Presentation
Learning To Select Scheduling Algorithms in OpenMP
DescriptionScientific and data science applications demand increasing computational performance, requiring effective scheduling and load balancing on high performance computing (HPC) systems. While OpenMP libraries such as LB4OMP provide several scheduling algorithms, selecting the best one for a given application-system pair remains an open challenge. This work addresses the scheduling algorithm selection problem by investigating automated approaches that can adapt to diverse workloads and architectures.
We propose and evaluate two automated selection strategies: expert- and reinforcement learning-based (RL). We use six applications and three systems to conduct the performance evaluation, revealing trade-offs between exploration overhead and optimality of selection of the methods. We further demonstrate that combining expert knowledge with RL improves overall performance.
With the poster we will present the methodology, results, and insights of expert- versus RL-based approaches. We highlight implications for future heterogeneous and multi-level systems and advertising the open source library (LB4OMP) where the methods were implemented.
We propose and evaluate two automated selection strategies: expert- and reinforcement learning-based (RL). We use six applications and three systems to conduct the performance evaluation, revealing trade-offs between exploration overhead and optimality of selection of the methods. We further demonstrate that combining expert knowledge with RL improves overall performance.
With the poster we will present the methodology, results, and insights of expert- versus RL-based approaches. We highlight implications for future heterogeneous and multi-level systems and advertising the open source library (LB4OMP) where the methods were implemented.
Event Type
Research and ACM SRC Posters
TimeTuesday, 18 November 20258:00am - 5:00pm CST
LocationSecond Floor Atrium
Archive
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