Predicting Compilation Resources for Adaptive Build in an Industrial Setting

作者：Junhao Hu, Chaozheng Wang, Hailiang Huang, Huang Luo, Yu Jin, Yuetang Deng, Tao Xie · 年份：2023 · DOI：10.1109/ase56229.2023.00128 · 被引用次数：1 · 研究领域：Cloud Computing and Resource Management、Advanced Data Storage Technologies、Distributed and Parallel Computing Systems

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.