一句话：CEMU 是软件化计算存储驱动全系统仿真平台，通过虚拟机冻结与延迟建模实现高可配置、高保真。
现有计算存储驱动（CSD）研究平台碎片化，硬件与仿真器受限于物理计算资源，而模拟器又缺乏全系统保真度。CEMU 是一个软件化 CSD 仿真平台，由 CSD 设备仿真器和面向 CSD 的软件栈组成。它通过新颖的虚拟机冻结机制实现高可配置性，并让 CSD 利用主机 CPU 执行真实计算以保留全系统行为，同时单独建模计算延迟，从而仿真具有 CPU 无界高算力的 CSD。软件栈遵循近期行业 CSD 标准，并通过新开发的文件系统 FDMFS 兼容现有 I/O 栈。作者与真实 CSD 硬件进行基准对比，端到端性能平均准确率达 95% 以上，并通过大语言模型训练和 LevelDB 两个案例研究证明其有效性及发现新见解的能力。
Computational storage drives (CSDs) present a promising approach to improve system performance through near data processing in SSDs. However, current research platforms are fragmented and inadequate to explore the full design space of CSD systems. Existing hardware and emulator platforms are constrained by physical compute resources, while simulators lack full-system fidelity. To address the problems, we introduce CEMU, a new software-based CSD emulation platform that enables full-system research. It consists of a CSD device emulator and a CSD-oriented software stack. Through a novel virtual machine freezing mechanism, CSD emulation achieves high configurability. While the CSD can utilize the host CPU to physically perform computation to preserve full-system behaviors, the computational delay can be modeled separately to emulate CSDs with CPU-unbounded high computing power. The software stack is designed with two principles, adhering to recent industry CSD standards and being compatible with the existing I/O stack, which is achieved via a newly developed file system FDMFS. We verify CEMU's emulation fidelity across a range of applications by benchmarking against actual CSD hardware, demonstrating average end-to-end performance accuracy of 95% or higher. We also use two case studies on large language model training and LevelDB to demonstrate that CEMU is effective in exploring CSD system research and can uncover insights that have not been discovered in previous research platforms.
@inproceedings{ZhangWZX0W0026,
title = {CEMU: Enabling Full-System Emulation of Computational Storage Beyond Hardware Limits},
author = {Qiuyang Zhang and Jiapin Wang and You Zhou and Peng Xu and Kai Lu and Jiguang Wan and Fei Wu and Tao Lu},
booktitle = {ASPLOS},
year = {2026},
doi = {10.1145/3779212.3790137}
}