TANGO: A GPU optimized traceback approach for sequence alignment algorithms-Reference-Cited by-同舟云学术

TANGO: A GPU optimized traceback approach for sequence alignment algorithms

Published:2023-11-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis
language:
Short-container-title:

Author:

Lindsey LeAnn¹^ORCID,Haseeb Muhammad²^ORCID,Sundar Hari¹^ORCID,Awan Muaaz²^ORCID

Affiliation:

1. University of Utah, USA

2. Lawrence Berkeley National Laboratory, USA

Funder

National Nuclear Security Administration

Office of Science

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3624062.3625128

Reference19 articles.

1. Nauman Ahmed , Jonathan Lévy , Shanshan Ren , Hamid Mushtaq , Koen Bertels , and Zaid Al-Ars . 2019. GASAL2: a GPU accelerated sequence alignment library for high-throughput NGS data. BMC bioinformatics 20, 1 ( 2019 ), 1–20. Publisher : Springer . Nauman Ahmed, Jonathan Lévy, Shanshan Ren, Hamid Mushtaq, Koen Bertels, and Zaid Al-Ars. 2019. GASAL2: a GPU accelerated sequence alignment library for high-throughput NGS data. BMC bioinformatics 20, 1 (2019), 1–20. Publisher: Springer.

2. Single cells make big data: New challenges and opportunities in transcriptomics

3. ADEPT: a domain independent sequence alignment strategy for gpu architectures

4. UPC++: A High-Performance Communication Framework for Asynchronous Computation

5. Richard Barnes . 2020. A Review of the Smith-Waterman GPU Landscape . Electrical Engineering and Computer Sciences University of California at Berkeley. Technical Report No. UCB/IEECS- 2020 -152. Retrieved from https://www2. eecs. berkeley. edu/Pubs/TechRpts/2020/EECS-2020-152. html 2020, 152 (2020), 1–27. Richard Barnes. 2020. A Review of the Smith-Waterman GPU Landscape. Electrical Engineering and Computer Sciences University of California at Berkeley. Technical Report No. UCB/IEECS-2020-152. Retrieved from https://www2. eecs. berkeley. edu/Pubs/TechRpts/2020/EECS-2020-152. html 2020, 152 (2020), 1–27.