Demystifying CXL Memory with Genuine CXL-Ready Systems and Devices
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Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Mar 2023.
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Citing papers per year: 2024: 1; 2025: 3; 2026: 2.
6 papers.
- 2025PIM Is All You Need: A CXL-Enabled GPU-Free System for Large Language Model Inferencecited by 22
- 2025Scalable Processing-Near-Memory for 1M-Token LLM Inference: CXL-Enabled KV-Cache Management Beyond GPU Limitscited by 4
- 2024PIFS-Rec: Process-In-Fabric-Switch for Large-Scale Recommendation System Inferencescited by 1
- 2025AI Compute Architecture and Evolution Trendscited by 1
- 2026From Tensor Buffer to Distributed Memory Hierarchy: A Survey of KV Cache Management for LLM Servingcited by 0
- 2026Composable CXL Memory as a Kubernetes-Native Shared Memory for LLM Servingcited by 0
- 2026Composable CXL Memory as a Kubernetes-Native Shared Memory for LLM Serving9 Sep 2026
- 2026From Tensor Buffer to Distributed Memory Hierarchy: A Survey of KV Cache Management for LLM Serving30 Jun 2026
- 2025Scalable Processing-Near-Memory for 1M-Token LLM Inference: CXL-Enabled KV-Cache Management Beyond GPU Limits31 Oct 2025
- 2025AI Compute Architecture and Evolution Trends29 Aug 2025
- 2025PIM Is All You Need: A CXL-Enabled GPU-Free System for Large Language Model Inference11 Feb 2025
- 2024PIFS-Rec: Process-In-Fabric-Switch for Large-Scale Recommendation System Inferences25 Sep 2024