Towards Automatic Learning of Heuristics for Mechanical Transformations of Procedural Code

TL;DR

This paper proposes a machine learning approach combining reinforcement learning and classification to automatically learn heuristics for program transformations, aiming to simplify programming on heterogeneous supercomputing systems.

Contribution

It introduces a novel combination of reinforcement learning and classification to automatically learn heuristics for program transformations in heterogeneous systems.

Findings

Preliminary results show the approach effectively aids programmability.

The method demonstrates potential for automating transformation strategies.

The approach is suitable for complex heterogeneous system programming.

Abstract

The current trend in next-generation exascale systems goes towards integrating a wide range of specialized (co-)processors into traditional supercomputers. However, the integration of different specialized devices increases the degree of heterogeneity and the complexity in programming such type of systems. Due to the efficiency of heterogeneous systems in terms of Watt and FLOPS per surface unit, opening the access of heterogeneous platforms to a wider range of users is an important problem to be tackled. In order to bridge the gap between heterogeneous systems and programmers, in this paper we propose a machine learning-based approach to learn heuristics for defining transformation strategies of a program transformation system. Our approach proposes a novel combination of reinforcement learning and classification methods to efficiently tackle the problems inherent to this type of systems. Preliminary results demonstrate the suitability of the approach for easing the programmability of heterogeneous systems.