Parallel tiled cache and energy efficient codes for [formula omitted] RNA folding algorithms

Abstract

In this paper, we consider two O(n4) RNA folding algorithms, Zuker’s recurrence and the maximum expected accuracy prediction (MEA), which are challenging dynamic programming tasks to optimize because they are computationally intensive and have a large number of non-uniform dependences. We apply our previously published approach to automatically tile and parallelize each loop in the studied algorithms by means of the polyhedral model. First, for each loop nest statement, rectangular tiles are formed within the iteration space of the loop nest. Then, those tiles are corrected to honor all dependences exposed for the original loop nest. Correction is based on applying the exact transitive closure of a dependence graph. We implemented our approach as a part of the source-to-source TRACO compiler, generated target code, and compare the performance and energy consumption of generated code with those of code obtained with the state-of-the-art PluTo compiler based on the affine transformation framework as well as with those of code generated by means of the manual cache-efficient Transpose method. Experiments were carried out on a modern multi-core processor to achieve the significant locality improvement and energy saving for generated code.