3671380
Dutta AJannesari A(2024)MIREncoder: Multi-modal IR-based Pretrained Embeddings for Performance OptimizationsProceedings of the 2024 International Conference on Parallel Architectures and Compilation Techniques10.1145/3656019.3676895(156-167)Online publication date: 14-Oct-2024
https://dl.acm.org/doi/10.1145/3656019.3676895
Rocha HMoori MKorol GLorenzon ABeck A(2024)Enhancing Graph Execution for Performance and Energy Efficiency on NUMA Machines2024 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)10.1109/ISVLSI61997.2024.00036(143-148)Online publication date: 1-Jul-2024
https://doi.org/10.1109/ISVLSI61997.2024.00036
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Published In

cover image ACM Conferences

ICS '20: Proceedings of the 34th ACM International Conference on Supercomputing

June 2020

499 pages

ISBN:9781450379830

DOI:10.1145/3392717

General Chairs:
Eduard Ayguadé
Universitat Politècnica de Catalunya and Barcelona Supercomputing Center
,
Wen-mei Hwu
University of Illinois at Urbana-Champaign
,
Program Chairs:
Rosa M. Badia
Barcelona Supercomputing Center and Universitat Politècnica de Catalunya
,
H. Peter Hofstee
IBM Austin

Copyright © 2020 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGARCH: ACM Special Interest Group on Computer Architecture

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Association for Computing Machinery

New York, NY, United States

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Published: 29 June 2020

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Ministerio de Economía y Competitividad
Ministerio de Ciencia, Innovación y Universidades
Ministerio de Educación, Cultura y Deporte
European HiPEAC Network of Excellence
Generalitat de Catalunya
Ministerio de Economía, Industria y Competitividad, Gobierno de España
European Research Council
Knut och Alice Wallenbergs Stiftelse

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ICS '20

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SIGARCH

ICS '20: 2020 International Conference on Supercomputing

June 29 - July 2, 2020

Spain, Barcelona

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Overall Acceptance Rate 629 of 2,180 submissions, 29%

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Cited By

Liu JYu Z(2024)Global-State Aware Automatic NUMA BalancingProceedings of the 15th Asia-Pacific Symposium on Internetware10.1145/3671016.3671380(317-326)Online publication date: 24-Jul-2024
https://dl.acm.org/doi/10.1145/3671016.3671380
Dutta AJannesari A(2024)MIREncoder: Multi-modal IR-based Pretrained Embeddings for Performance OptimizationsProceedings of the 2024 International Conference on Parallel Architectures and Compilation Techniques10.1145/3656019.3676895(156-167)Online publication date: 14-Oct-2024
https://dl.acm.org/doi/10.1145/3656019.3676895
Rocha HMoori MKorol GLorenzon ABeck A(2024)Enhancing Graph Execution for Performance and Energy Efficiency on NUMA Machines2024 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)10.1109/ISVLSI61997.2024.00036(143-148)Online publication date: 1-Jul-2024
https://doi.org/10.1109/ISVLSI61997.2024.00036
Saroliya UArima ELiu DSchulz M(2024)Reinforcement Learning-Driven Co-Scheduling and Diverse Resource Assignments on NUMA Systems2024 IEEE 42nd International Conference on Computer Design (ICCD)10.1109/ICCD63220.2024.00034(170-178)Online publication date: 18-Nov-2024
https://doi.org/10.1109/ICCD63220.2024.00034
Ajibesin AVajjhala NJoel ERakshit S(2024)Predictive Web Prefetching: A Combined Approach Using Clustering Algorithms and WEKA in High-Traffic SettingsArtificial Intelligence in Internet of Things (IoT): Key Digital Trends10.1007/978-981-97-5786-2_17(221-231)Online publication date: 17-Oct-2024
https://doi.org/10.1007/978-981-97-5786-2_17
Jamsaz AMahmud QChen LAhmed NJannesari AOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)PERFOGRAPHProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668640(57783-57794)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3668640
Lin WLuo XLi CLiang JWu GLi K(2023)An Energy-Efficient Tuning Method for Cloud Servers Combining DVFS and Parameter OptimizationIEEE Transactions on Cloud Computing10.1109/TCC.2023.330892711:4(3643-3655)Online publication date: Oct-2023
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Rocha HQuerol VLorenzon ABeck A(2023)Optimizing Single-Source Graph Execution on NUMA Machines2023 XIII Brazilian Symposium on Computing Systems Engineering (SBESC)10.1109/SBESC60926.2023.10324068(1-6)Online publication date: 21-Nov-2023
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Dutta AChoi JJannesari A(2023)Power Constrained Autotuning using Graph Neural Networks2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS54959.2023.00060(535-545)Online publication date: May-2023
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Scravaglieri LPopov MPilla LGuermouche AAumage OSaillard E(2023)Optimizing Performance and Energy Across Problem Sizes Through a Search Space Exploration and Machine LearningJournal of Parallel and Distributed Computing10.1016/j.jpdc.2023.104720(104720)Online publication date: Jun-2023
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