SALoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs

Seongyeon Park; Hajin Kim; Tanveer Ahmad; Nauman Ahmed; Zaid Al-Ars; H. Peter Hofstee; Youngsok Kim; Jinho Lee

doi:10.1109/IPDPS53621.2022.00076

SALoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs

Seongyeon Park, Hajin Kim, Tanveer Ahmad, Nauman Ahmed, Zaid Al-Ars, H. Peter Hofstee, Youngsok Kim, Jinho Lee

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Sequence alignment forms an important backbone in many sequencing applications. A commonly used strategy for sequence alignment is an approximate string matching with a two-dimensional dynamic programming approach. Although some prior work has been conducted on GPU acceleration of a sequence alignment, we identify several shortcomings that limit exploiting the full computational capability of modern GPUs. This paper presents SALoBa, a GPU-accelerated sequence alignment library focused on seed extension. Based on the analysis of previous work with real-world sequencing data, we propose techniques to exploit the data locality and improve work-load balancing. The experimental results reveal that SALoBa significantly improves the seed extension kernel compared to state-of-the-art GPU-based methods.

Original language	English
Title of host publication	Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	728-738
Number of pages	11
ISBN (Electronic)	9781665481069
DOIs	https://doi.org/10.1109/IPDPS53621.2022.00076
Publication status	Published - 2022
Event	36th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2022 - Virtual, Online, France Duration: 2022 May 30 → 2022 Jun 3

Publication series

Name	Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022

Conference

Conference	36th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2022
Country/Territory	France
City	Virtual, Online
Period	22/5/30 → 22/6/3

Bibliographical note

Funding Information:
This work has been supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2022R1C1C1008131, 2022R1C1C1011307) and Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University), 2021-0-00853, Developing Software Platform for Programming of PIM).

Publisher Copyright:
© 2022 IEEE.

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Hardware and Architecture
Computer Science Applications

Access to Document

10.1109/IPDPS53621.2022.00076

Cite this

Park, S., Kim, H., Ahmad, T., Ahmed, N., Al-Ars, Z., Hofstee, H. P., Kim, Y., & Lee, J. (2022). SALoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs. In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022 (pp. 728-738). (Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IPDPS53621.2022.00076

Park, Seongyeon ; Kim, Hajin ; Ahmad, Tanveer et al. / SALoBa : Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs. Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 728-738 (Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022).

@inproceedings{86e99e1bfd2a45aab2d84337d0615c0b,

title = "SALoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs",

abstract = "Sequence alignment forms an important backbone in many sequencing applications. A commonly used strategy for sequence alignment is an approximate string matching with a two-dimensional dynamic programming approach. Although some prior work has been conducted on GPU acceleration of a sequence alignment, we identify several shortcomings that limit exploiting the full computational capability of modern GPUs. This paper presents SALoBa, a GPU-accelerated sequence alignment library focused on seed extension. Based on the analysis of previous work with real-world sequencing data, we propose techniques to exploit the data locality and improve work-load balancing. The experimental results reveal that SALoBa significantly improves the seed extension kernel compared to state-of-the-art GPU-based methods.",

author = "Seongyeon Park and Hajin Kim and Tanveer Ahmad and Nauman Ahmed and Zaid Al-Ars and Hofstee, {H. Peter} and Youngsok Kim and Jinho Lee",

note = "Funding Information: This work has been supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2022R1C1C1008131, 2022R1C1C1011307) and Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University), 2021-0-00853, Developing Software Platform for Programming of PIM). Publisher Copyright: {\textcopyright} 2022 IEEE.; 36th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2022 ; Conference date: 30-05-2022 Through 03-06-2022",

year = "2022",

doi = "10.1109/IPDPS53621.2022.00076",

language = "English",

series = "Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "728--738",

booktitle = "Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022",

address = "United States",

}

Park, S, Kim, H, Ahmad, T, Ahmed, N, Al-Ars, Z, Hofstee, HP, Kim, Y & Lee, J 2022, SALoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs. in Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022. Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, Institute of Electrical and Electronics Engineers Inc., pp. 728-738, 36th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2022, Virtual, Online, France, 22/5/30. https://doi.org/10.1109/IPDPS53621.2022.00076

SALoBa : Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs. / Park, Seongyeon; Kim, Hajin; Ahmad, Tanveer et al.

Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 728-738 (Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - SALoBa

T2 - 36th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2022

AU - Park, Seongyeon

AU - Kim, Hajin

AU - Ahmad, Tanveer

AU - Ahmed, Nauman

AU - Al-Ars, Zaid

AU - Hofstee, H. Peter

AU - Kim, Youngsok

AU - Lee, Jinho

N1 - Funding Information: This work has been supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2022R1C1C1008131, 2022R1C1C1011307) and Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University), 2021-0-00853, Developing Software Platform for Programming of PIM). Publisher Copyright: © 2022 IEEE.

PY - 2022

Y1 - 2022

N2 - Sequence alignment forms an important backbone in many sequencing applications. A commonly used strategy for sequence alignment is an approximate string matching with a two-dimensional dynamic programming approach. Although some prior work has been conducted on GPU acceleration of a sequence alignment, we identify several shortcomings that limit exploiting the full computational capability of modern GPUs. This paper presents SALoBa, a GPU-accelerated sequence alignment library focused on seed extension. Based on the analysis of previous work with real-world sequencing data, we propose techniques to exploit the data locality and improve work-load balancing. The experimental results reveal that SALoBa significantly improves the seed extension kernel compared to state-of-the-art GPU-based methods.

AB - Sequence alignment forms an important backbone in many sequencing applications. A commonly used strategy for sequence alignment is an approximate string matching with a two-dimensional dynamic programming approach. Although some prior work has been conducted on GPU acceleration of a sequence alignment, we identify several shortcomings that limit exploiting the full computational capability of modern GPUs. This paper presents SALoBa, a GPU-accelerated sequence alignment library focused on seed extension. Based on the analysis of previous work with real-world sequencing data, we propose techniques to exploit the data locality and improve work-load balancing. The experimental results reveal that SALoBa significantly improves the seed extension kernel compared to state-of-the-art GPU-based methods.

UR - http://www.scopus.com/inward/record.url?scp=85136332067&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85136332067&partnerID=8YFLogxK

U2 - 10.1109/IPDPS53621.2022.00076

DO - 10.1109/IPDPS53621.2022.00076

M3 - Conference contribution

AN - SCOPUS:85136332067

T3 - Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022

SP - 728

EP - 738

BT - Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 30 May 2022 through 3 June 2022

ER -

Park S, Kim H, Ahmad T, Ahmed N, Al-Ars Z, Hofstee HP et al. SALoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs. In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 728-738. (Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022). doi: 10.1109/IPDPS53621.2022.00076

SALoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs

Abstract

Publication series

Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this