Balancing energy and performance: efficient allocation of solver jobs on high-performance computing systems

DOI
URI
Conference
34th European Conference on Operational Research, EURO 2025 ; Leeds, UK ; June 22–25, 2025
Project
Publisher
Universitätsbibliothek der HSU/UniBw H
Part of the university bibliography
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Files
Language
English
DDC Class
004 Informatik
Keyword
dtec.bw
hpc.bw
High performance computing
Mixed-integer programming
Performance analysis
Energy efficiency
Abstract
Many combinatorial optimization methods and related optimization software, particularly those for mixed-integer programming, exhibit limited scalability when utilizing parallel computing resources, whether across multiple cores or multiple nodes. Nevertheless, high-performance computing (HPC) systems continue to grow in size, with increasing core counts, memory capacity, and power consumption. Rather than dedicating all available resources to a single problem instance, HPC systems can be leveraged to solve multiple optimization instances concurrently – a common requirement in applications such as stochastic optimization, policy design for sequential decision making, parameter tuning, and optimization-as-a-service. In this work, we study strategies for efficiently allocating solver jobs across compute nodes, exploring how to schedule multiple optimization jobs across a given number of cores or nodes. Using metrics from performance monitoring and benchmarking tools as well as metered PDUs, we analyze trade-offs between energy consumption and runtime, providing insights into how to balance computational efficiency and sustainability in large-scale optimization workflows.
Version
Published version
Access right on openHSU
Open access