Scientific and engineering computing rely heavily on linear algebra for large-scale data analysis, modeling and simulation, machine learning, and other applied problems. Sparse linear system solution often dominates the execution time of such applications, prompting the ongoing development of highly optimized iterative algorithms and high-performance parallel implementations. In the Lighthouse project, we enable application developers with varied backgrounds to readily discover and effectively apply the best available numerical software for their problems, aiming to maximize both developer productivity and application performance. Lighthouse is a search-based expert system built on a software taxonomy that combines expert knowledge, machine learningbased classification of existing numerical software collections, and automated code generation and optimization. In this paper we present the integration of PETSc and Trilinos iterative solvers for sparse linear systems into the Lighthouse framework. In addition to functional information in the taxonomy, we have created a comprehensive machine learning-based workflow for the automated classification of sparse solvers, which can be generalized to other types of rapidly evolving numerical methods. We present a comparative analysis of the solver classification results for a varied set of input problems and machine learning methods, achieving up to 93% accuracy in identifying the best-performing linear solution methods in PETSc and Trilinos.

Exposing middle school students to computer science through game design appears to be a promising means to mitigate the computer science pipeline challenge. Particularly when game design is integrated into already-existing middle school courses, either with an academic or a technology skills focus, there is a high potential for exposure, often reaching hundreds of students per school per year. In contrast to after-school programs, students taking these classes are usually not self selected, creating an important opportunity to vastly broaden participation in computing activities and to include more female and underrepresented students. Research suggests that exposure to short game design activities is effective in motivating large percentages of students in a wide variety of demographic groups. The Scalable Game Design project has trained middle school teachers around the US to teach students how to make one simple arcade-style game in a 1-2 week session. A study with over 10,000 students is exploring the sustainability of this approach and finding positive responses to inquiries such as these: Do teachers continue to use game design? Do they have the desire and capacity to move forward without extrinsic rewards such as research stipends? After building one game, do students advance to building more games or even STEM simulations?

Linear algebra provides the building blocks for a wide variety of scientific and engineering simulation codes. Users of these codes face a world of continuously changing algorithms and high-performance implementations. In this paper, we describe new capabilities of our Lighthouse framework, whose goal is to match specific problems in the area of high-performance numerical computing with the best available solutions. Lighthouse's innovative strategy eliminates intensive reading of documents and automates the process for developing linear algebra software. Lighthouse provides a searchable taxonomy of popular but difficult to use numerical software for dense and sparse linear algebra while providing the user with the best algorithms for a given problem based on machine learning methods. We introduce the design of Lighthouse and show examples of its interface. We also present algorithm classification results for the preconditioned iterative linear solvers in the Parallel Extensible Toolkit for Scientific Computation (PETSc) and the Trilinos library.

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