.png){kind=icon}
.png){kind=icon}
Return
A low-space algorithm for the subset-sum problem on GPU
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cor.2017.02.006.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Subset-Sum problem
Parallel algorithm
CPU
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
C
IF:
4.3
Papers:
6.5K
Citations:
1.8W