Presentation
A Probabilistic Approach to Selecting Build Configurations in Package Managers
DescriptionModern scientific software in high performance computing is often complex, and many parallel applications and libraries depend on several other software or libraries. Developers and users of such complex software often use package managers for building them. Package managers depend on humans to codify package constraints, and the dependency graph of a software package can often become large. In this paper, we propose a methodology that uses historical build results to assist a package manager in selecting the best versions of package dependencies with an aim to improve the likelihood of a successful build. We train a machine learning (ML) model to predict the probability of build outcomes of different configurations of packages in the Spack package manager. When evaluated on common scientific software stacks, this ML model-based approach is able to achieve a 13% higher success rate in building packages than the default version selection mechanism in Spack.
