cPMEM: A Design of Byte-Addressable Persistent Memory with Compute Express Link for Data Center Applications
Persistent memory has emerged as a groundbreaking solution for byte-addressable storage-class memory (SCM). While persistent memory is highly desirable due to its advantages in high capacity, performance, and cost-effectiveness, novel system architecture solutions are required for its adoption in data center applications that demand high performance and reliability. The major challenges are issues related to persistent memory media, such as endurance, retention, and reliability, along with hardware and software system integration within the data center platform. To reduce the total service cost for end users by utilizing persistent memory media as the main memory, we have developed a comprehensive system comprising persistent memory chips, a persistent memory controller, firmware, and system software layers that effectively mitigate persistent memory issues and are compatible with data center applications. In this paper, we discuss the system design of a high-performance byte-addressable persistent memory with a Compute Express Link (CXL) interface. This design choice was based on real-world persistent memory limitations. It was implemented on Application-Specific Integrated Circuit (ASIC) platform, and tested on in-memory database (IMDB) applications. Bandwidth and latency results show that our design approach for CXL persistent memory offers comparable performance to CXL dynamic random access memory (DRAM) under certain test cases while providing increased memory capacity and reduced cost.
Authors
- Gaocong Liu
- Mengting Lu (ORCID: https://orcid.org/0000-0003-2333-2123)
- Shu Li (ORCID: https://orcid.org/0000-0002-7215-7619)
- Kun Wang (ORCID: https://orcid.org/0000-0002-5206-5311)
- Feng Zhu (ORCID: https://orcid.org/0009-0008-1119-3422)
Institutions
- Alibaba Group (China) (CN)
Publication Details
- Journal
- Proceedings of the ACM on Measurement and Analysis of Computing Systems
- Published
- 2026-05-29
- DOI
- https://doi.org/10.1145/3805632
- Primary Topic
- Parallel Computing and Optimization Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00