| Full text | |
| Author(s): |
Curtis, V. V.
;
Sanches, C. A. A.
Total Authors: 2
|
| Document type: | Journal article |
| Source: | Computers & Operations Research; v. 83, p. 120-124, JUL 2017. |
| Web of Science Citations: | 1 |
| Abstract | |
We present a highly scalable parallel solution for the Subset-Sum Problem on Graphics Processing Units (GPUs) that substantially reduces the memory access by the device and, consequently, decreases the total runtime. We test this algorithm only on hard instances, which require the exhaustion of the entire search space, instead of simple random benchmarks. On a GPU with only 1.2 GB of global memory, we address hard instances with 100,000 items limited to 10(6) and 200 items limited to 10(8). Our algorithm achieves excellent runtimes outperforming the best-known practical and parallel algorithms, reaching speed-ups higher than 1000 in the best case compared to its sequential version. (C) 2017 Elsevier Ltd. All rights reserved. (AU) | |
| FAPESP's process: | 15/18164-5 - Parallel solutions to intractable problems in industrial processes |
| Grantee: | Carlos Alberto Alonso Sanches |
| Support Opportunities: | Regular Research Grants |