A Comparative Study of OpenMP Scheduling Algorithm Selection Strategies
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915413939453952 |
|---|---|
| author | Korndörfer, Jonas H. Müller Mohammed, Ali Eleliemy, Ahmed Guilloteau, Quentin Krummenacher, Reto Ciorba, Florina M. |
| author_facet | Korndörfer, Jonas H. Müller Mohammed, Ali Eleliemy, Ahmed Guilloteau, Quentin Krummenacher, Reto Ciorba, Florina M. |
| contents | Scientific and data science applications are becoming increasingly complex, with growing computational and memory demands. Modern high performance computing (HPC) systems provide high parallelism and heterogeneity across nodes, devices, and cores. To achieve good performance, effective scheduling and load balancing techniques are essential. Parallel programming frameworks such as OpenMP now offer a variety of advanced scheduling algorithms to support diverse applications and platforms. This creates an instance of the scheduling algorithm selection problem, which involves identifying the most suitable algorithm for a given combination of workload and system characteristics.
In this work, we explore learning-based approaches for selecting scheduling algorithms in OpenMP. We propose and evaluate expert-based and reinforcement learning (RL)-based methods, and conduct a detailed performance analysis across six applications and three systems. Our results show that RL methods are capable of learning high-performing scheduling decisions, although they require significant exploration, with the choice of reward function playing a key role. Expert-based methods, in contrast, rely on prior knowledge and involve less exploration, though they may not always identify the optimal algorithm for a specific application-system pair. By combining expert knowledge with RL-based learning, we achieve improved performance and greater adaptability.
Overall, this work demonstrates that dynamic selection of scheduling algorithms during execution is both viable and beneficial for OpenMP applications. The approach can also be extended to MPI-based programs, enabling optimization of scheduling decisions across multiple levels of parallelism. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20312 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Comparative Study of OpenMP Scheduling Algorithm Selection Strategies Korndörfer, Jonas H. Müller Mohammed, Ali Eleliemy, Ahmed Guilloteau, Quentin Krummenacher, Reto Ciorba, Florina M. Distributed, Parallel, and Cluster Computing Artificial Intelligence Machine Learning Performance Scientific and data science applications are becoming increasingly complex, with growing computational and memory demands. Modern high performance computing (HPC) systems provide high parallelism and heterogeneity across nodes, devices, and cores. To achieve good performance, effective scheduling and load balancing techniques are essential. Parallel programming frameworks such as OpenMP now offer a variety of advanced scheduling algorithms to support diverse applications and platforms. This creates an instance of the scheduling algorithm selection problem, which involves identifying the most suitable algorithm for a given combination of workload and system characteristics. In this work, we explore learning-based approaches for selecting scheduling algorithms in OpenMP. We propose and evaluate expert-based and reinforcement learning (RL)-based methods, and conduct a detailed performance analysis across six applications and three systems. Our results show that RL methods are capable of learning high-performing scheduling decisions, although they require significant exploration, with the choice of reward function playing a key role. Expert-based methods, in contrast, rely on prior knowledge and involve less exploration, though they may not always identify the optimal algorithm for a specific application-system pair. By combining expert knowledge with RL-based learning, we achieve improved performance and greater adaptability. Overall, this work demonstrates that dynamic selection of scheduling algorithms during execution is both viable and beneficial for OpenMP applications. The approach can also be extended to MPI-based programs, enabling optimization of scheduling decisions across multiple levels of parallelism. |
| title | A Comparative Study of OpenMP Scheduling Algorithm Selection Strategies |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence Machine Learning Performance |
| url | https://arxiv.org/abs/2507.20312 |