Hardware Acceleration Of Multi-Deme Genetic Algorithm for DNA Codeword Searching

Abstract : A large and reliable DNA codeword library is key to the success of DNA based computing. Searching for sets of reliable DNA codewords is an NP-hard problem which can take days on state-of-art high performance cluster computers. This work presents a hybrid architecture that consists of a general purpose microprocessor and a hardware accelerator for accelerating the multi-deme genetic algorithm (GA) for the application of DNA codeword searching. The presented architecture provides more than 1000X speed-up compared to a software only implementation. A code extender that uses exhaustive search to produce locally optimum codes in about 1.5 hours for the case of length 16 codes is also described. The experimental results demonstrate that the GA can find 99% of the words in locally optimum libraries. Finally, we investigate the performance impact of migration, mating and mutation functions in the hardware accelerator. The analysis shows that a modified GA without mating is the most effective for DNA codeword searching.

[1]  Konstantinos G. Margaritis,et al.  New Processor Array Architectures for the Longest Common Subsequence Problem , 2005, The Journal of Supercomputing.

[2]  Navin Kashyap,et al.  On the Design of Codes for DNA Computing , 2005, WCC.

[3]  J. SantaLucia,et al.  A unified view of polymer, dumbbell, and oligonucleotide DNA nearest-neighbor thermodynamics. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[4]  D. Ashlock,et al.  Bounds on Optimal Edit Metric Codes , 2005 .

[5]  Byoung-Tak Zhang,et al.  Multiobjective evolutionary optimization of DNA sequences for reliable DNA computing , 2005, IEEE Transactions on Evolutionary Computation.

[6]  Péter L. Erdös,et al.  Exordium for DNA Codes , 2003, J. Comb. Optim..

[7]  Fumiaki Tanaka,et al.  Design of nucleic acid sequences for DNA computing based on a thermodynamic approach , 2005, Nucleic acids research.

[8]  Anne Condon,et al.  Strand design for biomolecular computation , 2002, Theor. Comput. Sci..

[9]  R. Brualdi,et al.  Greedy Codes , 1993, Proceedings. IEEE International Symposium on Information Theory.

[10]  L M Adleman,et al.  Molecular computation of solutions to combinatorial problems. , 1994, Science.

[11]  Yen-Chun Lin,et al.  A Scalable and Efficient Systolic Algorithm for the Longest Common Subsequence Problem , 2002, J. Inf. Sci. Eng..

[12]  Anne Condon,et al.  Stochastic Local Search Algorithms for DNA Word Design , 2002, DNA.