Title: Towards Automatic Learning of Heuristics for Mechanical Transformations of Procedural Code,
Abstract: The current trends in next-generation exascale systems go towards integrating
a wide range of specialized (co-)processors into traditional supercomputers.
Due to the efficiency of heterogeneous systems in terms of Watts and FLOPS per
surface unit, opening the access of heterogeneous platforms to a wider range of
users is an important problem to be tackled. However, heterogeneous platforms
limit the portability of the applications and increase development complexity
due to the programming skills required. Program transformation can help make
programming heterogeneous systems easier by defining a step-wise transformation
process that translates a given initial code into a semantically equivalent
final code, but adapted to a specific platform. Program transformation systems
require the definition of efficient transformation strategies to tackle the
combinatorial problem that emerges due to the large set of transformations
applicable at each step of the process. In this paper we propose a machine
learning-based approach to learn heuristics to define program transformation
strategies. 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 this
approach.