A CYK approach to parsing in parallel: a case study
SIGCSE '91 Proceedings of the twenty-second SIGCSE technical symposium on Computer science education
An FPGA-Based Syntactic Parser for Real-Life Almost Unrestricted Context-Free Grammars
FPL '01 Proceedings of the 11th International Conference on Field-Programmable Logic and Applications
An FPGA-Based Coprocessor for the Parsing of Context-Free Grammars
FCCM '00 Proceedings of the 2000 IEEE Symposium on Field-Programmable Custom Computing Machines
Concurrency and Computation: Practice & Experience - Third IEEE International Workshop on High Performance Computational Biology (HiCOMB 2004)
Accelerating Nussinov RNA secondary structure prediction with systolic arrays on FPGAs
ASAP '08 Proceedings of the 2008 International Conference on Application-Specific Systems, Architectures and Processors
Bioinformatics
CASES '09 Proceedings of the 2009 international conference on Compilers, architecture, and synthesis for embedded systems
Hardware-Accelerated RNA Secondary-Structure Alignment
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
An experimental study of optimizing bioinformatics applications
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Exploiting parallelization for RNA secondary structure prediction in cluster
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
Accelerating ncRNA homology search with FPGAs
Proceedings of the ACM/SIGDA international symposium on Field programmable gate arrays
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In the field of RNA secondary structure prediction, the CYK (Coche-Younger-Kasami) algorithm is one of the most popular methods using a SCFG (stochastic context-free grammar) model. Accelerating SCFGs for large models and large RNA database searching becomes a challenging task in computational bioinformatics because the parallel efficiency of general purpose computer systems is limited by the O (L^3) computational complexity and by complicated data dependences. Furthermore, large scale parallel computers are too expensive to be easily accessible to many research institutes. Recently, FPGA chips have emerged as one promising application accelerator to accelerate the CYK algorithm by exploiting a fine-grained custom design. We propose a systolic-like array structure including one master PE and multiple slave PEs for the fine-grained hardware implementation on FPGA to accelerate the CYK/inside algorithm with Query-Dependent Banding (QDB) heuristics. We partition the tasks by columns and assign them to PEs for load balance. We exploit data reuse schemes to reduce the need to load matrices from external memory. The experimental results show a speedup factor of more than 14x over the Infernal-1.0 with QDB optimization for the alignment of a single long RNA sequence to a large CM model with thousands of states running on a PC platform with Intel Dual-core 2.5GHz CPU. The computational power of our accelerator is comparable to that of a PC cluster consisting of 16 Intel-Xeon 2.0GHz Quad CPUs for large-scale database alignment applications (cmsearch) with multiple input sequences, but the power consumption is only about 10% of that of the cluster.