Efficient CPU-GPU cooperative computing for solving the subset-sum problem

Heterogeneous CPU-GPU system is a powerful way to accelerate compute-intensive applications, such as the subset-sum problem. Many parallel algorithms for solving the problem have been implemented on graphics processing units (GPUs). However, these GPU implementations may fail to fully utilize all the CPU cores and the GPU resources. When the GPU performs computational task, only one CPU core is used to control the GPUs, and all the remaining CPU cores are in idle state, which leads to large amounts of available CPU resources being wasted. This paper proposes an efficient CPU-GPU cooperative computing scheme for solving the subset-sum problem, which enables the full utilization of all the computing power of both CPUs and GPUs. In order to find the most appropriate task distribution ratio between CPUs and GPUs, this paper establishes a simple but effective task distribution model. Considering the high CPU-GPU communication overhead and the unbalanced workload between CPUs and GPUs may greatly reduce the performance, an incremental data transfer method is proposed to reduce the CPU-GPU communication overhead, and a feedback-based dynamic task distribution scheme is designed to effectively balance the workload between CPUs and GPUs during runtime. The experimental results show that the CPU-GPU cooperative computing achieves a significant performance benefit over the CPU-only or GPU-only computing. Copyright © 2015 John Wiley & Sons, Ltd.