GPU Accelerated RNA Folding Algorithm

Abstract

Abstract. Many bioinformatics studies require the analysis of RNA or DNA structures. More specifically, extensive work is done to elaborate efficient algorithms able to predict the 2-D folding structures of RNA or DNA sequences. However, the high computational complexity of the algorithms, combined with the rapid increase of genomic data, triggers the need of faster methods. Current approaches focus on parallelizing these algorithms on multiprocessor systems or on clusters, yielding to good performance but at a relatively high cost. Here, we explore the use of computer graphics hardware to speed up these algorithms which, theoretically, provide both high performance and low cost. We use the CUDA programming language to harness the power of NVIDIA graphic cards for general computation with a C-like environment. Performances on recent graphic cards achieve a ×17 speed-up.

Dynamic Detection of Uniform and Affine Vectors in GPGPU Computations

“…RNAFold GPU is a CUDA program which performs RNA folding. Based on dynamic programming, it achieves a 17-time speedup compared to a multicore implementation [8].…”

Reducing the worst case running times of a family of RNA and CFG problems, using Valiant’s approach

“…However, practical, hardware-based fast computations of matrix multiplications are gaining popularity within recent years [40,41], due the highly parallelized nature of such computations and the availability of new technologies that exploit this parallelism. Such technologies were previously used for some related problems [51,52], yet there is an intrinsic advantage for its utilization via the VMT framework. While optimizing the code for each specific problem and each specific hardware requires special expertise, the VMT framework conveniently allows differing the bottleneck part of the computation to the execution of matrix multiplication subroutines, and thus off-the-shelf, hardware tailored optimized solutions can be easily integrated into all VMT problems, instead of being developed separately for each problem.…”

Rapid RNA Folding: Analysis and Acceleration of the Zuker Recurrence

“…Rizk and Lavenier [7] used an NVIDIA GTX280 GPU to search for microRNAs of length 120 bases, accelerating Zuker 17-fold versus one core of a Xeon 2.66 GHz workstation. This GPU has 30 multiprocessors, each a SIMD unit of eight 32-bit processors.…”

“…Folding algorithms require time at least cubic in the length of the sequence, so high-throughput folding is a major computational challenge. Consequently, researchers have parallelized folding algorithms using both multi-core generalpurpose processors [5] and specialized architectures on Field-Programmable Gate Arrays (FPGAs) [6] and Graphics Processing Units (GPUs) [7].…”