Hongxu Xu

dblp:415/4669 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Compilers and program optimization › compiler optimization
missed optimization detection
1.012026
LPO: Discovering Missed Peephole Optimizations with Large Language Models · ASPLOS (2) 2026
Compilers and program optimization › compiler optimization › local optimization
peephole optimization
1.012026
LPO: Discovering Missed Peephole Optimizations with Large Language Models · ASPLOS (2) 2026
Natural language and speech › Language models and text generation
code analysis
0.312026
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
YearPublicationVenuePosition
2026 LPO: Discovering Missed Peephole Optimizations with Large Language Models
abstract
Peephole 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