CXL Memory Performance for In-Memory Data Processing
Marcel Weisgut, Daniel Ritter, Pinar Tözün, Lawrence Benson, Tilmann Rabl
Abstract
The Compute Express Link (CXL) standard enables new forms of memory management and access across devices and servers. Based on PCIe, it enables cache-coherent access to remote memory. This widens the design space for database systems by expanding the available memory beyond memory local to the CPU. Efficiently utilizing CXL-attached memory requires conscious decisions by data systems about data placement and management. In this paper, we provide an in-depth analysis of database operation performance with data interleaved across multiple CXL memory devices. We experimentally evaluate the memory access performance for basic access patterns, the performance impact of placing data across multiple CXL memory devices for in-memory column scans and in-memory B+tree operations, and the performance impact of placing data in CXL memory for an in-memory database system when running the analytical TPC-H workload. Our experiments show that access to CXL-attached memory does not have to penalize performance over local access, but careful workload-aware data management is required. Our TPC-H evaluation shows that placing table columns based on access frequencies allows storing over 80% of the table data in CXL memory with a performance of 85% of a local-memory-only solution.
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Install the CLIlune papers fulltext 18725cc8-fb80-42b1-ad9f-cd2eeb5dc5bcCited by top-tier papers4
- Octopus: Enhancing CXL Memory Pods via Sparse TopologyYuhong Zhong, Fiodar Kazhamiaka, Pantea Zardoshti, Shuwei Teng et al.NSDI 2026 · 15 citations
- SIDLE: Tree-structure Aware Indexes for CXL-based Heterogeneous MemoryHaoru Zhao, Mingkai Dong, Fangnuo Wu, Haibo ChenVLDB 2026 · 1 citation
- Performance Predictability in Heterogeneous MemoryJinshu Liu, Hanchen Xu, Daniel S. Berger, Marcos K. Aguilera et al.ASPLOS 2026 · 1 citation
- MAC: Metadata Acceleration for Sustainable Performance in Big-Data Systems with CXL DRAMDusol Lee, Yan Sun, Houxiang Ji, Vinit Gupta et al.OSDI 2026
Builds on21
- An Empirical Guide to the Behavior and Use of Scalable Persistent MemoryJian Yang, Juno Kim, Morteza Hoseinzadeh, Joseph Izraelevitz et al.FAST 2020 · 470 citations
- Pond: CXL-Based Memory Pooling Systems for Cloud PlatformsHuaicheng Li, Daniel S. Berger, Lisa Hsu, Daniel Ernst et al.ASPLOS 2023 · 328 citations
- TPP: Transparent Page Placement for CXL-Enabled Tiered-MemoryHasan Al Maruf, Hao Wang, Abhishek Dhanotia, Johannes Weiner et al.ASPLOS 2023 · 255 citations
- Demystifying CXL Memory with Genuine CXL-Ready Systems and DevicesYan Sun, Yifan Yuan, Zeduo Yu, Reese Kuper et al.MICRO 2023 · 133 citations
- TMO: transparent memory offloading in datacentersJohannes Weiner, Niket Agarwal, Dan Schatzberg, Leon Yang et al.ASPLOS 2022 · 103 citations
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- CXLMC: Model Checking CXL Shared Memory ProgramsSimon Guo, Conan Truong, Brian DemskyASPLOS 2026 · 1 citation
- CTXNL: A Software-Hardware Co-designed Solution for Efficient CXL-Based Transaction ProcessingZhao Wang, Yiqi Chen, Cong Li, Yijin Guan et al.ASPLOS 2025 · 9 citations
- Tigon: A Distributed Database for a CXL PodYibo Huang, Haowei Chen, Newton Ni, Yan Sun et al.OSDI 2025 · 12 citations
- CXL and the Return of Scale-Up Database EnginesAlberto Lerner, Gustavo AlonsoVLDB 2024 · 34 citations
- Exploring Performance and Cost Optimization with ASIC-Based CXL MemoryYupeng Tang, Ping Zhou, Wenhui Zhang, Henry Hu et al.EuroSys 2024 · 40 citations
