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DRCD: A Regional-Contention-Driven Arbitration Policy for CPU-GPU Heterogeneous Systems

Research Square
2024
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Article Description

In CPU-GPU heterogeneous systems, there exists intense resource contention between CPUs and GPUs. Traditional resource arbitration policies fail to account for the heterogeneity of cores, leading to inefficient network resource utilization for the CPU, which negatively impacts its performance. In heterogeneous networks, the degree of resource contention varies across different regions. This paper first uses reinforcement learning to analyze the message feature weights relied upon for resource arbitration in different network regions. To achieve more efficient resource allocation, a regional-contention-driven arbitration policy is proposed. Simulation results show that, compared to traditional arbitration policy, the overall network latency is reduced by 7.99%, and CPU performance is improved by 11.42%. Furthermore, a dynamic regional-contention-driven arbitration policy is proposed, which further reduces the overall network latency by 10.47% and increases CPU performance by 16.79% compared to traditional arbitration policy.

Bibliographic Details

Juan Fang; Haoyu Cheng; Yuening Wang; Ran Zhai

Springer Science and Business Media LLC

Biochemistry, Genetics and Molecular Biology; Immunology and Microbiology; Medicine; Neuroscience; Psychology; Dentistry

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