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 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
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
Fine-grained parallel RNA secondary structure prediction using SCFGs on FPGA
Parallel Computing
| Hi-index | 0.00 |
In the field of RNA secondary structure prediction, the CYK (Coche-Younger-Kasami) algorithm is a most popular methods using SCFG (stochastic context-free grammars) model. However, general purpose parallel computers including SMP multiprocessors or cluster systems exhibit low parallel efficiency and they are too expensive to be used easily for many research institutes. FPGA chips provide a new approach to accelerate the CYK algorithm by exploiting fine-grained custom design. The CYK algorithm shows complicated data dependence, in which the dependence distance is variable, and the dependence direction is also across two dimensions. We propose a systolic array structure including one master PE and multiple slave PEs for fine grain hardware implementation on FPGA. We partition tasks by columns and assign tasks to PEs for load balance. We exploit data reuse schemes to reduce the need to load matrix from external memory. To our knowledge, our implementation with 16 PEs is the only FPGA accelerator implementing the complete CYK/inside algorithm. The experimental results show a factor of more than 14 speedup over the Infernal-0.55 software running on a PC platform with Pentium 4 2.66GHz CPU. The computational power of our platform with FPGA 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 15% and 10% of the latter respectively.