CUDAlign: using GPU to accelerate the comparison of megabase genomic sequences
Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
Acceleration of the Smith-Waterman algorithm using single and multiple graphics processors
Journal of Computational Physics
GPU parallelization of algebraic dynamic programming
PPAM'09 Proceedings of the 8th international conference on Parallel processing and applied mathematics: Part II
High performance technique for database applications using a hybrid GPU/CPU platform
Proceedings of the 21st edition of the great lakes symposium on Great lakes symposium on VLSI
FPGA-based smith-waterman algorithm: analysis and novel design
ARC'11 Proceedings of the 7th international conference on Reconfigurable computing: architectures, tools and applications
Parametrizing multicore architectures for multiple sequence alignment
Proceedings of the 8th ACM International Conference on Computing Frontiers
Parallel models for sequence alignment on CPU and GPU
Proceedings of the 12th International Conference on Computer Systems and Technologies
Microprocessors & Microsystems
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Direct approaches to exploit many-core architecture in bioinformatics
Future Generation Computer Systems
Performance comparison of GPU programming frameworks with the striped Smith-Waterman algorithm
ACM SIGARCH Computer Architecture News - ACM SIGARCH Computer Architecture News/HEART '12
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Parallelizing dynamic programming through rank convergence
Proceedings of the 19th ACM SIGPLAN symposium on Principles and practice of parallel programming
Fine-grained parallel implementations for SWAMP+ Smith-Waterman alignment
Parallel Computing
International Journal of Computational Science and Engineering
Hi-index | 0.00 |
The Smith Waterman algorithm for sequence alignment is one of the main tools of bioinformatics. It is used for sequence similarity searches and alignment of similar sequences. The high end Graphical Processing Unit (GPU), used for processing graphics on desktop computers, deliver computational capabilities exceeding those of CPUs by an order of magnitude. Recently these capabilities became accessible for general purpose computations thanks to CUDA programming environment on Nvidia GPUs and ATI Stream Computing environment on ATI GPUs. Here we present an efficient implementation of the Smith Waterman algorithm on the Nvidia GPU. The algorithm achieves more than 3.5 times higher per core performance than previously published implementation of the Smith Waterman algorithm on GPU, reaching more than 70% of theoretical hardware performance. The differences between current and earlier approaches are described showing the example for writing efficient code on GPU.