A low-space algorithm for the subset-sum problem on GPU

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 106 and 200 items limited to 108. 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.