TY - GEN
T1 - Segalign
T2 - 2020 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2020
AU - Goenka, Sneha D.
AU - Turakhia, Yatish
AU - Paten, Benedict
AU - Horowitz, Mark
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - Pairwise Whole Genome Alignment (WGA) is a crucial first step to understanding evolution at the DNA sequence-level. Pairwise WGA of thousands of currently available species genomes could help make biological discoveries, however, computing them for even a fraction of the millions of possible pairs is prohibitive - WGA of a single pair of vertebrate genomes (human-mouse) takes 11 hours on a 96-core Amazon Web Services (AWS) instance (c5.24xlarge). This paper presents SegAlign - a scalable, GPU-accelerated system for computing pairwise WGA. SegAlign is based on the standard seed-filter-extend heuristic, in which the filtering stage dominates the runtime (e.g. 98% for human-mouse WGA), and is accelerated using GPU(s). Using three vertebrate genome pairs, we show that SegAlign provides a speedup of up to ;14 × on an 8-GPU, 64-core AWS instance (p3.16xlarge) for WGA and nearly ;2.3 × reduction in dollar cost. SegAlign also allows parallelization over multiple GPU nodes and scales efficiently.
AB - Pairwise Whole Genome Alignment (WGA) is a crucial first step to understanding evolution at the DNA sequence-level. Pairwise WGA of thousands of currently available species genomes could help make biological discoveries, however, computing them for even a fraction of the millions of possible pairs is prohibitive - WGA of a single pair of vertebrate genomes (human-mouse) takes 11 hours on a 96-core Amazon Web Services (AWS) instance (c5.24xlarge). This paper presents SegAlign - a scalable, GPU-accelerated system for computing pairwise WGA. SegAlign is based on the standard seed-filter-extend heuristic, in which the filtering stage dominates the runtime (e.g. 98% for human-mouse WGA), and is accelerated using GPU(s). Using three vertebrate genome pairs, we show that SegAlign provides a speedup of up to ;14 × on an 8-GPU, 64-core AWS instance (p3.16xlarge) for WGA and nearly ;2.3 × reduction in dollar cost. SegAlign also allows parallelization over multiple GPU nodes and scales efficiently.
KW - Apache Spark
KW - Comparative Genomics
KW - Graphics Processing Unit (GPU)
KW - Whole Genome Alignment
UR - http://www.scopus.com/inward/record.url?scp=85102369022&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85102369022&partnerID=8YFLogxK
U2 - 10.1109/SC41405.2020.00043
DO - 10.1109/SC41405.2020.00043
M3 - Conference contribution
AN - SCOPUS:85102369022
T3 - International Conference for High Performance Computing, Networking, Storage and Analysis, SC
BT - Proceedings of SC 2020
PB - IEEE Computer Society
Y2 - 9 November 2020 through 19 November 2020
ER -