Automatic source code transformation for GPUs based on program comprehension

Abstract

In this work is presented a technique to transform sequential source code to execute it on parallel architectures as heterogeneous many-core systems or GPUs. Source code is parsed and basic algorithmic concepts are discovered from it in order to feed a knowledge base. A reasoner, by consulting algorithmic rules, can compose this basic concepts to pinpoint code regions representing a known algorithm. This code can be annotated and / or transformed with a source-to-source process. A prototype tool has been built and tested on a case study to analyse the source code of a matrix multiplication. After recognition of the algorithm, the code is modified with calls to nVIDIA GPU cuBLAS library. © 2012 Springer-Verlag Berlin Heidelberg.