A lightweight BLASTP and its implementation on CUDA GPUs

被引:0
|
作者
Liang-Tsung Huang
Kai-Cheng Wei
Chao-Chin Wu
Chao-Yu Chen
Jian-An Wang
机构
[1] Tzu Chi University,Department of Medical Informatics
[2] National Changhua University of Education,Department of Computer Science and Information Engineering
来源
关键词
BLAST; BLASTP; GPU; Bioinformatics; Parallelization;
D O I
暂无
中图分类号
学科分类号
摘要
The BLAST server in the National Center for Biotechnology Information in the USA receives tens of thousands of queries per day on average. However, the service is always the same for every query even though query lengths vary significantly. In fact, the lengths of a large portion of protein sequences are less than 500. On the other hand, the hit detection process consumes the most of the execution time of BLAST and its core architecture is a lookup table. Following the above reasons, we propose a lightweight BLASTP for servicing not-too-long queries, where a hybrid query-index table is proposed accordingly. Each table entry consists of four bytes that can store up to three query positions. Therefore, a sequence word usually requires only one memory fetch to retrieve its hit information. Furthermore, additional dummy entries are embedded into the table and interleaved with original entries. The entries without any hits and dummy entries both can be used to buffer spilled query positions. The above features result in a much smaller lookup table with a higher utilization rate and a lower cache miss ratio. Experimental results show that the lightweight BLASTP outperforms CUDA-BLASTP with speedups ranging from 1.82 to 3.37 based on the first two critical phases.
引用
收藏
页码:322 / 342
页数:20
相关论文
共 50 条
  • [1] A lightweight BLASTP and its implementation on CUDA GPUs
    Huang, Liang-Tsung
    Wei, Kai-Cheng
    Wu, Chao-Chin
    Chen, Chao-Yu
    Wang, Jian-An
    JOURNAL OF SUPERCOMPUTING, 2021, 77 (01): : 322 - 342
  • [2] The Design and Implementation of an Improved Lightweight BLASTP on CUDA GPU
    Sun, Xue
    Wu, Chao-Chin
    Liu, Yan-Fang
    SYMMETRY-BASEL, 2021, 13 (12):
  • [3] CUDA-BLASTP: Accelerating BLASTP on CUDA-Enabled Graphics Hardware
    Liu, Weiguo
    Schmidt, Bertil
    Mueller-Wittig, Wolfgang
    IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2011, 8 (06) : 1678 - 1684
  • [4] Parallel Fast Walsh Transform Algorithm and Its Implementation with CUDA on GPUs
    Bikov, Dusan
    Bouyukliev, Iliya
    CYBERNETICS AND INFORMATION TECHNOLOGIES, 2018, 18 (05) : 21 - 43
  • [5] Design and implementation of an efficient integer count sort in CUDA GPUs
    Kolonias, Vasileios
    Voyiatzis, Artemios G.
    Goulas, George
    Housos, Efthymios
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2011, 23 (18): : 2365 - 2381
  • [6] A Parameterisable and Scalable Smith-Waterman Algorithm Implementation on CUDA-compatible GPUs
    Ling, Cheng
    Benkrid, Khaled
    Hamada, Tsuyoshi
    2009 IEEE 7TH SYMPOSIUM ON APPLICATION SPECIFIC PROCESSORS (SASP 2009), 2009, : 94 - +
  • [7] Fault mitigation strategies for CUDA GPUs
    Di Carlo, Stefano
    Gambardella, Giulio
    Martella, Ippazio
    Prinetto, Paolo
    Rolfo, Daniele
    Trotta, Pascal
    2013 IEEE INTERNATIONAL TEST CONFERENCE (ITC), 2013,
  • [8] Real-time video image processing through GPUs and CUDA and its future implementation in real problems in a Smart City
    Alberto Hernandez-Aguilar, Jose
    Carlos Bonilla-Robles, Juan
    Zavala Diaz, Jose Crispin
    Ochoa, Alberto
    INTERNATIONAL JOURNAL OF COMBINATORIAL OPTIMIZATION PROBLEMS AND INFORMATICS, 2019, 10 (03): : 33 - 49
  • [10] CUDA Flux: A Lightweight Instruction Profiler for CUDA Applications
    Braun, Lorenz
    Froning, Holger
    PROCEEDINGS OF 2019 IEEE/ACM PERFORMANCE MODELING, BENCHMARKING AND SIMULATION OF HIGH PERFORMANCE COMPUTER SYSTEMS (PMBS 2019), 2019, : 73 - 81