Fine-Grained Parallel RNA Secondary Structure Prediction Using SCFGs on FPGA

Abstract

In the field of RNA secondary structure prediction,CYK(Coche-Younger-Kasami) algorithm is one of the most popular methods using SCFG(stochastic context-free grammars) model.Accelerating SCFGs for large models and large RNA database searches becomes a challenge task in computational bioinformatics because of the O(L3) computational demands that are required.General purpose computers including parallel SMP multiprocessors or cluster systems exhibit low parallel efficiency.Furthermore,large scaled parallel computers are too expensive to be used easily for many research institutes.FPGA chips provide a new approach to accelerate CYK algorithm by exploiting fine-grained custom design.CYK algorithm shows complicated data dependence,in which the dependence distance is variable,and the dependence direction is also across two dimensions.This paper proposes a systolic-like array structure including one master PE(Processing Element) and multiple slave PEs for fine-grained hardware implementation on FPGA.By columns and assign,tasks are partitioned to PEs for load balance.Data reuse schemes reduce the need to load energy matrices from external memory.The experimental results show a factor of more than 14 speedup over the Infernal-1.0 software for 959-residue RNA sequence and a CM model with 3145 states running on a PC platform with Intel Dual-Core 2.5GHz CPU.The computational power of the accelerator is comparable to a PC cluster consisting of 20 Intel-Xeon CPUs for RNA secondary structure prediction using SCFGs,but the hardware cost and power consumption is only about 20% and 10% that of the latter respectively.