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cuBLASTP: Fine-Grained Parallelization of Protein Sequence Search on a GPU

Jing Zhang, Hao Wang, Heshan Lin, Wu-chun Feng
Dept. of Computer Science, Virginia Tech
IEEE International Parallel and Distributed Processing Symposium, 2014

@InProceedings{zhang-cublastp-ipdps14,

   author={Zhang, Jing and Wang, Hao and Lin, Heshan and Feng, Wu-chun},

   title={cuBLASTP: Fine-Grained Parallelization of Protein Sequence Search on a GPU},

   booktitle={IEEE International Parallel and Distributed Processing Symposium},

   address={Phoenix, Arizona, USA},

   month={May},

   year={2014}

}

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BLAST, short for Basic Local Alignment Search Tool, is a fundamental algorithm in the life sciences that compares biological sequences. However, with the advent of next-generation sequencing (NGS) and increase in sequence read-lengths, whether at the outset or downstream from NGS, the exponential growth of sequence databases is arguably outstripping our ability to analyze the data. Though several recent studies have utilized the graphics processing unit (GPU) to speedup the BLAST algorithm for searching protein sequences (i.e., BLASTP), these studies used coarse-grained parallel approaches, where one sequence alignment is mapped to only one thread. Moreover, due to the irregular memory access patterns in BLASTP, there remain significant challenges to map the most time-consuming phases (i.e., hit detection and ungapped extension) to the GPU using a fine-grained multithreaded approach. To address the above issues, we propose cuBLASTP, an efficient fine-grained BLASTP implementation for the GPU using CUDA. Our cuBLASTP realization encompasses many research contributions, including (1) memory-access reordering to reorder hits from column-major order to diagonal-major order, (2) position-based indexing to map a hit with a packed data structure to a bin, (3) aggressive hit filtering to eliminate hits beyond the threshold distance along the diagonal, (4) diagonal-based parallelism and hit-based parallelism for ungapped extension to extend sequences with different lengths in databases, and (5) hierarchical buffering to reduce memory-access overhead for the core data structures. The experimental results show that on a NVIDIA Kepler GPU, cuBLASTP delivers up to a 5.0-fold speedup over sequential FSA-BLAST and a 3.7-fold speedup over multithreaded NCBI-BLAST for the overall program execution. In addition, compared with GPU-BLASTP (the fastest GPU implementation of BLASTP to date), cuBLASTP achieves up to a 2.8-fold speedup for the kernel execution on the GPU and a 1.8-fold speedup for the overall program execution.
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