比對生物序列和蛋白質資料庫是生物資訊研究及應用裡一個重要的步驟，由於讀取序列技術快速的成長，序列資料變得難以處理。由NCBI提供的BLASTX是最知名的比對工具，因爲他的靈敏度很好。然而，用它來比對大量資料和資料庫會非常慢。

2015年，由Buchfink Xie和Huson提出的軟體DIAMOND(Nature Methods, 2015), 顯著地加速比對程序且保持和BLASTX差不多的靈敏度，然而，用DIAMOND比對大量資料仍然很慢。已經有一些加速手段被研究去加速DIAMOND，例如，AC-DIAMOND(Mai et al., Proc BIBE, 2016)使用CPU SIMD指令並且得到四倍的加速；HAMOND(Yu et al., J. Biotechnology, 2017)把DIAMOND在Hadoop分散式系統上平行化。

儘管最近使用GPU加速演算法的成功例子很多，卻沒有DIAMOND的GPU加速版本。
在這篇論文中，我們提出有效率GPU加速DIAMOND的CU-DIAMOND。
實驗結果顯示CU-DIAMOND在DIAMOND最花時間的部分加速了十倍，整體則得到比DIAMOND快了四倍的結果。
這些結果都是保證和DIAMOND相同的靈敏度。

Aligning biological sequences against a protein database is an im-
portant step of bioinformatics research and applications. Due to
the rapid growth of sequencing technologies, sequence data becomes
more difficult to handle. BLASTX, a software provided by NCBI, is
the most popular alignment tool due to its high sensitivity. However,
it is too slow in aligning large dataset with database.

In 2015, DIAMOND, a software proposed by Buchfink, Xie, and
Huson (Nature Methods, 2015) , speeds up the alignment process
significantly while maintaining similar sensitivity as BLASTX. How-
ever, DIAMOND is still slow when the query data is large. Several
acceleration techniques have been studied to improve the speed of
DIAMOND. For instance, AC-DIAMOND (Mai et al., Proc. BIBE,
2016) utilizes CPU SIMD instructions and reports a 4-fold overall
speedup over DIAMOND; HAMOND (Yu et al., J. Biotechnology,
2017) parallelizes DIAMOND on Hadoop distributed system.
Despite the many recent successes in applying GPU technology
to speed up algorithms, there is no GPU-accelerated version of DIA-
MOND.

In this thesis, we present CU-DIAMOND, an efficient GPU
acceleration of DIAMOND. Experimental results show that CU-
DIAMOND achieves a 10-fold speedup in the most time-consuming
alignment part of DIAMOND, and gains a 4-fold overall speedup
over DIAMOND (and a 33% speedup over AC-DIAMOND), while
sensitivity remains the same.

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