# A Comparative Study of OpenMP Scheduling Algorithm Selection Strategies

arXiv: https://arxiv.org/abs/2507.20312 · HTML: https://mlsanity.com/paper/2507.20312/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 54 references that are in the map.
- First posted on arXiv 27 Jul 2025.

## Builds on (most cited first, 9 of 9)

- [Imagination-Augmented Agents for Deep Reinforcement Learning](https://mlsanity.com/paper/1707.06203/) (2017)
- [Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation](https://mlsanity.com/paper/1802.04325/) (2018)
- [The GAP Benchmark Suite](https://mlsanity.com/paper/1508.03619/) (2015)
- [The Role of Idle Waves, Desynchronization, and Bottleneck Evasion in the Performance of Parallel Programs](https://mlsanity.com/paper/2205.04190/) (2022)
- [OpenMP Loop Scheduling Revisited: Making a Case for More Schedules](https://mlsanity.com/paper/1809.03188/) (2018)
- [SimAS: A Simulation-assisted Approach for the Scheduling Algorithm Selection under Perturbations](https://mlsanity.com/paper/1912.02050/) (2019)
- [LB4OMP: A Dynamic Load Balancing Library for Multithreaded Applications](https://mlsanity.com/paper/2106.05108/) (2021)
- [Auto-Tuning for OpenMP Dynamic Scheduling applied to Full Waveform Inversion](https://mlsanity.com/paper/2402.16728/) (2024)
- [Algorithm Selection in Short-Range Molecular Dynamics Simulations](https://mlsanity.com/paper/2505.03438/) (2025)

## Abstract (arXiv)

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.