A novel cooperative accelerated parallel two-list algorithm for solving the subset-sum problem on a hybrid CPU-GPU cluster

Many parallel algorithms have recently been developed to accelerate solving the subset-sum problem on a heterogeneous CPU–GPU system. However, within each compute node, only one CPU core is used to control one GPU and all the remaining CPU cores are in idle state, which leads to a large number of CPU cores being wasted. In this paper, based on a cost-optimal parallel two-list algorithm, we propose a novel heterogeneous cooperative computing approach to solve the subset-sum problem on a hybrid CPU–GPU cluster, which can make full use of all available computational resources of a cluster. The unbalanced workload distribution and the huge communication overhead are two main obstacles for the heterogeneous cooperative computing. In order to assign the most suitable workload to each compute node and reasonably partition it between CPU and GPU within each node, and minimize the internode and intra-node communication costs, we design a communication-avoiding workload distribution scheme suitable for the parallel two-list algorithm. According to this scheme, we provide an efficient heterogeneous cooperative implementation of the algorithm. A series of experiments are conducted on a hybrid CPU–GPU cluster, where each node has two 6-core CPUs and one GPU. The results show that the heterogeneous cooperative computing significantly outperforms the CPU-only or GPU-only computing. © 2016 Elsevier Inc. All rights reserved.