Name
Abstract | ||
|---|---|---|
In this paper, we propose a work distribution strategy to implement DNA global sequence alignment (GSA). The proposed strategy is implemented on Compute Unified Device Architecture (CUDA) for large biological sequences. The main objective of this work is to minimize the execution time required for DNA global alignment of large biological sequences. The proposed approach covers both the memory optimizations and minimization of execution time. We considered the biological sequences of different size to fit into the global memory under CUDA. The efficient use of global memory and memory optimization dominate the results of execution time. The results demonstrate the significant higher speedup using CUDA. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CCCI49893.2020.9256747 | 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) |
Keywords | DocType | ISBN |
DNA sequence,CUDA,GPU,global sequence alignment | Conference | 978-1-7281-7316-0 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kailash W. Kalare | 1 | 0 | 0.34 |
| Mohammad S. Obaidat | 2 | 2190 | 315.70 |
| Jitendra V. Tembhurne | 3 | 0 | 0.34 |
| Chandrashekhar Meshram | 4 | 56 | 7.23 |
| Kuei-Fang Hsiao | 5 | 83 | 9.82 |