Parallel accelerated vector similarity calculations for genomics applications
Abstract
The surge in availability of genomic data holds promise for enabling determination of genetic causes of observed individual traits, with applications to problems such as discovery of the genetic roots of phenotypes, be they molecular phenotypes such as gene expression or metabolite concentrations, or complex phenotypes such as diseases. However, the growing sizes of these datasets and the quadratic, cubic or higher scaling characteristics of the relevant algorithms pose a serious computational challenge necessitating use of leadership scale computing. In this study we describe a new approach to performing vector similarity metrics calculations, suitable for parallel systems equipped with graphics processing units (GPUs) or Intel Xeon Phi processors. Our primary focus is the Proportional Similarity metric applied to Genome Wide Association Studies (GWAS) and Phenome Wide Association Studies (PheWAS). We describe the implementation of the algorithms on accelerated processors, methods used for eliminating redundant calculations due to symmetries, and techniques for efficient mapping of the calculations to many-node parallel systems. Finally, results are presented demonstrating high per-node performance and parallel scalability with rates of more than five quadrillion (5 × 1015) elementwise comparisons achieved per second on the ORNL Titan system. In a companion paper we describe corresponding techniques appliedmore »
- Authors:
-
[1];
[1];
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States). The Bredesen Center for Interdisciplinary Research and Graduate Education
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1474712
- Alternate Identifier(s):
- OSTI ID: 1548509
- Grant/Contract Number:
- AC05-00OR22725; PS02-06ER64304
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Parallel Computing
- Additional Journal Information:
- Journal Volume: 75; Journal ID: ISSN 0167-8191
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; 59 BASIC BIOLOGICAL SCIENCES; high performance computing; parallel algorithms; NVIDIA ® GPU; Intel ® Xeon Phi; comparative genomics; vector similarity metrics; Proportional Similarity metric
Citation Formats
Joubert, Wayne, Nance, James, Weighill, Deborah, and Jacobson, Daniel. Parallel accelerated vector similarity calculations for genomics applications. United States: N. p., 2018.
Web. doi:10.1016/j.parco.2018.03.009.
Joubert, Wayne, Nance, James, Weighill, Deborah, & Jacobson, Daniel. Parallel accelerated vector similarity calculations for genomics applications. United States. https://doi.org/10.1016/j.parco.2018.03.009
Joubert, Wayne, Nance, James, Weighill, Deborah, and Jacobson, Daniel. Tue .
"Parallel accelerated vector similarity calculations for genomics applications". United States. https://doi.org/10.1016/j.parco.2018.03.009. https://www.osti.gov/servlets/purl/1474712.
@article{osti_1474712,
title = {Parallel accelerated vector similarity calculations for genomics applications},
author = {Joubert, Wayne and Nance, James and Weighill, Deborah and Jacobson, Daniel},
abstractNote = {The surge in availability of genomic data holds promise for enabling determination of genetic causes of observed individual traits, with applications to problems such as discovery of the genetic roots of phenotypes, be they molecular phenotypes such as gene expression or metabolite concentrations, or complex phenotypes such as diseases. However, the growing sizes of these datasets and the quadratic, cubic or higher scaling characteristics of the relevant algorithms pose a serious computational challenge necessitating use of leadership scale computing. In this study we describe a new approach to performing vector similarity metrics calculations, suitable for parallel systems equipped with graphics processing units (GPUs) or Intel Xeon Phi processors. Our primary focus is the Proportional Similarity metric applied to Genome Wide Association Studies (GWAS) and Phenome Wide Association Studies (PheWAS). We describe the implementation of the algorithms on accelerated processors, methods used for eliminating redundant calculations due to symmetries, and techniques for efficient mapping of the calculations to many-node parallel systems. Finally, results are presented demonstrating high per-node performance and parallel scalability with rates of more than five quadrillion (5 × 1015) elementwise comparisons achieved per second on the ORNL Titan system. In a companion paper we describe corresponding techniques applied to calculations of the Custom Correlation Coefficient for comparative genomics applications.},
doi = {10.1016/j.parco.2018.03.009},
journal = {Parallel Computing},
number = ,
volume = 75,
place = {United States},
year = {Tue Mar 27 00:00:00 EDT 2018},
month = {Tue Mar 27 00:00:00 EDT 2018}
}
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Figures / Tables:
Figure 1: (a): Computational pattern of 2-way metric calculation; (b) Data decomposition for 2-way metric calculation
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Figures / Tables found in this record:
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