Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Heterogeneous CPU-GPU system is a porful 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. Hover, 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.  proposes an efficient CPU-GPU cooperative computing scheme for solving the subset-sum problem, which enables the full utilization of all the computing por of both CPUs and GPUs. In order to find the most appropriate task distribution ratio beten CPUs and GPUs, this paper establishes a simple but effective task distribution model. Considering the high CPU-GPU communication overhead and the unbalanced workload beten 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 beten 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.
['Lanjun Wan', 'Kenli Li', 'Jing Liu', 'Keqin Li']
I am writing a research paper on a idea Efficient CPU-GPU cooperative computing for solving the subset-sum problem
[{"from": "human", "value": "I am writing a research paper on a idea Efficient CPU-GPU cooperative computing for solving the subset-sum problem"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Heterogeneous CPU-GPU system is a porful 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. Hover, 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.  proposes an efficient CPU-GPU cooperative computing scheme for solving the subset-sum problem, which enables the full utilization of all the computing por of both CPUs and GPUs. In order to find the most appropriate task distribution ratio beten CPUs and GPUs, this paper establishes a simple but effective task distribution model. Considering the high CPU-GPU communication overhead and the unbalanced workload beten 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 beten 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 \u00a9 2015 John Wiley & Sons, Ltd."}]