FusionFS: A Contention-Resilient File System for Persistent CPU Caches

Byte-addressable storage (BAS), such as persistent memory and CXL-SSDs, does not meet system designers’ expectations for data flushing and access granularity. Persistent CPU caches, enabled by recent techniques like Intel’s eADR and CXL’s Global Persistent Flush, can mitigate these issues without sacrificing consistency. However, the shared nature of CPU caches can lead to cache contention, which can result in cached data being frequently evicted to the BAS and reloaded into caches, negating the benefits of caching. If the BAS write granularity is larger than the cacheline eviction granularity, this can also lead to severe write amplification. In this article, we identify, characterize, and propose solutions to the problem of contention in persistent CPU caches, which is largely overlooked by existing systems. These systems either simply assume that cached data is hot enough to survive cache evictions or use unsupported cache allocation techniques without testing their effectiveness. We also present FusionFS, a contention-resilient kernel file system that uses persistent CPU caches to redesign data update approaches. FusionFS employs an adaptive data update approach that chooses the most effective mechanism based on file access patterns during system calls and memory mapping accesses, minimizing BAS media writes and improving throughput. FusionFS also employs contention-aware cache allocation to minimize various types of cache contention. Experimental results show that FusionFS outperforms existing file systems and effectively mitigates various types of cache contention.

Authors

Institutions

Publication Details

Journal
ACM Transactions on Architecture and Code Optimization
Published
2025-02-26
DOI
https://doi.org/10.1145/3719656
Citations
1
Primary Topic
Advanced Data Storage Technologies
Type
article
Field-Weighted Citation Impact
1.49

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

FusionFS: A Contention-Resilient File System for Persistent CPU Caches

Shengan Zheng, Congyong Chen, Linpeng Huang, Yuhang Zhang
1 citations
ACM Transactions on Architecture and Code Optimization
Advanced Data Storage Technologies
1.49
article

FusionFS: A Contention-Resilient File System for Persistent CPU Caches

Shengan Zheng, Congyong Chen, Linpeng Huang, Yuhang Zhang
article en
1 citations

Abstract

Byte-addressable storage (BAS), such as persistent memory and CXL-SSDs, does not meet system designers’ expectations for data flushing and access granularity. Persistent CPU caches, enabled by recent techniques like Intel’s eADR and CXL’s Global Persistent Flush, can mitigate these issues without sacrificing consistency. However, the shared nature of CPU caches can lead to cache contention, which can result in cached data being frequently evicted to the BAS and reloaded into caches, negating the benefits of caching. If the BAS write granularity is larger than the cacheline eviction granularity, this can also lead to severe write amplification. In this article, we identify, characterize, and propose solutions to the problem of contention in persistent CPU caches, which is largely overlooked by existing systems. These systems either simply assume that cached data is hot enough to survive cache evictions or use unsupported cache allocation techniques without testing their effectiveness. We also present FusionFS, a contention-resilient kernel file system that uses persistent CPU caches to redesign data update approaches. FusionFS employs an adaptive data update approach that chooses the most effective mechanism based on file access patterns during system calls and memory mapping accesses, minimizing BAS media writes and improving throughput. FusionFS also employs contention-aware cache allocation to minimize various types of cache contention. Experimental results show that FusionFS outperforms existing file systems and effectively mitigates various types of cache contention.

ACM Transactions on Architecture and Code OptimizationVol. 22(2)
Shanghai Jiao Tong University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 20%
Advanced Data Storage Technologies
1.49
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.