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CLC number: TP391.9

On-line Access: 2023-07-24

Received: 2022-10-11

Revision Accepted: 2023-07-24

Crosschecked: 2023-01-04

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Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.7 P.994-1006


A perceptual and predictive batch-processing memory scheduling strategy for a CPU-GPU heterogeneous system

Author(s):  Juan FANG, Sheng LIN, Huijing YANG, Yixiang XU, Xing SU

Affiliation(s):  Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China

Corresponding email(s):   fangjuan@bjut.edu.cn, lins@emails.bjut.edu.cn, yangkx@emails.bjut.edu.cn, xuyx@emails.bjut.edu.cn, suxing@bjut.edu.cn

Key Words:  CPU-GPU heterogeneous, Multi-core, Unified memory, Access scheduling

Juan FANG, Sheng LIN, Huijing YANG, Yixiang XU, Xing SU. A perceptual and predictive batch-processing memory scheduling strategy for a CPU-GPU heterogeneous system[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(7): 994-1006.

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A1 - Juan FANG
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When multiple central processing unit (CPU) cores and integrated graphics processing units (GPUs) share off-chip main memory, CPU and GPU applications compete for the critical memory resource. This causes serious resource competition and has a negative impact on the overall performance of the system. We describe the competition for shared-memory resources in a CPU-GPU heterogeneous multi-core architecture, and a shared-memory request scheduling strategy based on perceptual and predictive batch-processing is proposed. By sensing the CPU and GPU memory request conditions in the request buffer, the proposed scheduling strategy estimates the GPU latency tolerance and reduces mutual interference between CPU and GPU by processing CPU or GPU memory requests in batches. According to the simulation results, the scheduling strategy improves CPU performance by 8.53% and reduces mutual interference by 10.38% with low hardware complexity.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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