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Optimizing distributed build time in large industrial build cluster
The Hong Kong University of Science and Technology
Department of Computer Science and Engineering
MPhil Thesis Defence
Title: "Optimizing distributed build time in large industrial build cluster"
By
Mr. Bangyan DU
Abstract:
Large IT companies encounter significant compilation demands due to their
extensive codebases, particularly when managing and hosting them in a
multirepo approach. For instance, WeChat has over 1,500 code repositories
totaling more than 800 GB, covering various services. With more than 800
frontline developers making over 4,000 code sub- missions daily, the average
compilation demand exceeds 20,000 requests. Distributed build clusters
encounter two main challenges: resource scheduling and the absence of dis-
tributed preprocessing. Firstly, effective resource scheduling is crucial
for optimizing the use of existing cluster to compile more C++ files
quickly. The second challenge stems from the lack of distributed
preprocessing in a build cluster, primarily due to the high costs associated
with uploading all related header files from the local master machine to the
re- mote slave machines for a given C++ source file. To address the first
issue, we propose a resource prediction model that estimates the compilation
resources required for each C++ source file. This model facilitates the
allocation of adequate remote slave machines and helps eliminate typical
errors like Out of Memory errors and Deadline Exceeded errors in the build
cluster. For the second issue, we present a two-stage distributed
preprocessing solution and further minimize the huge number of system calls
of GCC C++ compiler to help reduce the preprocessing time on the local
master machine. Our experience report not only sheds light on WeChat’s build
cluster but also offers insights into effective practices of build time
reduction for large-scale IT companies.
Date: Tuesday, 27 May 2025
Time: 1:00pm - 3:00pm
Venue: Room 2128B
Lift 19
Chairman: Dr. Shuai WANG
Committee Members: Prof. Charles ZHANG (Supervisor)
Dr. Ziyi LIU