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Huang Luo

dblp:360/7517 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 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 2021Graphics, computer vision, multimedia, augmented reality and games · 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
Software maintenance and evolution · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Cloud and datacenter computing · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software maintenance and evolution
build systems
0.712023
Predicting Compilation Resources for Adaptive Build in an Industrial Setting · ASE 2023
Cloud and datacenter computing
cluster resource management and scheduling
0.212023
Predicting Compilation Resources for Adaptive Build in an Industrial Setting · ASE 2023

Methods — techniques the papers use, named apart from their topics

machine learning prediction · 1.3linear regression · 1.3
YearPublicationVenuePosition
2025 Learning Distinct Semantic Information in Modality-Specific and Modality-Shared Features for Visible-Infrared Person Re-identification
Min Jiang 0008, Huang Luo, Jun Kong 0001, Ming Lu 0008
PRCV (17)2
2023 Predicting Compilation Resources for Adaptive Build in an Industrial Setting
abstract
Development teams in large companies often maintain a huge codebase whose build time can be painfully long in a single machine. To reduce the build time, tools such as Bazel and distcc are used to build the code base in a distributed way. However, in the process of distributed build, certain remote slave machines can crash due to two types of errors: Out Of Memory (OOM) and Deadline Exceeded (DE) errors. These crashes lead to time-consuming rebuilds, as suffered by the WeiXin Group (WXG) of Tencent Inc. (the vendor of WeChat, a highly popular mobile app in China). Aiming to prevent these two types of errors, in this paper, we propose a new approach named PCRLINEAR, which predicts the memory and time requirements of the given C++ file, allowing the underlying distributed build system to schedule compilation resources adaptively according to the prediction results. Our experiments show that PCRLINEAR reduces the number of OOM and DE errors from 5% to 0.2% and, at the same time, achieves substantial build-performance improvement of 30% on average.
Chaozheng Wang, Hailiang Huang 0004, Huang Luo, Yuetang Deng, Tao Xie 0001
ASE4