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Towards Practical, Generalizable Machine-Learning Training Pipelines to build Regression Models for Predicting Application Resource Needs on HPC Systems

Authors:

Manikya Swathi Vallabhajosyula,

Rajiv RamnathAuthors Info & Claims

PEARC '22: Practice and Experience in Advanced Research Computing 2022: Revolutionary: Computing, Connections, You

Article No.: 43, Pages 1 - 5

https://doi.org/10.1145/3491418.3535172

Published: 08 July 2022 Publication History

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PEARC '22: Practice and Experience in Advanced Research Computing 2022: Revolutionary: Computing, Connections, You

Towards Practical, Generalizable Machine-Learning Training Pipelines to build Regression Models for Predicting Application Resource Needs on HPC Systems

Pages 1 - 5

Abstract
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Abstract

This paper explores the potential for cost-effectively developing generalizable and scalable machine-learning-based regression models for predicting the approximate execution time of an HPC application given its input data and parameters. This work examines: (a) to what extent models can be trained on scaled-down datasets on commodity environments and adapted to production environments, (b) to what extent models built for specific applications can generalize to other applications within a family, and (c) how the most appropriate model may change based on the type of data and its mix. As part of this work, we also describe and show the use of an automatable pipeline for generating the necessary training data and building the model.

References

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Vallabhajosyula MBudhya SRamnath R(2024)Reference Implementation of Smart Scheduler: A CI-Aware, AI-Driven Scheduling Framework for HPC WorkloadsPractice and Experience in Advanced Research Computing 2024: Human Powered Computing10.1145/3626203.3670555(1-4)Online publication date: 17-Jul-2024
https://dl.acm.org/doi/10.1145/3626203.3670555
Vallabhajosyula MRamnath R(2023)Towards Characterizing DNNs to Estimate Training Time using HARP (HPC Application Resource (runtime) PredictorPractice and Experience in Advanced Research Computing 2023: Computing for the Common Good10.1145/3569951.3597607(483-485)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.1145/3569951.3597607
Vallabhajosyula MRamnath R(2023)Insights from the HARP Framework: Using an AI-Driven Approach for Efficient Resource Allocation in HPC Scientific WorkflowsPractice and Experience in Advanced Research Computing 2023: Computing for the Common Good10.1145/3569951.3597595(341-344)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.1145/3569951.3597595
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Published In

PEARC '22: Practice and Experience in Advanced Research Computing 2022: Revolutionary: Computing, Connections, You

July 2022

455 pages

ISBN:9781450391610

DOI:10.1145/3491418

Editors:
Julie Wernert
Indiana University
,
Alan Chalker
Ohio Supercomputer Center
,
Shava Smallen
San Diego Supercomputer Center, University of California, San Diego
,
Tabitha Samuel
University of Tennessee, Knoxville
,
JP Navarro
Argonne National Laboratory

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 08 July 2022

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Vallabhajosyula MBudhya SRamnath R(2024)Reference Implementation of Smart Scheduler: A CI-Aware, AI-Driven Scheduling Framework for HPC WorkloadsPractice and Experience in Advanced Research Computing 2024: Human Powered Computing10.1145/3626203.3670555(1-4)Online publication date: 17-Jul-2024
https://dl.acm.org/doi/10.1145/3626203.3670555
Vallabhajosyula MRamnath R(2023)Towards Characterizing DNNs to Estimate Training Time using HARP (HPC Application Resource (runtime) PredictorPractice and Experience in Advanced Research Computing 2023: Computing for the Common Good10.1145/3569951.3597607(483-485)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.1145/3569951.3597607
Vallabhajosyula MRamnath R(2023)Insights from the HARP Framework: Using an AI-Driven Approach for Efficient Resource Allocation in HPC Scientific WorkflowsPractice and Experience in Advanced Research Computing 2023: Computing for the Common Good10.1145/3569951.3597595(341-344)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.1145/3569951.3597595
Vallabhajosyula SRamnath R(2022)Establishing a Generalizable Framework for Generating Cost-Aware Training Data and Building Unique Context-Aware Walltime Prediction Regression Models2022 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00070(497-506)Online publication date: Dec-2022
https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom57177.2022.00070

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Insights from the HARP Framework: Using an AI-Driven Approach for Efficient Resource Allocation in HPC Scientific Workflows

Parallel resource co-allocation for the computational grid

A demonstration-based model transformation approach to automate model scalability

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