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Fine-grained parallel application specific computing for RNA secondary structure prediction using SCFGS on FPGA
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International Conference on Compilers, Architecture and Synthesis for Embedded Systems archive
Proceedings of the 2009 international conference on Compilers, architecture, and synthesis for embedded systems table of contents
Grenoble, France
SESSION: Architectural optimizations table of contents
Pages 107-116  
Year of Publication: 2009
ISBN:978-1-60558-626-7
Authors
Yong Dou  National Laboratory for Parallel & Distributed Processing, National University of Defence Technology, 410073 ChangSha, China
Fei Xia  National Laboratory for Parallel & Distributed Processing, National University of Defence Technology, 410073 ChangSha, China
Jingfei Jiang  National Laboratory for Parallel & Distributed Processing, National University of Defence Technology, 410073 ChangSha, China
Sponsors
SIGDA: ACM Special Interest Group on Design Automation
ACM: Association for Computing Machinery
SIGBED: ACM Special Interest Group on Embedded Systems
SIGMICRO: ACM Special Interest Group on Microarchitectural Research and Processing
Publisher
ACM  New York, NY, USA
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ABSTRACT

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.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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