AITuning: Machine Learning-based Tuning Tool for Run-Time Communication Libraries

TL;DR

This paper presents AITuning, a reinforcement learning-based tool that automatically tunes communication libraries like OpenCoarrays to optimize performance without human intervention, leveraging MPI-3 features.

Contribution

The work introduces a novel reinforcement learning approach for tuning communication libraries and demonstrates its application to OpenCoarrays using MPI-3 interfaces.

Findings

Reinforcement learning effectively tunes communication library parameters.

AITuning improves performance without manual tuning.

MPI-3 interfaces facilitate automated performance optimization.

Abstract

In this work, we address the problem of tuning communication libraries by using a deep reinforcement learning approach. Reinforcement learning is a machine learning technique incredibly effective in solving game-like situations. In fact, tuning a set of parameters in a communication library in order to get better performance in a parallel application can be expressed as a game: Find the right combination/path that provides the best reward. Even though AITuning has been designed to be utilized with different run-time libraries, we focused this work on applying it to the OpenCoarrays run-time communication library, built on top of MPI-3. This work not only shows the potential of using a reinforcement learning algorithm for tuning communication libraries, but also demonstrates how the MPI Tool Information Interface, introduced by the MPI-3 standard, can be used effectively by run-time libraries to improve the performance without human intervention.