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系統識別號 U0026-1108201116331100
論文名稱(中文) 快速的序列比對方法使用具有CUDA的GPU
論文名稱(英文) Fast Sequence Alignment Methods Using CUDA-enabled GPU
校院名稱 成功大學
系所名稱(中) 資訊工程學系碩博士班
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 99
學期 2
出版年 100
研究生(中文) 陳德宇
研究生(英文) De-Yu Chen
學號 p76981293
學位類別 碩士
語文別 英文
論文頁數 63頁
口試委員 指導教授-張燕光
口試委員-朱治平
口試委員-陳培殷
口試委員-謝孫源
口試委員-徐武孝
中文關鍵字 生物序列比對  動態規劃  prefix max scan  GPU  CUDA 
英文關鍵字 sequence alignment  dynamic programming  prefix max scan  GPU  CUDA 
學科別分類
中文摘要 生物序列比對是一件計算序列間相似程度的工作。利用序列比對從資料庫中找尋一條跟查詢序列最相似的序列是生物資訊研究的第一個步驟。Needleman-Wunsch(NW)演算法首先被提出使用動態規劃的去計算最佳的全域比對。NW首先計算比對分數,接著Smith-Waterman(SW)演算法被提出去計算最佳的局部比對。然而,每年資料庫中的序列數量呈指數增加,上述的兩個演算法會因慢速執行時間和消耗龐大空間導致不理想。因此,加速或改善序列比對演算法已成為近幾年研究的趨勢。
在本篇論文中,我們使用具有CUDA技術的GPU提出了兩個快速序列比對的方法。第一個提出的方法稱為Parallel Smith-Waterman (PSW),是SW演算法的平行版本。在PSW中,SW演算法的遞迴公式被重新定義以至於矩陣的一列可以被平行的計算,接著prefix max scan的方法被使用來減少一個cell的計算複雜度。除此之外,共享記憶體被使用來減少記憶體存取的時間。第二個提出的方法稱為Common Substring Alignment (CSA),是一個快速計算全域分數和藉由同時加入gap執行比對的全域比對演算法。然而,我們專注於計算全域比對。兩條序列有許多共同子字串可能使序列相似程度很高。所以基於觀察NW演算法的traceback路徑,CSA對齊共同子字串並且在已對齊的共同子字串間加入間隙,最後全域比對分數可以被簡單的取得藉由匹配兩條序列的每個字母。我們的實驗對於序列長度128~8192展示出下列結果。PSW 44~62倍快於基於CPU的SW演算法製作,1.1~7.1倍快於基於CUDA的SW演算法製作CUDASW++。CSA也44~64倍快於基於CPU的NW演算法製作和1.1~6倍快於基於CUDA的NW演算法。
英文摘要 Sequence alignment is a task that calculates the degree of similarity between two sequences. Finding sequence form database which are the most similar to a given query sequence by sequence alignment is the first step in bioinformatics research. The Needleman-Wunsch (NW) algorithm was first proposed to compute the optimal global alignment by using dynamic programming. NW first computes the alignment scores which are then used to perform the so-called traceback process to complete the alignment by adding gaps. Afterward, Smith-Waterman (SW) algorithm was proposed to compute an optimal local alignment. However, the number of sequences in database increases exponentially every year and the cost of these two algorithms is expensive in terms of running time and memory space to find similar database sequences to a query sequence. Thus, accelerating the sequence alignment algorithms has become the trend in recent years.In this thesis, we propose two fast sequence alignment methods using CUDA-enabled GPUs. The first proposed method called Parallel Smith-Waterman (PSW) is a parallel version of SW algorithm. In PSW, the recursive formula of SW algorithm is redefined so that one row of the matrix can be calculated in parallel. Then, the prefix max scan method is used to reduce the cell computation complexity. In addition, on-chip memory is used for reducing the penalty of memory accesses. The second proposed method called Common Substring Alignment (CSA) is a rapid global alignment algorithm that computes the global score and performs the alignment by adding gaps at the same time. However, we focus on computing the global alignment. Two sequences sharing many common substrings may have a high degree of similarity between them. Therefore, based on the observation of the traceback path in NW algorithm, CSA aligns the common substrings and inserts the gaps between the aligned common substrings. Finally, the global alignment score can be easily obtained by matching all the letters of both sequences. Our experiments for sequences of lengths 128 to 8,192 show the following results. PSW is 44~62 times faster than CPU-based implementation of SW algorithm and 1.1~7.1 times faster than CUDASW++, the most popular CUDA-based implementation of SW algorithm, depending on the length difference between query and database sequences. In term of run times for computing the global alignment score, CSA performs 44~64 times faster than CPU-based implementation of NW algorithm and 1.1~6 times faster than the CUDA-based implementation of NW algorithm.
論文目次 Chapter 1 Introduction 1
Chapter 2 Related work 6
2.1 Needleman-Wunsch algorithm 7
2.1.1 Computation phase 8
2.1.2 Traceback phase 9
2.1.3 Performance metrics 10
2.2 Smith-Waterman algorithm 12
2.2.1 Computation phase 13
2.2.2 Traceback phase 15
2.3 Accelerated computation 16
2.3.1 The anti-diagonal parallelism mechanism for SW 16
2.3.2 The Vector based parallelism mechanism for SW 17
2.3.3 The combined parallelism mechanism for SW 19
2.4 FASTA algorithm 19
2.5 BLAST algorithm 21
2.5.1 Seed generation 22
2.5.2 Ungapped Extension 24
2.5.3 Gapped extension 25
Chapter 3 CUDA 26
3.1 CUDA programming model 26
3.2 CUDA hardware model 30
Chapter 4 Proposed methods 31
4.1 Parallel Smith-Waterman (PSW) 32
4.1.1 Optimization 34
4.1.2 CUDA implementation 35
4.1.3 Pseudo code 37
4.2 Common Substring Alignment (CSA) 41
4.2.1 Find the common substrings 42
4.2.2 Common substring selection strategy 44
4.2.3 Realignment procedure 49
4.2.4 CUDA implementation 50
4.2.5 Pseudo code 51
Chapter 5 Experimental results 54
5.1 PSW performance 55
5.2 CSA performance 57
Chapter 6 Conclusion 60
Chapter 7 References 61
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