GPU 3 Smith-Waterman

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129 211 11 GPU 3 Smith-Waterman Saori SUDO 1 GPU Graphic Processing Unit GPU GPGPUGeneral-purpose computing on GPU 1) CPU GPU GPU GPGPU NVIDIA C CUDACompute Unified Device Architecture 2) OpenCL 3)

1 GPU 3 Smith-Waterman Saori SUDO 1 GPU Graphic Processing Unit GPU GPGPUGeneral-purpose computing on GPU 1) CPU GPU GPU GPGPU NVIDIA C CUDACompute Unified Device Architecture 2) OpenCL 3) DNA 4) SWSmith-Waterman 5) 6) 7) 8) SW 9) SW 1 SW 2 SW GPU SW GPU 3 SW GPU Fig.1 CPU GPU (pair-wise sequence alignment) 3 multiple sequence alignment global sequence alignment local sequence alignment SW 2.2 SWSmith-Waterman SW 2 2 X Y 2 SW 3 SW 1

2 matchmismatchgap match 2 mismatch gap 2 mn SW O(mn) 2.3 SWSmith-Waterman SW match = 1mismatch = -1gap = -1 m X = x 1 x 2...x m n Y = y 1 y 2...y n SW Step1 XY Step2 i j SW (i, j) k m l n SW (k, l) = SW (k, ) = SW (, l) = Step3 1 Step4 Fig.2 Fig.3 SW SW (i, j) 8 j SW (i 1, j 1) + match if(xi = y j ) >< SW (i 1, j 1) + mismatch else = max SW (i 1, j) + gap >: SW (i, j 1) + gap (1) Fig.4 ACAC AGCA SW 4 5 (i, j) = (4, 3) (i, j) = (4, 3), (i, j) = (3, 2), (i, j) = (2, 1) ACAC AGCA CA 3 CUDA 3.1 CUDA NVIDIA GPU CUDA CUDA GPU CUDA C GPU CUDA kernel Fig.5 kernel 3.2 GPU gridblockthread 3 thread block block grid 1 grid 1 GPU GPU Streaming Multi Processor( SM) 1 SM 1 block SM Streaming Processor( 2

3 SP) 1 SP 1 thread thread warp 1warp 32 thread thread 32 warp thread GPU warp divergent 4 SW BLAST(Basic Local Alignment Search Tool) 1) FASTA(FAST-ALL) 4) DP 3 11) 3 CLUSTALW 12) MAFFT 13) MAVID 14) T-COFFEE 15) SW n GPU 16) 17) CUDAlign 5) CUDASW++2. 6) SW GPU SW SW 5 3 SW SW SW 3 SW Fig.6 3DSW SW m X = x 1 x 2...x m n Y = y 1 y 2...y n l Z = z 1 z 2...z l 3 SW Step1 Step2 Step3 Step4 XY Z xyz i, j, k SW (i, j, k) p m q n r l SW (p, q, r) = SW (p,, ) = SW (, q, ) = SW (,, r) = SW (i, j, k) 8 j SW (i 1, j 1, k 1) + match if(xi = y j = z k ) SW (i 1, j 1, k 1) + mismatch else SW (i 1, j 1, k) + gap >< SW (i, j 1, k 1) + gap = max SW (i 1, j, k 1) + gap (2) SW (i 1, j, k) + gap SW (i, j 1, k) + gap >: SW (i, j, k 1) + gap 6 (i, j, k) = (3, 3, 3) x 3 =Ty 3 = Tz 3 =T 3 3 SW (3, 3, 3) 2 SW (2, 2, 2) = SW (3, 3, 3) = SW GPU 3 GPU 3 SW GPU-3DSWT GPU CPU 3 SW GPU-3DSW 3

4 Fig.7 Fig.8 CPU 3 SW CPU-3DSW 6.1 GPU 3 SW SW CUDA 3 SW GPU 6.2 GPU 9 CUDA thread 1 thread 1 step=3 7 block thread block 11 block x y step 6.3 GPU SW GPU CPU SW 1 SW 3 Fig.9 Fig.1 GPU-3DSW thread Table1 1 2 CPU Intel Xeon W GHz Intel Core i GHz GPU Tesla C25 GeForce GTX 46 Memory 6GB 8GB OS Debian 5..6 Ubuntu 11.4 CUDA 3.1 CUDA 3.2 -O3 -O3 GPU DNA 3 SW CPU-3DSWGPU-3DSWGPU-3DSWT 4

