DRCD: A Regional-Contention-Driven Arbitration Policy for CPU-GPU Heterogeneous Systems
Research Square
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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
Springer Science and Business Media LLC
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