Efficient dynamic pinning of parallelized applications by reinforcement learning with applications

• Georgios Chasparis
• Michael Roßbory
• Vladimir Janjic

• Francisco F. Rivera
• Tomás F. Pena
• José C. Cabaleiro

This paper introduces a resource allocation framework specifically tailored for addressing the problem of dynamic placement (or pinning) of parallelized applications to processing units. Decisions are updated recursively for each thread by a resource manager/scheduler which runs in parallel to the application’s threads and periodically records their performances and assigns to them new CPU affinities. For updating the CPU-affinities, the scheduler uses a reinforcement-learning algorithm, each branch of which is responsible for assigning a new placement strategy to each thread. The proposed resource allocation framework is flexible enough to address alternative optimization criteria, such as maximum average processing speed and minimum speed variance among threads. We demonstrate the response of the dynamic scheduler under fixed and varying availability of resources (e.g., when other applications running on the same platform) in a parallel implementation of the Ant-Colony Optimization.