2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) (2018)
Washington, DC, USA
May 1, 2018 to May 4, 2018
Graphics Processing Units (GPUs) are gaining notable popularity due to their affordable high performance multi-core architecture. They are particularly useful for massive computations that involve large data sets. In this paper, we present a highly scalable approach for the NP-hard Vertex Cover problem. Our method is based on an advanced data structure to reduce memory usage for more parallelism and we propose a load balancing scheme that is effective for multiGPU architectures. Our parallel algorithm was implemented on multiple AMD GPUs using OpenCL. Experimental results show that our proposed approach can achieve significant speedups on the hard instances of the DIMACS benchmarks as well as the notoriously hard 120-Cell graph and its variants.
computational complexity, coprocessors, data structures, graph theory, graphics processing units, multiprocessing systems, optimisation, parallel algorithms, parallel architectures, resource allocation
F. N. Abu-Khzam, D. Kim, M. Perry, K. Wang and P. Shaw, "Accelerating Vertex Cover Optimization on a GPU Architecture," 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), Washington, DC, USA, 2018, pp. 616-625.