Demystifying CXL Memory with Genuine CXL-Ready Systems and Devices
Yan Sun, Yifan Yuan, Zeduo Yu, Reese Kuper, Chihun Song, Jinghan Huang, Houxiang Ji, Siddharth Agarwal, Jiaqi Lou, Ipoom Jeong, Ren Wang, Jung Ho Ahn
摘要
The ever-growing demands for memory with larger capacity and higher bandwidth have driven recent innovations on memory expansion and disaggregation technologies based on Compute eXpress Link (CXL). Especially, CXL-based memory expansion technology has recently gained notable attention for its ability not only to economically expand memory capacity and bandwidth but also to decouple memory technologies from a specific memory interface of the CPU. However, since CXL memory devices have not been widely available, they have been emulated using DDR memory in a remote NUMA node. In this paper, for the first time, we comprehensively evaluate a true CXL-ready system based on the latest 4th-generation Intel Xeon CPU with three CXL memory devices from different manufacturers. Specifically, we run a set of microbenchmarks not only to compare the performance of true CXL memory with that of emulated CXL memory but also to analyze the complex interplay between the CPU and CXL memory in depth. This reveals important differences between emulated CXL memory and true CXL memory, some of which will compel researchers to revisit the analyses and proposals from recent work. Next, we identify opportunities for memory-bandwidth-intensive applications to benefit from the use of CXL memory. Lastly, we propose a CXL-memory-aware dynamic page allocation policy, Caption to more efficiently use CXL memory as a bandwidth expander. We demonstrate that Caption can automatically converge to an empirically favorable percentage of pages allocated to CXL memory, which improves the performance of memory-bandwidth-intensive applications by up to 24% when compared to the default page allocation policy designed for traditional NUMA systems.
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引用它的顶会 Paper63
- Nomad: Non-Exclusive Memory Tiering via Transactional Page MigrationLingfeng Xiang, Zhen Lin, Weishu Deng, Hui Lu 等OSDI 2024 · 被引用 59 次
- Systematic CXL Memory Characterization and Performance Analysis at ScaleJinshu Liu, Hamid Hadian, Yuyue Wang, Daniel S. Berger 等ASPLOS 2025 · 被引用 47 次
- PIM Is All You Need: A CXL-Enabled GPU-Free System for Large Language Model InferenceYufeng Gu, Alireza Khadem, Sumanth Umesh, Ning Liang 等ASPLOS 2025 · 被引用 44 次
- HydraRPC: RPC in the CXL EraTeng Ma, Zheng Liu, Chengkun Wei, Jialiang Huang 等USENIX ATC 2024 · 被引用 39 次
- Partial Failure Resilient Memory Management System for (CXL-based) Distributed Shared MemoryMingxing Zhang, Teng Ma, Jinqi Hua, Zheng Liu 等SOSP 2023 · 被引用 38 次
它引用的顶会 Paper27
- An Empirical Guide to the Behavior and Use of Scalable Persistent MemoryJian Yang, Juno Kim, Morteza Hoseinzadeh, Joseph Izraelevitz 等FAST 2020 · 被引用 470 次
- Pond: CXL-Based Memory Pooling Systems for Cloud PlatformsHuaicheng Li, Daniel S. Berger, Lisa Hsu, Daniel Ernst 等ASPLOS 2023 · 被引用 328 次
- TPP: Transparent Page Placement for CXL-Enabled Tiered-MemoryHasan Al Maruf, Hao Wang, Abhishek Dhanotia, Johannes Weiner 等ASPLOS 2023 · 被引用 255 次
- RecNMP: Accelerating Personalized Recommendation with Near-Memory ProcessingLiu Ke, Udit Gupta, Benjamin Youngjae Cho, David Brooks 等ISCA 2020 · 被引用 235 次
- MatrixKV: Reducing Write Stalls and Write Amplification in LSM-tree Based KV Stores with Matrix Container in NVMTing Yao, Yiwen Zhang, Jiguang Wan, Qiu Cui 等USENIX ATC 2020 · 被引用 186 次
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