TPP: Transparent Page Placement for CXL-Enabled Tiered-Memory
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Abstract (arXiv)
The increasing demand for memory in hyperscale applications has led to memory becoming a large portion of the overall datacenter spend. The emergence of coherent interfaces like CXL enables main memory expansion and offers an efficient solution to this problem. In such systems, the main memory can constitute different memory technologies with varied characteristics. In this paper, we characterize memory usage patterns of a wide range of datacenter applications across the server fleet of Meta. We, therefore, demonstrate the opportunities to offload colder pages to slower memory tiers for these applications. Without efficient memory management, however, such systems can significantly degrade performance. We propose a novel OS-level application-transparent page placement mechanism (TPP) for CXL-enabled memory. TPP employs a lightweight mechanism to identify and place hot/cold pages to appropriate memory tiers. It enables a proactive page demotion from local memory to CXL-Memory. This technique ensures a memory headroom for new page allocations that are often related to request processing and tend to be short-lived and hot. At the same time, TPP can promptly promote performance-critical hot pages trapped in the slow CXL-Memory to the fast local memory, while minimizing both sampling overhead and unnecessary migrations. TPP works transparently without any application-specific knowledge and can be deployed globally as a kernel release. We evaluate TPP in the production server fleet with early samples of new x86 CPUs with CXL 1.1 support. TPP makes a tiered memory system performant as an ideal baseline (<1% gap) that has all the memory in the local tier. It is 18% better than today's Linux, and 5-17% better than existing solutions including NUMA Balancing and AutoTiering. Most of the TPP patches have been merged in the Linux v5.18 release.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Jun 2022.
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Citing papers per year: 2022: 2; 2023: 0; 2024: 2; 2025: 2; 2026: 4.
10 papers.
- 2022COMET: A Comprehensive Cluster Design Methodology for Distributed Deep Learning Trainingcited by 2
- 2024Herding LLaMaS: Using LLMs as an OS Modulecited by 2
- 2024PIFS-Rec: Process-In-Fabric-Switch for Large-Scale Recommendation System Inferencescited by 1
- 2025Harmonia: Enhancing Data Placement and Migration in Hybrid Storage Systems via Multi-Agent Reinforcement Learningcited by 1
- 2025Vulcan: Instance-specialized, Verifiable Systems Heuristics Through LLM-driven Searchcited by 1
- 2026A CXL Memory Rack for Multi-Turn LLM Servingcited by 1
- 2022Analysis and Optimization of GNN-Based Recommender Systems on Persistent Memorycited by 0
- 2026Topology-Aware Data Movement for Disaggregated GPU Inferencecited by 0
- 2026Heterogeneous LLM Serving with General-Purpose Processing-Near-Memory for Retrieval-Based Sparse Attentioncited 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
- 2026Heterogeneous LLM Serving with General-Purpose Processing-Near-Memory for Retrieval-Based Sparse Attention4 Aug 2026
- 2026A CXL Memory Rack for Multi-Turn LLM Serving20 Jul 2026
- 2026Topology-Aware Data Movement for Disaggregated GPU Inference19 Apr 2026
- 2025Vulcan: Instance-specialized, Verifiable Systems Heuristics Through LLM-driven Search31 Dec 2025
- 2025Harmonia: Enhancing Data Placement and Migration in Hybrid Storage Systems via Multi-Agent Reinforcement Learning26 Mar 2025
- 2024PIFS-Rec: Process-In-Fabric-Switch for Large-Scale Recommendation System Inferences25 Sep 2024
- 2024Herding LLaMaS: Using LLMs as an OS Module17 Jan 2024
- 2022COMET: A Comprehensive Cluster Design Methodology for Distributed Deep Learning Training30 Nov 2022
- 2022Analysis and Optimization of GNN-Based Recommender Systems on Persistent Memory25 Jul 2022