Fine-grained parallelism accelerating for RNA secondary structure prediction with pseudoknots based on FPGA

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 re...

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Veröffentlicht in:Journal of bioinformatics and computational biology Jg. 12; H. 3; S. 1450008
Hauptverfasser: Xia, Fei, Jin, Guoqing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore 01.06.2014
Schlagworte:
Algorithms
Computational Biology
Computer Simulation
Nucleic Acid Conformation
RNA - chemistry
RNA Folding
Software
dynamic programming
pseudoknots
fine-grained parallel algorithm
Bioinformatics
RNA secondary structure
ISSN:1757-6334, 1757-6334
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Zusammenfassung: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.
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ISSN:1757-6334
1757-6334
DOI:10.1142/S0219720014500085