Graduation Semester and Year
Spring 2025
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Computer Engineering
Department
Computer Science and Engineering
First Advisor
Jia Rao
Second Advisor
Hong Jiang
Third Advisor
Song Jiang
Fourth Advisor
Hui Lu
Abstract
The rapid advancement of memory technologies presents new challenges and opportunities for system software and architectural design. This dissertation investigates how to optimize modern computing systems for next-generation memory, mostly focusing on persistent memory and Compute Express Link (CXL)-based memory. First, we conduct a detailed characterization of Intel Optane DC Persistent Memory, identifying the distinct behaviors of its on-DIMM read and write buffers and analyzing their impact on application performance. These insights motivate optimizations that decouple read and write paths, revealing that random read latency—especially in pointer-chasing workloads—is a dominant performance bottleneck. Second, we present NOMAD, a page management framework that enables non-exclusive memory tiering, allowing a page to temporarily exist in both fast and slow memory. NOMAD introduces transactional page migration to remove migration from the critical path and abort in-progress migrations when pages are dirtied. This design mitigates memory thrashing and improves efficiency under memory pressure. Implemented in Linux, NOMAD achieves up to 6× performance improvement over the default Linux memory manager and outperforms recent hardware-assisted tiering approaches. Finally, we propose FileShim, a POSIX-like file caching framework that facilitates efficient and consistent file sharing across isolated environments using a shared memory window and software-managed coherence. FileShim delivers up to 3× better write throughput than NFS, supports lock-free concurrent access, and makes strong file-level consistency in containerized environments. Through this work, we identify inefficiencies in current memory management approaches and highlight the need for further research to develop efficient, scalable strategies that can fully leverage emerging memory technologies.
Keywords
Disaggregated memory, Memory management, Distributed System, Operating System
Disciplines
Computer and Systems Architecture | Data Storage Systems | Hardware Systems
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Xiang, Lingfeng, "Optimizing Architecture and Software for Next-Generation Memory Systems" (2025). Computer Science and Engineering Dissertations. 406.
https://mavmatrix.uta.edu/cse_dissertations/406
Included in
Computer and Systems Architecture Commons, Data Storage Systems Commons, Hardware Systems Commons