EDBT 2026 Demo / reviewers in the wild / expert
Yanfeng Gao
dblp:60/11288
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 since 2021
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.
| Software engineering, system software, and programming languages
1 paper |
Compilers and program optimization · 50% Operating systems · 50% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Memory systems · 50% Storage systems · 50% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Compilers and program optimization › compiler construction
compiler support for persistent memory |
0.9 | 1 | 2025 | HybridPersist: A Compiler Support for User-Friendly and Efficient PM Programming · Proc. ACM Program. Lang. 2025 |
Operating systems › persistence
crash consistency |
0.9 | 1 | 2025 | HybridPersist: A Compiler Support for User-Friendly and Efficient PM Programming · Proc. ACM Program. Lang. 2025 |
Storage systems
crash consistency |
0.9 | 1 | 2025 | HybridPersist: A Compiler Support for User-Friendly and Efficient PM Programming · Proc. ACM Program. Lang. 2025 |
Memory systems › non-volatile memory
persistent memory |
0.9 | 1 | 2025 | HybridPersist: A Compiler Support for User-Friendly and Efficient PM Programming · Proc. ACM Program. Lang. 2025 |
Methods — techniques the papers use, named apart from their topics
static analysis · 1.7program annotations · 0.9program annotation · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | HybridPersist: A Compiler Support for User-Friendly and Efficient PM ProgrammingabstractPersistent memory (PM), with its data persistence, has found widespread applications. However, programmers have to manually annotate PM operations in programming to achieve crash consistency, which is labor-intensive and error-prone. In this paper, to alleviate the burden of programming PM applications, we develop HybridPersist, a compiler support for user-friendly and efficient PM programming. On the one hand, HybridPersist automatically achieves crash consistency, minimally intruding on programmers with negligible annotations. On the other hand, it enhances both performance and correctness of PM programs through a series of dedicated analysis passes. The evaluations on well-known benchmarks validate that HybridPersist offers superior programming productivity and runtime performance compared to the state-of-the-art. Yiyu Zhang, Yanfeng Gao, Xuandong Li, Zhiqiang Zuo 0002 |
Proc. ACM Program. Lang. | 3 |