CXL and the Return of Scale-Up Database Engines
Alberto Lerner, Gustavo Alonso
摘要
The trend toward specialized processing devices such as TPUs, DPUs, GPUs, and FPGAs has exposed the weaknesses of PCIe in interconnecting these devices and their hosts. Several attempts have been proposed to improve, augment, or downright replace PCIe, and more recently, these efforts have converged into a standard called Compute Express Link (CXL). CXL is already on version 2.0 in terms of commercial availability, but its potential to radically change the conventional server architecture has only just started to surface. For example, CXL can increase the bandwidth and quantity of memory available to any single machine beyond what that machine can originally provide, most importantly, in a manner that is fully transparent to software applications.
We argue, however, that CXL can have a broader impact beyond memory expansion and deeply affect the architecture of data-intensive systems. In a nutshell, while the cloud favored scale-out approaches that grew in capacity by adding full servers to a rack, CXL brings back scale-up architectures that can grow by fine-tuning individual resources, all while transforming the rack into a large shared-memory machine. In this paper, we describe why such architectural transformations are now possible, how they benefit emerging heterogeneous hardware platforms for data-intensive systems, and the associated research challenges.
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引用它的顶会 Paper6
- FlexMem: Adaptive Page Profiling and Migration for Tiered MemoryDong Xu, Junhee Ryu, Kwangsik Shin, Pengfei Su 等USENIX ATC 2024 · 被引用 41 次
- Tigon: A Distributed Database for a CXL PodYibo Huang, Haowei Chen, Newton Ni, Yan Sun 等OSDI 2025 · 被引用 12 次
- A Programming Model for Disaggregated Memory over CXLGal Assa, Moritz Lumme, Lucas Bürgi, Michal Friedman 等ASPLOS 2026 · 被引用 4 次
- Oasis: Pooling PCIe Devices Over CXL to Boost UtilizationYuhong Zhong, Daniel S. Berger, Pantea Zardoshti, Enrique Saurez 等SOSP 2025 · 被引用 2 次
- SIDLE: Tree-structure Aware Indexes for CXL-based Heterogeneous MemoryHaoru Zhao, Mingkai Dong, Fangnuo Wu, Haibo ChenVLDB 2026 · 被引用 1 次
它引用的顶会 Paper6
- 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
- Demystifying CXL Memory with Genuine CXL-Ready Systems and DevicesYan Sun, Yifan Yuan, Zeduo Yu, Reese Kuper 等MICRO 2023 · 被引用 133 次
- Pump Up the Volume: Processing Large Data on GPUs with Fast InterconnectsClemens Lutz, Sebastian Breß, Steffen Zeuch, Tilmann Rabl 等SIGMOD 2020 · 被引用 99 次
- The Case for Distributed Shared-Memory Databases with RDMA-Enabled Memory DisaggregationRuihong Wang, Jianguo Wang, Stratos Idreos, M. Tamer Özsu 等VLDB 2023 · 被引用 49 次
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