HIVE: A High-Priority Victim Cache for Accelerating GPU Memory Accesses

The victim cache was originally designed as a secondary cache to handle misses in the L1 data (L1D) cache in CPUs. However, this design is often sub-optimal for GPUs. Accessing the high-latency L1D cache and its victim cache can lead to significant latency overhead, severely degrading the performance of certain applications. We introduce HIVE, a high-priority victim cache designed to accelerate GPU memory accesses. HIVE handles memory requests first, before they reach the L1D cache. Our experimental results show that HIVE achieves an average performance improvement of $\mathbf{7 7. 1 \%}$ and $\mathbf{2 1. 7 \%}$ compared to the baseline and the state-of-the-art architecture, respectively.

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Publication Details

Published
2025-06-22
DOI
https://doi.org/10.1109/dac63849.2025.11133338
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
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article

HIVE: A High-Priority Victim Cache for Accelerating GPU Memory Accesses

Jinhai Tang, Lizhou Wu, Hanqing Li, Jianmin Zhang et al.
Parallel Computing and Optimization Techniques
article

HIVE: A High-Priority Victim Cache for Accelerating GPU Memory Accesses

Jinhai Tang, Lizhou Wu, Hanqing Li, Jianmin Zhang, Tiejun Li, Sheng Ma, Shengbai Luo, Yuhan Tang
article en

Abstract

The victim cache was originally designed as a secondary cache to handle misses in the L1 data (L1D) cache in CPUs. However, this design is often sub-optimal for GPUs. Accessing the high-latency L1D cache and its victim cache can lead to significant latency overhead, severely degrading the performance of certain applications. We introduce HIVE, a high-priority victim cache designed to accelerate GPU memory accesses. HIVE handles memory requests first, before they reach the L1D cache. Our experimental results show that HIVE achieves an average performance improvement of $\mathbf{7 7. 1 \%}$ and $\mathbf{2 1. 7 \%}$ compared to the baseline and the state-of-the-art architecture, respectively.

National University of Defense Technology (CN)
Openalex Percentile: Top 73%
Parallel Computing and Optimization Techniques
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