Engineering topics

Nichols, Daniel

Publications and source records attributed to Nichols, Daniel.

3 records

DOE OSTI2026

Report of the 2026 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science

Scientific computing is undergoing rapid transformation as advances in artificial intelligence, heterogeneous computing, automation, and data-intensive research reshape not only computational tools but also the institutions, workforce models, and collaborative practices that support scientific discovery. This report synthesizes insights from the 2026 Workshop on Next-Generation Ecosystems for Scientific Computing, the second in a three-year series focused on strengthening scientific computing ecosystems through socio-technical co-design. Workshop discussions identified four interdependent strategic themes: software ecosystems for AI-enabled scientific discovery; trust, validation, and traceability; human-AI teaming and paradigm shifts; and workforce, pedagogy, and governance. The report translates these themes into eight priorities for community action spanning shared research infrastructure, trust and traceability, user experience, human-AI teaming, workforce development, cross-sector coordination, stewardship and sustainability, and evaluation of scientific value. Together, these priorities outline directions for building scientific computing ecosystems that remain trustworthy, sustainable, innovative, and resilient as AI assumes a growing role in scientific work.

AI↗

DOE OSTI2023

Learning to Predict and Improve Build Successes in Package Ecosystems

Software has become increasingly complex, with a typical application depending on tens or hundreds of packages. Finding compatible versions and build configurations of these packages is challenging. This paper presents a method to learn the likelihood of software build success, and techniques for leveraging this information to guide dependency solvers to better software configurations. We leverage the heavily parameterized package recipes from the Spack package manager to produce a training data set of builds, and we use Graph Neural Networks to learn whether a given package configuration will build successfully or not. We apply our tool to the U.S. Exascale Computing Project’s software stack. We demonstrate its effectiveness in predicting whether a given package will build successfully. We show that our technique can be used to improve the solutions generated by dependency solvers, reducing the need for developers to find working builds by trial and error.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

A Probabilistic Approach To Selecting Build Configurations in Package Managers

In the past decade software has grown significantly in complexity and scale. Likewise the number of dependencies for most software has increased with typical software packages depending on tens to hundreds of other packages. Such large numbers of dependencies place a significant burden on package managers to correctly maintain dependency lists and constraints between them. Due to this package managers have incorporated sophisticated tooling such as SAT solvers into their dependency management mechanisms. Despite these tools package managers still rely on many human annotated constraints for dependency and version selection. These are error-prone and require a significant amount of labor to constantly update and test. In this paper we propose a methodology to make use of historical build results in selecting the version for package dependencies. Our method utilizes the flexibility of the Spack package manager’s heavily parameterized package configurations to incorporate a machine learning model trained to predict the probability of build outcomes. This work is able to build and install packages with a 13% higher success rate than the default version selection mechanism in Spack.

97 MATHEMATICS AND COMPUTING↗