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Related Experiment Video

Updated: Sep 29, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Improving HPC System Performance by Predicting Job Resources via Supervised Machine Learning.

Mohammed Tanash¹, Brandon Dunn¹, Daniel Andresen¹

¹Kansas State University, Manhattan, Kansas.

PEARC19 : Practice and Experience in Advanced Research Computing 2019 : Rise of the Machines (Learning) : July 28-August 1, 2019, Chicago, Illinois. Practice and Experience in Advanced Research Computing (Conference) (2019 : Chicago, Il

|March 21, 2022

Summary

This summary is machine-generated.

We developed a machine learning model to predict High-Performance Computing (HPC) job resource needs, improving cluster efficiency. This reduces job turnaround time and enhances overall HPC system utilization.

Keywords:

HPC Performance Scheduling Slurm Supervised Machine Learning User Modeling

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Area of Science:

Computer Science
Computational Science
Machine Learning

Background:

High-Performance Computing (HPC) systems are crucial for data-intensive research, but inefficient resource allocation is a common problem.
Users often struggle to accurately estimate resource requirements (memory, runtime) for their jobs, leading to over-allocation and wasted resources.
This inefficiency results in suboptimal cluster utilization and increased computational turnaround times.

Purpose of the Study:

To develop and integrate a supervised machine learning model into the Slurm resource manager simulator.
To accurately predict the required memory and execution time for High-Performance Computing (HPC) jobs.
To enhance Slurm performance and improve overall HPC system utilization.

Main Methods:

A supervised machine learning model was developed using various algorithms.
The model was integrated into the Slurm resource manager simulator.
Over 10,000 HPC tasks from log files were used to train and evaluate the model's accuracy and performance.

Main Results:

The integrated model significantly reduced computational turnaround time for large jobs, from five days to ten hours.
Substantial increases in HPC system utilization were observed.
The average waiting time for submitted jobs decreased.

Conclusions:

The supervised machine learning model effectively predicts job resource requirements, leading to improved HPC cluster efficiency.
Accurate resource prediction optimizes Slurm performance and enhances overall HPC system utilization.
The model demonstrates a practical solution to the challenge of inefficient resource allocation in HPC environments.