ACRS: Adjacent Computation Resource Sharing among Partitioned GPU Sub-Cores

Modern GPUs typically segment Streaming Multiprocessors (SMs) into sub-cores (e.g. 4 sub-cores) to reduce power consumption and chip area. However, this partitioned design prevents potential task distributions across sub-cores, impairing overall execution efficiency. In this paper, we explore the pe...

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Bibliographic Details
Published in:2025 62nd ACM/IEEE Design Automation Conference (DAC) pp. 1 - 7
Main Authors: Song, Penghao, Wang, Chongxi, Han, Chenji, Zhao, Haoyu, Zhang, Tingting, Liu, Tianyi, Wang, Jian
Format: Conference Proceeding
Language:English
Published: IEEE 22.06.2025
Subjects:
Computational efficiency
Computer architecture
Graphics processing units
Hardware
Matched filters
Monitoring
Performance gain
Power demand
Resource management
Wiring
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Summary:Modern GPUs typically segment Streaming Multiprocessors (SMs) into sub-cores (e.g. 4 sub-cores) to reduce power consumption and chip area. However, this partitioned design prevents potential task distributions across sub-cores, impairing overall execution efficiency. In this paper, we explore the performance benefit of sharing hardware resources among sub-cores and identify functional units (FUs) as critical components for compute-intensive applications. Moreover, our observations reveal that instructions residing in operand collectors can be obstructed by back-end FUs, but there is a high probability that unoccupied FUs are available in adjacent sub-cores during such blockages. In response, we introduce the adjacent computation resource sharing (ACRS) framework to efficiently utilize these unoccupied units among sub-cores. ACRS has two key modules: Shared FU Issue (SF_ISSUE) and Shared FU Write Back (SF_WriteBack). SF_ISSUE monitors the status of operand collectors and functional units, and offloads instructions from blocked sub-cores to unoccupied resources. Meanwhile, SF_WriteBack routes results back to the original sub-core.To minimize wiring overhead, each sub-core is assigned a fixed target core for sharing. We design a series of matching policies and finally filter out the most effective sequential method. Evaluation results show that ACRS improves performance by up to 46.4 \%, with an average of 14.1 \% over the traditional partitioned architecture, while reducing energy consumption by 8.3 \%. Besides, ACRS achieves an additional 12.3% performance improvement compared with the SOTA method.
DOI:10.1109/DAC63849.2025.11132550