title: Fine-grained parallelism accelerating for RNA secondary structure prediction with pseudoknots based on FPGA.
abstract: PKNOTS is a most famous benchmark program and has been widely used to predict RNA secondary structure including pseudoknots. It adopts the standard four-dimensional (4D) dynamic programming (DP) method and is the basis of many variants and improved algorithms. Unfortunately, the O(N(6)) computing requirements and complicated data dependency greatly limits the usefulness of PKNOTS package with the explosion in gene database size. In this paper, we present a fine-grained parallel PKNOTS package and prototype system for accelerating RNA folding application based on FPGA chip. We adopted a series of storage optimization strategies to resolve the "Memory Wall" problem. We aggressively exploit parallel computing strategies to improve computational efficiency. We also propose several methods that collectively reduce the storage requirements for FPGA on-chip memory. To the best of our knowledge, our design is the first FPGA implementation for accelerating 4D DP problem for RNA folding application including pseudoknots. The experimental results show a factor of more than 50x average speedup over the PKNOTS-1.08 software running on a PC platform with Intel Core2 Q9400 Quad CPU for input RNA sequences. However, the power consumption of our FPGA accelerator is only about 50% of the general-purpose micro-processors.
authors: Electronic Engineering College, Naval University of Engineering, Wuhan 430033, China.
authors: Fei
authors: F
authors: Xia
authors: Guoqing
authors: G
authors: Jin
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D003198:Computer Simulation; D009690:Nucleic Acid Conformation; D012313:RNA / Q000737:chemistry*; D059370:RNA Folding; D012984:Software
chemical_list: D012313:RNA
keywords: Bioinformatics; RNA secondary structure; dynamic programming; fine-grained parallel algorithm; pseudoknots