WFAsic: A High-Performance ASIC Accelerator for DNA Sequence Alignment on a RISC-V SoC-Reference-Cited by-同舟云学术

WFAsic: A High-Performance ASIC Accelerator for DNA Sequence Alignment on a RISC-V SoC

Published:2023-08-07 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 52nd International Conference on Parallel Processing
language:
Short-container-title:

Author:

Haghi Abbas¹^ORCID,Alvarez Lluc¹^ORCID,Front Jordi¹^ORCID,De Haro Ruiz Juan Miguel¹^ORCID,Figueras Roger²^ORCID,Doblas Max¹^ORCID,Marco-Sola Santiago³^ORCID,Moreto Miquel¹^ORCID

Affiliation:

1. Barcelona Supercomputing Center, Spain and Universitat Politècnica de Catalunya, Spain

2. Barcelona Supercomputing Center, Spain

3. Barcelona Supercomputing Center, Spain and Universitat Autònoma de Barcelona, Spain

Funder

HiPEAC Network

Spanish Ministry of Science and Innovation

Generalitat de Catalunya

European Union

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3605573.3605651

Reference21 articles.

1. Quim Aguado-Puig , Santiago Marco-Sola , Juan Carlos Moure , Christos Matzoros , David Castells-Rufas , Antonio Espinosa , and Miquel Moreto . 2022. WFA-GPU: Gap-affine pairwise alignment using GPUs. bioRxiv ( 2022 ). Quim Aguado-Puig, Santiago Marco-Sola, Juan Carlos Moure, Christos Matzoros, David Castells-Rufas, Antonio Espinosa, and Miquel Moreto. 2022. WFA-GPU: Gap-affine pairwise alignment using GPUs. bioRxiv (2022).

2. Basic local alignment search tool

3. A new approach to text searching

4. Damla Senol Cali , Gurpreet S Kalsi , Zülal Bingöl , Can Firtina , Lavanya Subramanian , Jeremie S Kim , Rachata Ausavarungnirun , Mohammed Alser , Juan Gomez-Luna , Amirali Boroumand , 2020. GenASM: A high-performance, low-power approximate string matching acceleration framework for genome sequence analysis . IEEE , 951–966. Damla Senol Cali, Gurpreet S Kalsi, Zülal Bingöl, Can Firtina, Lavanya Subramanian, Jeremie S Kim, Rachata Ausavarungnirun, Mohammed Alser, Juan Gomez-Luna, Amirali Boroumand, 2020. GenASM: A high-performance, low-power approximate string matching acceleration framework for genome sequence analysis. IEEE, 951–966.

5. Damla Senol Cali , Konstantinos Kanellopoulos , Joël Lindegger , Zülal Bingöl , Gurpreet S Kalsi , Ziyi Zuo , Can Firtina , Meryem Banu Cavlak , Jeremie Kim , Nika Mansouri Ghiasi , 2022. SeGraM: a universal hardware accelerator for genomic sequence-to-graph and sequence-to-sequence mapping. arXiv preprint arXiv:2205.05883 ( 2022 ). Damla Senol Cali, Konstantinos Kanellopoulos, Joël Lindegger, Zülal Bingöl, Gurpreet S Kalsi, Ziyi Zuo, Can Firtina, Meryem Banu Cavlak, Jeremie Kim, Nika Mansouri Ghiasi, 2022. SeGraM: a universal hardware accelerator for genomic sequence-to-graph and sequence-to-sequence mapping. arXiv preprint arXiv:2205.05883 (2022).

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. QUETZAL: Vector Acceleration Framework for Modern Genome Sequence Analysis Algorithms;2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA);2024-06-29

2. BIMSA: Accelerating Long Sequence Alignment Using Processing-In-Memory;2024-05-14