Scalable HPC Job Scheduling and Resource Management in SST

Efficient job scheduling and resource management contributes towards system throughput and efficiency maximization in high-performance computing (HPC) systems. In this paper, we introduce a scalable job scheduling and resource management component within the structural simulation toolkit (SST), a cycle-accurate and parallel discrete-event simulator. Our proposed simulator includes state-of-the-art job scheduling algorithms and resource management techniques. Additionally, it introduces a workflow management components that supports the simulation of task dependencies and resource allocations, crucial for workflows typical in scientific computing and data-intensive applications. We present validation and scalability results of our job scheduling simulator. Simulation shows that our simulator achieves good accuracy in various metrics (e.g., job wait times, number of nodes usage) and also achieves good parallel performance.

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Publication Details

Published
2024-12-15
DOI
https://doi.org/10.1109/wsc63780.2024.10838714
Citations
4
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
1.40

Funders

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Scalable HPC Job Scheduling and Resource Management in SST

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4 citations
Distributed and Parallel Computing Systems
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article

Scalable HPC Job Scheduling and Resource Management in SST

Kishwar Ahmed, Kazutomo Yoshii, Kevin A. Brown, Abrar Hossain, Abubeker Abdurahman
article en
4 citations

Abstract

Efficient job scheduling and resource management contributes towards system throughput and efficiency maximization in high-performance computing (HPC) systems. In this paper, we introduce a scalable job scheduling and resource management component within the structural simulation toolkit (SST), a cycle-accurate and parallel discrete-event simulator. Our proposed simulator includes state-of-the-art job scheduling algorithms and resource management techniques. Additionally, it introduces a workflow management components that supports the simulation of task dependencies and resource allocations, crucial for workflows typical in scientific computing and data-intensive applications. We present validation and scalability results of our job scheduling simulator. Simulation shows that our simulator achieves good accuracy in various metrics (e.g., job wait times, number of nodes usage) and also achieves good parallel performance.

Argonne National Laboratory (US), University of Toledo (US)
National Science Foundation, U.S. Department of Energy, Office of Science
Decent work and economic growth
Openalex Percentile: Top 16%
Distributed and Parallel Computing Systems
1.40
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