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Title: ADEPT: a domain independent sequence alignment strategy for gpu architectures

Abstract

Bioinformatic workflows frequently make use of automated genome assembly and protein clustering tools. At the core of most of these tools, a significant portion of execution time is spent in determining optimal local alignment between two sequences. This task is performed with the Smith-Waterman algorithm, which is a dynamic programming based method. With the advent of modern sequencing technologies and increasing size of both genome and protein databases, a need for faster Smith-Waterman implementations has emerged. Multiple SIMD strategies for the Smith-Waterman algorithm are available for CPUs. However, with the move of HPC facilities towards accelerator based architectures, a need for an efficient GPU accelerated strategy has emerged. Existing GPU based strategies have either been optimized for a specific type of characters (Nucleotides or Amino Acids) or for only a handful of application use-cases. In this paper, we present ADEPT, a new sequence alignment strategy for GPU architectures that is domain independent, supporting alignment of sequences from both genomes and proteins. Our proposed strategy uses GPU specific optimizations that do not rely on the nature of sequence. We demonstrate the feasibility of this strategy by implementing the Smith-Waterman algorithm and comparing it to similar CPU strategies as well as themore » fastest known GPU methods for each domain. ADEPT’s driver enables it to scale across multiple GPUs and allows easy integration into software pipelines which utilize large scale computational systems. We have shown that the ADEPT based Smith-Waterman algorithm demonstrates a peak performance of 360 GCUPS and 497 GCUPs for protein based and DNA based datasets respectively on a single GPU node (8 GPUs) of the Cori Supercomputer. Overall ADEPT shows 10x faster performance in a node-to-node comparison against a corresponding SIMD CPU implementation. ADEPT demonstrates a performance that is either comparable or better than existing GPU strategies. We demonstrated the efficacy of ADEPT in supporting existing bionformatics software pipelines by integrating ADEPT in MetaHipMer a high-performance denovo metagenome assembler and PASTIS a high-performance protein similarity graph construction pipeline. Our results show 10% and 30% boost of performance in MetaHipMer and PASTIS respectively.« less

Authors:
 [1];  [1];  [1];  [1];  [1];  [1];  [1]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC); Exascale Computing Project
OSTI Identifier:
1706662
Grant/Contract Number:  
AC02-05CH11231; 17-SC-20-SC
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
BMC Bioinformatics
Additional Journal Information:
Journal Volume: 21; Journal Issue: 1; Journal ID: ISSN 1471-2105
Publisher:
BioMed Central
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Bioinformatics; GPU; alignment; protein; DNA

Citation Formats

Awan, Muaaz G., Deslippe, Jack, Buluc, Aydin, Selvitopi, Oguz, Hofmeyr, Steven, Oliker, Leonid, and Yelick, Katherine. ADEPT: a domain independent sequence alignment strategy for gpu architectures. United States: N. p., 2020. Web. doi:10.1186/s12859-020-03720-1.
Awan, Muaaz G., Deslippe, Jack, Buluc, Aydin, Selvitopi, Oguz, Hofmeyr, Steven, Oliker, Leonid, & Yelick, Katherine. ADEPT: a domain independent sequence alignment strategy for gpu architectures. United States. https://doi.org/10.1186/s12859-020-03720-1
Awan, Muaaz G., Deslippe, Jack, Buluc, Aydin, Selvitopi, Oguz, Hofmeyr, Steven, Oliker, Leonid, and Yelick, Katherine. Tue . "ADEPT: a domain independent sequence alignment strategy for gpu architectures". United States. https://doi.org/10.1186/s12859-020-03720-1. https://www.osti.gov/servlets/purl/1706662.
@article{osti_1706662,
title = {ADEPT: a domain independent sequence alignment strategy for gpu architectures},
author = {Awan, Muaaz G. and Deslippe, Jack and Buluc, Aydin and Selvitopi, Oguz and Hofmeyr, Steven and Oliker, Leonid and Yelick, Katherine},
abstractNote = {Bioinformatic workflows frequently make use of automated genome assembly and protein clustering tools. At the core of most of these tools, a significant portion of execution time is spent in determining optimal local alignment between two sequences. This task is performed with the Smith-Waterman algorithm, which is a dynamic programming based method. With the advent of modern sequencing technologies and increasing size of both genome and protein databases, a need for faster Smith-Waterman implementations has emerged. Multiple SIMD strategies for the Smith-Waterman algorithm are available for CPUs. However, with the move of HPC facilities towards accelerator based architectures, a need for an efficient GPU accelerated strategy has emerged. Existing GPU based strategies have either been optimized for a specific type of characters (Nucleotides or Amino Acids) or for only a handful of application use-cases. In this paper, we present ADEPT, a new sequence alignment strategy for GPU architectures that is domain independent, supporting alignment of sequences from both genomes and proteins. Our proposed strategy uses GPU specific optimizations that do not rely on the nature of sequence. We demonstrate the feasibility of this strategy by implementing the Smith-Waterman algorithm and comparing it to similar CPU strategies as well as the fastest known GPU methods for each domain. ADEPT’s driver enables it to scale across multiple GPUs and allows easy integration into software pipelines which utilize large scale computational systems. We have shown that the ADEPT based Smith-Waterman algorithm demonstrates a peak performance of 360 GCUPS and 497 GCUPs for protein based and DNA based datasets respectively on a single GPU node (8 GPUs) of the Cori Supercomputer. Overall ADEPT shows 10x faster performance in a node-to-node comparison against a corresponding SIMD CPU implementation. ADEPT demonstrates a performance that is either comparable or better than existing GPU strategies. We demonstrated the efficacy of ADEPT in supporting existing bionformatics software pipelines by integrating ADEPT in MetaHipMer a high-performance denovo metagenome assembler and PASTIS a high-performance protein similarity graph construction pipeline. Our results show 10% and 30% boost of performance in MetaHipMer and PASTIS respectively.},
doi = {10.1186/s12859-020-03720-1},
url = {https://www.osti.gov/biblio/1706662}, journal = {BMC Bioinformatics},
issn = {1471-2105},
number = 1,
volume = 21,
place = {United States},
year = {2020},
month = {9}
}

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