Phoenix: A Refactored I/O Stack for GPU Direct Storage without Phony Buffers
Jianqin Yan, Shi Qiu, Yina Lv, Yifan Hu, Hao Chen, Zhirong Shen, Xin Yao, Renhai Chen, Jiwu Shu, Gong Zhang, Yiming Zhang
2025年份
3被引次数
1顶会引用
摘要
GPU Direct Storage (GDS) plays a vital role in GPU-based training and inference systems, leveraging Peer-to-Peer Direct Memory Access (P2P-DMA) to establish a direct data transfer path between the GPU and the storage device. The direct I/O path reduces GPU storage access latency and CPU overhead, thus improving the efficiency of data transfer. Currently, however, GDS employs a phony buffer in the host memory to interact with the Linux kernel, which results in suboptimal I/O performance, extra resource consumption, and high deployment complexity.
问问这篇 Paper
问问你的智能体。
Lune 读过与它相关的顶会 Paper,每个回答都会注明依据哪几篇。
引用它的顶会 Paper1
问问它们各自怎么用它相关 Paper
- Managing Scalable Direct Storage Accesses for GPUs with GoFSShaobo Li, Yirui Eric Zhou, Yuqi Xue, Yuan Xu 等SOSP 2025
- Efficient Multi-GPU Shared Memory via Automatic Optimization of Fine-Grained TransfersHarini Muthukrishnan, David W. Nellans, Daniel Lustig, Jeffrey A. Fessler 等ISCA 2021 · 被引用 16 次
- <u>G</u>PU <u>i</u>nitiated <u>O</u>penSHMEM: correct and efficient intra-kernel networking for dGPUsKhaled Hamidouche, Michael LeBeanePPoPP 2020 · 被引用 17 次
- ARK: GPU-driven Code Execution for Distributed Deep LearningChangho Hwang, KyoungSoo Park, Ran Shu, Xinyuan Qu 等NSDI 2023 · 被引用 22 次
- RoPeerTo: A Datacenter-Scale Architecture for Peer-To-Peer DMA between GPUs and FPGAsMarco Venere, Giuseppe Sorrentino, Benjamin Ramhorst, Maximilian Jakob Heer 等EuroSys 2026 · 被引用 1 次
