EDBT 2026 Demo / reviewers in the wild / expert
Congyong Chen
dblp:271/8361
· DBLP profile ↗
3ranked-venue papers
2as first author
2since 2021 · last 2025
0000-0003-1682-7435ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1 · 1 first-author · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Storage systems · 72% Memory systems · 28% | |
| Computer graphics and multimedia
1 paper |
Image and video processing · 100% |
Topics — the 7 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Memory systems › memory interference
cache contention |
0.9 | 1 | 2025 | FusionFS: A Contention-Resilient File System for Persistent CPU Caches · ACM Trans. Archit. Code Optim. 2025 |
Storage systems
file systems |
0.9 | 1 | 2025 | FusionFS: A Contention-Resilient File System for Persistent CPU Caches · ACM Trans. Archit. Code Optim. 2025 |
Storage systems › file systems › file system design
persistent memory file system |
0.9 | 1 | 2025 | FusionFS: A Contention-Resilient File System for Persistent CPU Caches · ACM Trans. Archit. Code Optim. 2025 |
Image and video processing › video enhancement
compressed video quality enhancement |
0.4 | 1 | 2020 | Multi-level Wavelet-Based Generative Adversarial Network for Perceptual Quality Enhancement of Compressed Video · ECCV (14) 2020 |
Image and video processing › image enhancement
perceptual quality enhancement |
0.4 | 1 | 2020 | Multi-level Wavelet-Based Generative Adversarial Network for Perceptual Quality Enhancement of Compressed Video · ECCV (14) 2020 |
Storage systems › non-volatile memory storage
byte-addressable storage |
0.3 | 1 | 2025 | FusionFS: A Contention-Resilient File System for Persistent CPU Caches · ACM Trans. Archit. Code Optim. 2025 |
Storage systems › flash and SSD › flash memory management › garbage collection
write amplification |
0.3 | 1 | 2025 | FusionFS: A Contention-Resilient File System for Persistent CPU Caches · ACM Trans. Archit. Code Optim. 2025 |
Methods — techniques the papers use, named apart from their topics
contention-aware cache allocation · 0.9adaptive data update · 0.9wavelet transform · 0.4generative adversarial network · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | FusionFS: A Contention-Resilient File System for Persistent CPU CachesabstractByte-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. Congyong Chen, Shengan Zheng, Yuhang Zhang 0027, Linpeng Huang |
ACM Trans. Archit. Code Optim. | 1 |
| 2024 | Redesigning Data and Metadata Updates in PM File Systems with Persistent CPU Caches
Congyong Chen, Shengan Zheng, Yuhang Zhang 0027, Linpeng Huang |
DASFAA (6) | 1 |
| 2020 | Multi-level Wavelet-Based Generative Adversarial Network for Perceptual Quality Enhancement of Compressed Video
Jianyi Wang, Xin Deng 0002, Mai Xu, Congyong Chen, Yuhang Song 0001 |
ECCV (14) | 4 |