5 me[msec] 1E+4 1E+3 1E+2 1E+1 1E+ CPU-3DSW GPU-3DSW GPU-3DSWT CPU- 3DSW/GPU- 3DSWT Fig.11 GPU-3DSW block 1E E-2 1E me[msec] 1E+3 1E+2 1E+1 1E+ 1E-1 1E-2 1E-3 Fig.12 CPU-3DSW GPU-3DSW GPU-3DSWT CPU- 3DSW/GPU- 3DSWT length of strings 1 CPU-3DSW GPU-3DSWT CPU CPU.8 GPU-3DSW GPU-3DSWT GPU-3DSWT GPU warp divergent 8 3 SW CUDA 3 SW GPU CPU 3 SW 1.5 kernel kernel 3 n SW n SW SW E length of strings Fig ) John D. Owens, David Luebke, Naga Govindaraju, Mark Harris, Jens Kruger, Aaron Lefohn, and Timothy J. Purcell. A Survey of General-Purpose Computation on Graphics Hardware. In Eurographics 25, State of the Art Reports, pp , August 25. 2) NVIDIA. Compute Unified Device Architecture Programming Guide ) John E Stone, David Gohara, and Guochun Shi. OpenCL: A parallel Programming Standard for Heterogenous Computing Systems. Computing in Science Engineering. 4) David W Mount. 2., 25. 5) Edans Flavius de O Sandes and Alba Cristina M A de Melo. Smith-Waterman Alignment of Huge Sequences with GPU in Linear Space. 211 IEEE International Parallel & Distributed Processing Symposium, Vol. 25, pp , May ) Yongchao Liu, Bertil Schmidt, and Douglas L Maskell. CUDASW++2.: enhanced Smith- Waterman protein database search on CUDAenabled GPUs based on SIMT and virtualized SIMD abstractions. BMC Research Notes, Vol. 3, No. 1, pp. 1 12, April 21. 5

6 7),. ().. A,, Vol. 88, No. 8, pp , ),,,,. 2 DTW.. MPS,, Vol. 21, No. 24, pp. 1 6, ) Tomoyuki Hiroyasu, Takuma Nishii, Masato Yoshimi, Mitsunori Miki, and Hisatake Yokouchi. The proposal of optical topograhy analizing sysytem and evaluation., August ) NCBI. BLAST Basic Local Alignment Search Tool ) Yongchao Liu, Bertil Schmidt, and Douglas L. Maskell. MSA-CUDA: Multiple Sequence Alignment on Graphics Processing Units with CUDA. Application-Specific Systems, Architectures and Processors, IEEE International Conference on, Vol., pp , ) Julie D Thompson, Desmond G Higgins, and Toby J Gibson. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Research, Vol. 22, No. 22, pp , April ) Kazutaka Katoh, Kazuharu Misawa, Keiichi Kuma, and Takashi Miyata. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Research, Vol. 3, No. 14, pp , July ) Nicolas Bray and Lior Pachter. MAVID: constrained ancestral alignment of multiple sequences. Genome Research, Vol. 14, No. 4, pp , April ) C Notredame, Desmond G. Higgins, and Jaap Heringa. T-Coffee: A Novel Method for Fast and Accurate Multiple Sequence Alignment. Journal of Molecular Biology, Vol. 14, No. 4, pp , April ),,,,. GPU Smith-Waterman. SACSIS21, Vol. 21, No. 5, pp , April ),,. CUDA GPU., Vol. 114, No. 19, pp , March 28. 6

Keywords -Bioinformatics, sequence alignment, Smith- waterman (SW) algorithm, GPU, CUDA

Keywords -Bioinformatics, sequence alignment, Smith- waterman (SW) algorithm, GPU, CUDA Volume 5, Issue 5, May 2015 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Accelerating Smith-Waterman