1465. Balancing Energy and Performance: Efficient Allocation of Solver Jobs on High-Performance Computing Systems
Invited abstract in session WC-43: Deployment & Execution, stream Software for Optimization.
Wednesday, 12:30-14:00Room: Newlyn GR.07
Authors (first author is the speaker)
| 1. | Willi Leinen
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| High Performance Computing, Helmut Schmidt University | |
| 2. | Andreas Fink
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| Chair of Information Systems, Helmut-Schmidt-University | |
| 3. | Philipp Neumann
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| IT, High Performance Computing und Data Science, Deutsches Elektronen-Synchrotron (DESY), University of Hamburg |
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.
Keywords
- Parallel Algorithms and Implementation
- Software
- Large Scale Optimization
Status: accepted
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