Accelerating Smith-Waterman Algorithm for Biological Database Search on CUDA-Compatible GPUs

This paper presents a fast method capable of accelerating the Smith-Waterman algorithm for biological database search on a cluster of graphics processing units (GPUs). Our method is implemented using compute unified device architecture (CUDA), which is available on the nVIDIA GPU. As compared with previous methods, our method has four major contributions. (1) The method efficiently uses on-chip shared memory to reduce the data amount being transferred between off-chip video memory and processing elements in the GPU. (2) It also reduces the number of data fetches by applying a data reuse technique to query and database sequences. (3) A pipelined method is also implemented to overlap GPU execution with database access. (4) Finally, a master/worker paradigm is employed to accelerate hundreds of database searches on a cluster system. In experiments, the peak performance on a GeForce GTX 280 card reaches 8.32 giga cell updates per second (GCUPS). We also find that our method reduces the amount of data fetches to 1/140, achieving approximately three times higher performance than a previous CUDA-based method. Our 32-node cluster version is approximately 28 times faster than a single GPU version. Furthermore, the effective performance reaches 75.6 giga instructions per second (GIPS) using 32 GeForce 8800 GTX cards.

[1]  Guang R. Gao,et al.  Implementation of the Smith-Waterman algorithm on a reconfigurable supercomputing platform , 2007, HPRCTA.

[2]  James Demmel,et al.  Benchmarking GPUs to tune dense linear algebra , 2008, HiPC 2008.

[3]  Dinesh Manocha,et al.  LU-GPU: Efficient Algorithms for Solving Dense Linear Systems on Graphics Hardware , 2005, ACM/IEEE SC 2005 Conference (SC'05).

[4]  Fumihiko Ino,et al.  Harnessing the Power of Idle GPUs for Acceleration of Biological Sequence Alignment , 2009, Parallel Process. Lett..

[5]  William R. Mark,et al.  Cg: a system for programming graphics hardware in a C-like language , 2003, ACM Trans. Graph..

[6]  Weikuan Yu,et al.  Performance Evaluation of FPGA-Based Biological Applications , 2007 .

[7]  Kevin Truong,et al.  160-fold acceleration of the Smith-Waterman algorithm using a field programmable gate array (FPGA) , 2007, BMC Bioinformatics.

[8]  Weiguo Liu,et al.  A Hybrid Computational Grid Architecture for Comparative Genomics , 2008, IEEE Transactions on Information Technology in Biomedicine.

[9]  Fumihiko Ino,et al.  RGBA packing for fast cone beam reconstruction on the GPU , 2009, Medical Imaging.

[10]  Jens H. Krüger,et al.  A Survey of General‐Purpose Computation on Graphics Hardware , 2007, Eurographics.

[11]  Rolf Apweiler,et al.  The SWISS-PROT protein sequence data bank and its supplement TrEMBL , 1997, Nucleic Acids Res..

[12]  David L. Wheeler,et al.  GenBank , 2015, Nucleic Acids Res..

[13]  Amitabh Varshney,et al.  High-throughput sequence alignment using Graphics Processing Units , 2007, BMC Bioinformatics.

[14]  Ali Akoglu,et al.  Sequence alignment with GPU: Performance and design challenges , 2009, 2009 IEEE International Symposium on Parallel & Distributed Processing.

[15]  Weiguo Liu,et al.  Streaming Algorithms for Biological Sequence Alignment on GPUs , 2007, IEEE Transactions on Parallel and Distributed Systems.

[16]  M S Waterman,et al.  Identification of common molecular subsequences. , 1981, Journal of molecular biology.

[17]  Giorgio Valle,et al.  CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment , 2008, BMC Bioinformatics.

[18]  Witold R. Rudnicki,et al.  An efficient implementation of Smith Waterman algorithm on GPU using CUDA, for massively parallel scanning of sequence databases , 2009, 2009 IEEE International Symposium on Parallel & Distributed Processing.

[19]  Michael Farrar,et al.  Sequence analysis Striped Smith – Waterman speeds database searches six times over other SIMD implementations , 2007 .

[20]  A. Klimovitski,et al.  Using SSE and SSE2 : Misconceptions and reality , 2001 .

[21]  W. Pearson.  Searching protein sequence libraries: comparison of the sensitivity and selectivity of the Smith-Waterman and FASTA algorithms. , 1991, Genomics.

[22]  Yongchao Liu,et al.  CUDASW++: optimizing Smith-Waterman sequence database searches for CUDA-enabled graphics processing units , 2009, BMC Research Notes.

[23]  Fumihiko Ino,et al.  Design and implementation of the Smith-Waterman algorithm on the CUDA-compatible GPU , 2008, 2008 8th IEEE International Conference on BioInformatics and BioEngineering.

[24]  E. Myers,et al.  Basic local alignment search tool. , 1990, Journal of molecular biology.