Proceedings Article10.1109/ICCIS.2011.61
Accelerating Biological Sequence Alignment Algorithm on GPU with CUDA
Fang Zheng,Xianbin Xu,Yuanhua Yang,Shuibing He,Yuping Zhang +4 more
- 21 Oct 2011
- pp 18-21
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TL;DR: A multi-threaded parallel design and implementation of the Smith-Waterman (SW) on CUDA to reduce execution time and results show this m implementation achieves more better performance than the other parallel implementation on the Graphics Processing Unit.
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Abstract: In this paper, we have used Compute Unified Device Architecture (CUDA) GPU to accelerate pair wise sequence alignment using the Smith-Waterman (SW) algorithm Smith-Waterman(SW) is by far the best algorithm for its accuracy in similarity scoring But the executing time of this algorithm is too long in sequence alignment So we describe a multi-threaded parallel design and implementation of the Smith-Waterman (SW) on CUDA to reduce execution time And according the architecure of CUDA, we have divided the computation of a whole pair wise sequence alignment scoring matrix into multiple sub-matrices, using 32 threads to process on submatrice, more over we optimized memory distribution scheme, and used reduction to find the maximum element of the alignment scoring matrix We experiment the algorimthm on GeForce 9600 GT, connet to Windows xp 64-bit system The results show this mplementation achieves more better performance than the other parallel implementation on the Graphics Processing Unit
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Citations
A parallel algorithm for DNA sequences alignment based on MPI
Qianfei Xue,Jiang Xie,Junhui Shu,Huiran Zhang,Dongbo Dai,Xing Wu,Wu Zhang +6 more
- 26 Apr 2014
TL;DR: A new parallel algorithm based on FED algorithm for exact sequences alignment with MPI is proposed and the experimental results indicate that the proposed algorithm can report the matched positions in the specific sequence and improve the matching speed withMPI, as well as reduce the storage requirement.
4
•Dissertation
Bioinformatics Sequence Comparisons on Manycore Processors
Tuan Tu Tran
- 21 Dec 2012
TL;DR: The thesis proposes several solutions tailored for manycore processors such as today's GPUs, and presents MAROSE, a prototype parallel read mapper using these concepts, which is more efficient than the existing read mappers with a comparable sensitivity.
2
A Learning Approach to Introducing GPU Computing in Undergraduate Engineering Program
TL;DR: The graphics processing unit (GPU) learning initiative is developed within a project awarded by the Moroccan Fulbright Alumni Association, entitled “GPU Acceleration of Human Genome Sequencing”, and is conducted in collaboration with the High Performance Computing Lab at the George Washington University in U.S.
Accelerating Discrete Haar Wavelet Transform on GPU cluster
Selcuk Aslan,Hasan Badem,Dervis Karaboga,Alper Basturk,Tayyip Ozcan +4 more
- 01 Nov 2015
TL;DR: The wavelet transform was ported in a compute-efficient way to CPU cluster and programmable GPU cluster by utilizing MPI and CUDA respectively to achieve speedup of the GPU based transform.
2
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