<u>G</u>PU <u>i</u>nitiated <u>O</u>penSHMEM: correct and efficient intra-kernel networking for dGPUs
Khaled Hamidouche, Michael LeBeane
2020Year
17Citations
Abstract
Current state-of-the-art in GPU networking utilizes a host-centric, kernel-boundary communication model that reduces performance and increases code complexity. To address these concerns, recent works have explored performing network operations from within a GPU kernel itself. However, these approaches typically involve the CPU in the critical path, which leads to high latency and inefficient utilization of network and/or GPU resources.
Ask about this paper
Ask your agent about it.
Lune has read the top-tier papers around this one, so every answer names the papers it rests on.
Your agent calls
Lunesearch_papers
Free to start. No credit card required.
Terminal
Install the CLIlune papers get b1e8c604-6eeb-4dbe-b243-e45f01870f6bRelated papers
- High-throughput and Flexible Host Networking for Accelerated ComputingAthinagoras Skiadopoulos, Zhiqiang Xie, Mark Zhao, Qizhe Cai et al.OSDI 2024 · 11 citations
- Efficient Data Passing for Serverless Inference Workflows: A GPU-Centric ApproachHao Wu, Yaochen Liu, Minchen Yu, Qizhen Weng et al.EuroSys 2026
- GPU-Ether: GPU-native Packet I/O for GPU Applications on Commodity EthernetChangue Jung, Suhwan Kim, Ikjun Yeom, Honguk Woo et al.INFOCOM 2021 · 7 citations
- ARK: GPU-driven Code Execution for Distributed Deep LearningChangho Hwang, KyoungSoo Park, Ran Shu, Xinyuan Qu et al.NSDI 2023 · 22 citations
- FpgaNIC: An FPGA-based Versatile 100Gb SmartNIC for GPUsZeke Wang, Hongjing Huang, Jie Zhang, Fei Wu et al.USENIX ATC 2022 · 58 citations
