Automatic code optimization for computing the McCaskill partition functions

Abstract

In this paper, we present the application of three automatic source-to-source compilers to code implementing McCaskill’s bioinformatics algorithm. It computes probabilities of various substructures for RNA prediction. McCaskill’s algorithm is compute and data intensive and it is within dynamic programming. A corresponding programming code exposes non-uniform dependences that complicate tiling of that code. The corresponding code is represented within the polyhedral model. Its optimization is still a challenging task for optimizing compilers employing multi-threaded loop tiling. To generate optimized code, we used the popular PLuTo compiler that finds and applies affine transformations, the TRACO compiler based on calculating the transitive closure of loop dependence graphs, and the newest polyhedral tool DAPT implementing space-time tiling. An experimental study fulfilled on two multi-core machines: an AMD Epyc with 64 threads and a 2x Intel Xeon Platinum 9242 with 192 threads demonstrates considerable speedup, high locality, and scalability for various problem sizes and the number of threads of generated codes by means of space-time tiling.