Proceedings Article10.1109/CGC.2012.98
Implementing Smith-Waterman Algorithm with Two-Dimensional Cache on GPUs
Xiaowen Feng,Hai Jin,Ran Zheng,Zhiyuan Shao,Lei Zhu +4 more
- 01 Nov 2012
- pp 25-30
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TL;DR: A new method to implement Smith-Waterman algorithm with two-dimensional cache is proposed, which aims at accelerating the first stage of Smith- waterman algorithm and coalesced writing back the corresponding results to GPU global memory.
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Abstract: Finding regions of similarity between two data streams is a computational intensive and memory consuming problem, which refers to as sequence alignment for biological sequence. Smith-Waterman algorithm is an optimal method to find the local sequence alignment. It requires a large amount of computation and memory, and is also constrained by the memory access speed when accelerated by using Graphics Processing Units (GPUs). A new method to implement Smith-Waterman algorithm with two-dimensional cache is proposed, which aims at accelerating the first stage of Smith-Waterman algorithm and coalesced writing back the corresponding results to GPU global memory. Our proposal is implemented over NVIDIA Geforce GTX295 GPU, and compared with CUDASW++ 2.0. Experimental results show that our approach outperforms CUDASW++ 2.0 in the datasets chosen from NCBI.
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Citations
Optimization Techniques for GPU Programming
TL;DR: In this article , a survey discusses various optimization techniques found in 450 articles published in the last 14 years and analyzes the optimizations from different perspectives which shows that the various optimizations are highly interrelated, explaining the need for techniques such as auto-tuning.
54
•Journal Article
GPU-Clustalw : Using graphics hardware to accelerate multiple-sequence alignment
TL;DR: In this article, the authors presented a new approach to reduce the computational complexity of ClustalW by using graphics processing units (GPUs) to accelerate the computationally expensive part of the algorithm.
53
Efficient Distributed Smith-Waterman Algorithm Based on Apache Spark
Bo Xu,Changlong Li,Hang Zhuang,Jiali Wang,Qingfeng Wang,Xuehai Zhou +5 more
- 25 Jun 2017
TL;DR: CloudSW is presented, an efficient distributed Smith-Waterman algorithm which leverages Apache Spark and SIMD instructions to accelerate the algorithm and which has excellent scalability and achieves up to 529 giga cell updates per second in protein database search with 50 nodes in Aliyun Cloud.
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A novel structure of the Smith-Waterman Algorithm for efficient sequence alignment
Saad Khan Zahid,Laiq Hasan,Asif Ali Khan,Salini Ullah +3 more
- 05 Mar 2015
TL;DR: A novel structure of the Smith Waterman algorithm is presented that takes less number of cycles at the cost of utilizing a minimal amount of extra hardware resources as compared to its existing form, and achieves up to 25% performance gain.
13
References
Basic Local Alignment Search Tool
TL;DR: A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score.
98.8K
Improved tools for biological sequence comparison.
TL;DR: Three computer programs for comparisons of protein and DNA sequences can be used to search sequence data bases, evaluate similarity scores, and identify periodic structures based on local sequence similarity.
13.3K
A general method applicable to the search for similarities in the amino acid sequence of two proteins
TL;DR: A computer adaptable method for finding similarities in the amino acid sequences of two proteins has been developed and it is possible to determine whether significant homology exists between the proteins to trace their possible evolutionary development.
13.2K
Identification of common molecular subsequences.
TL;DR: This letter extends the heuristic homology algorithm of Needleman & Wunsch (1970) to find a pair of segments, one from each of two long sequences, such that there is no other Pair of segments with greater similarity (homology).
11.3K
Profile hidden Markov models.
TL;DR: Profile HMM methods performed comparably to threading methods in the CASP2 structure prediction exercise and complement standard pairwise comparison methods for large-scale sequence analysis.
5.9K