VLDB 2026 Research / reviewers in the wild / expert
Hongxu Xu
dblp:415/4669
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 since 2021Software 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 · 67% Program synthesis and code generation · 33% | |
| Artificial intelligence
1 paper |
Language models and text generation · 100% |
Topics — the 3 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Compilers and program optimization › compiler optimization
missed optimization detection |
1.0 | 1 | 2026 | LPO: Discovering Missed Peephole Optimizations with Large Language Models · ASPLOS (2) 2026 |
Compilers and program optimization › compiler optimization › local optimization
peephole optimization |
1.0 | 1 | 2026 | LPO: Discovering Missed Peephole Optimizations with Large Language Models · ASPLOS (2) 2026 |
Natural language and speech › Language models and text generation
code analysis |
0.3 | 1 | 2026 | LPO: Discovering Missed Peephole Optimizations with Large Language Models · ASPLOS (2) 2026 |
Methods — techniques the papers use, named apart from their topics
peephole pattern mining · 2.0large language model prompting · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | LPO: Discovering Missed Peephole Optimizations with Large Language ModelsabstractPeephole optimization is an essential class of compiler optimizations that targets small, inefficient instruction sequences within programs. By replacing such suboptimal instructions with refined and more optimal sequences, these optimizations not only directly optimize code size and performance, but also enable more transformations in the subsequent optimization pipeline. Despite their importance, discovering new and effective peephole optimizations remains challenging due to the complexity and breadth of instruction sets. Prior approaches either lack scalability or have significant restrictions on the peephole optimizations that they can find. Hongxu Xu, Yongqiang Tian 0001, Xintong Zhou, Chengnian Sun |
ASPLOS (2) | 2 |