WIC: Hiding Producer-Consumer Synchronization Delays with Warp-Level Interrupt-based GPU Communications
Jiajian Zhang, Fangyu Wu, Hai Jiang, Qiufeng Wang, Genlang Chen, Chaoyi Pang
Abstract
GPU communication plays a pivotal role in collaborative computation across multiple devices. Despite advancements in inter-device communication fabrics and architectures, synchronization still remains a significant challenge due to the manual coordination required between producers and consumers at the application level. In this work, we first reveal that traditional synchronization is a primary bottleneck in GPU communication, where consumers frequently poll for producer data availability. Specifically, early-started polling leads to the unnecessary occupation of computational resources. To address this issue, we propose Warplevel Interrupt-based Communication (WIC), a novel synchronization framework for GPU communication that introduces a fine-grained interruption mechanism at the warp level to replace repetitive polling. WIC preemptively stalls warps engaged in frequent polling and releases computational resources for other warps, thereby effectively overlapping producer-consumer synchronization with ongoing computations. Comprehensive experiments demonstrate that WIC significantly outperforms conventional polling methods by 1.13× on average across various applications with diverse communication patterns.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext f98b8a2f-418f-4d93-8102-329f0c582596Builds on16
- Griffin: Hardware-Software Support for Efficient Page Migration in Multi-GPU SystemsTrinayan Baruah, Yifan Sun, Ali Tolga Dinçer, Saiful A. Mojumder et al.HPCA 2020 · 50 citations
- MGG: Accelerating Graph Neural Networks with Fine-Grained Intra-Kernel Communication-Computation Pipelining on Multi-GPU PlatformsYuke Wang, Boyuan Feng, Zheng Wang, Tong Geng et al.OSDI 2023 · 46 citations
- HMG: Extending Cache Coherence Protocols Across Modern Hierarchical Multi-GPU SystemsXiaowei Ren, Daniel Lustig, Evgeny Bolotin, Aamer Jaleel et al.HPCA 2020 · 38 citations
- Accelerating the Training of Large Language Models using Efficient Activation Rematerialization and Optimal Hybrid ParallelismTailing Yuan, Yuliang Liu, Xucheng Ye, Shenglong Zhang et al.USENIX ATC 2024 · 34 citations
- Fast and Efficient Model Serving Using Multi-GPUs with Direct-Host-AccessJinwoo Jeong, Seungsu Baek, Jeongseob AhnEuroSys 2023 · 28 citations
Related papers
- GICC: A High-Performance Runtime for GPU-Initiated Communication and Coordination in Modern HPC SystemsBaodi Shan, Mauricio Araya-Polo, Barbara M. ChapmanHPDC 2026
- MGI: A Communication Framework for Data Processing in Massive GPU InfrastructuresDi Wu, Hongshi Tan, Hanzhang Yang, Bingsheng He et al.VLDB 2026
- A Simple Cache Coherence Scheme for Integrated CPU-GPU SystemsArdhi Wiratama Baskara Yudha, Reza Pulungan, Henry Hoffmann, Yan SolihinDAC 2020 · 3 citations
- Tally: Non-Intrusive Performance Isolation for Concurrent Deep Learning WorkloadsWei Zhao, Anand Jayarajan, Gennady PekhimenkoASPLOS 2025 · 4 citations
- WASP: Exploiting GPU Pipeline Parallelism with Hardware-Accelerated Automatic Warp SpecializationNeal Clayton Crago, Sana Damani, Karthikeyan Sankaralingam, Stephen W. KecklerHPCA 2024 · 13 citations
