Parallel tiled Nussinov RNA folding loop nest generated using both dependence graph transitive closure and loop skewing

Marek Palkowski,Wlodzimierz Bielecki

doi:10.1186/s12859-017-1707-8

Abstract

BackgroundRNA secondary structure prediction is a compute intensive task that lies at the core of several search algorithms in bioinformatics. Fortunately, the RNA folding approaches, such as the Nussinov base pair maximization, involve mathematical operations over affine control loops whose iteration space can be represented by the polyhedral model. Polyhedral compilation techniques have proven to be a powerful tool for optimization of dense array codes. However, classical affine loop nest transformations used with these techniques do not optimize effectively codes of dynamic programming of RNA structure predictions.ResultsThe purpose of this paper is to present a novel approach allowing for generation of a parallel tiled Nussinov RNA loop nest exposing significantly higher performance than that of known related code. This effect is achieved due to improving code locality and calculation parallelization. In order to improve code locality, we apply our previously published technique of automatic loop nest tiling to all the three loops of the Nussinov loop nest. This approach first forms original rectangular 3D tiles and then corrects them to establish their validity by means of applying the transitive closure of a dependence graph. To produce parallel code, we apply the loop skewing technique to a tiled Nussinov loop nest.ConclusionsThe technique is implemented as a part of the publicly available polyhedral source-to-source TRACO compiler. Generated code was run on modern Intel multi-core processors and coprocessors. We present the speed-up factor of generated Nussinov RNA parallel code and demonstrate that it is considerably faster than related codes in which only the two outer loops of the Nussinov loop nest are tiled.

Highlights

RNA secondary structure prediction is a compute intensive task that lies at the core of several search algorithms in bioinformatics
This paper focuses on the base pair maximization algorithm developed by Nussinov [1], which predicts RNA secondary structure in a computationally efficient way
Motivated by the deficiency of the mentioned techniques, we developed and present in this paper a novel approach for tiling and parallelization of the Nussinov loop nest

Summary

Introduction

RNA secondary structure prediction is a compute intensive task that lies at the core of several search algorithms in bioinformatics. The RNA folding approaches, such as the Nussinov base pair maximization, involve mathematical operations over affine control loops whose iteration space can be represented by the polyhedral model. Classical affine loop nest transformations used with these techniques do not optimize effectively codes of dynamic programming of RNA structure predictions. Algorithms to make predictions of the structure of single RNA molecules use empirical models to estimate the free energies of folded structures. This paper focuses on the base pair maximization algorithm developed by Nussinov [1], which predicts RNA secondary structure in a computationally efficient way. Xn, where xi is a nucleotide from the alphabet {G (guanine), A (adenine), U (uracil), C (cytosine)}, Nussinov’s algorithm solves the problem of RNA non-crossing secondary structure prediction by means of computing the maximum number of base pairs for subsequences xi, . Given an RNA sequence x1, x2, . . . , xn, where xi is a nucleotide from the alphabet {G (guanine), A (adenine), U (uracil), C (cytosine)}, Nussinov’s algorithm solves the problem of RNA non-crossing secondary structure prediction by means of computing the maximum number of base pairs for subsequences xi, . . . , xj, starting with subsequences of length 1 and building upwards, storing the result of each subsequence in a dynamic programming array

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