TY - GEN
T1 - Efficient pairwise statistical significance estimation for local sequence alignment using GPU
AU - Zhang, Yuhong
AU - Misra, Sanchit
AU - Honbo, Daniel
AU - Agrawal, Ankit
AU - Liao, Wei-Keng
AU - Choudhary, Alok Nidhi
PY - 2011/4/14
Y1 - 2011/4/14
N2 - Pairwise statistical significance has been found to be quite accurate in identifying related sequences (homologs), which is a key step in numerous bioinformatics applications. However, it is computational and data intensive, particularly for a large amount of sequence data. To prevent it from becoming a performance bottleneck, we resort to Graphics Processing Units (GPUs) for accelerating the computation. In this paper, we present a GPU memory-access optimized implementation for a pairwise statistical significance estimation algorithm. By exploring the algorithm's data access characteristics, we developed a tile-based scheme that can produce a contiguous memory accesses pattern to GPU global memory and sustain a large number of threads to achieve a high GPU occupancy. Our experimental results present both single- and multi-pair statistical significance estimations. The performance evaluation was carried out on an NVIDIA Telsa C2050 GPU. We observe more than 180× end-to-end speedup over the CPU implementation on an Intel
AB - Pairwise statistical significance has been found to be quite accurate in identifying related sequences (homologs), which is a key step in numerous bioinformatics applications. However, it is computational and data intensive, particularly for a large amount of sequence data. To prevent it from becoming a performance bottleneck, we resort to Graphics Processing Units (GPUs) for accelerating the computation. In this paper, we present a GPU memory-access optimized implementation for a pairwise statistical significance estimation algorithm. By exploring the algorithm's data access characteristics, we developed a tile-based scheme that can produce a contiguous memory accesses pattern to GPU global memory and sustain a large number of threads to achieve a high GPU occupancy. Our experimental results present both single- and multi-pair statistical significance estimations. The performance evaluation was carried out on an NVIDIA Telsa C2050 GPU. We observe more than 180× end-to-end speedup over the CPU implementation on an Intel
KW - GPU
KW - Pairwise sequence alignment
KW - Statistical significance
UR - http://www.scopus.com/inward/record.url?scp=79953827249&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79953827249&partnerID=8YFLogxK
U2 - 10.1109/ICCABS.2011.5729885
DO - 10.1109/ICCABS.2011.5729885
M3 - Conference contribution
AN - SCOPUS:79953827249
SN - 9781612848525
T3 - 2011 IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2011
SP - 226
EP - 231
BT - 2011 IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2011
T2 - 1st IEEE International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2011
Y2 - 3 February 2011 through 5 February 2011
ER -