Publications

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This report summarizes the progress made as part of a one year lab-directed research and development (LDRD) project to fund the research efforts of Bryan Marker at the University of Texas at Austin. The goal of the project was to develop new techniques for automatically tuning the performance of dense linear algebra kernels. These kernels often represent the majority of computational time in an application. The primary outcome from this work is a demonstration of the value of model driven engineering as an approach to accurately predict and study performance trade-offs for dense linear algebra computations.

It is often observed that software engineering (SE) processes and practices for computational science and engineering (CSE) lag behind other SE areas [7]. This issue has been a concern for funding agencies, since new research increasingly relies upon and produces computational tools. At the same time, CSE research organizations find it difficult to prescribe formal SE practices for funded projects. Theoretical and experimental science rely heavily on independent verification of results as part of the scientific process. Computational science should have the same regard for independent verification but it does not. In this paper, we present an argument for using reproducibility and independent verification requirements as a driver to improve SE processes and practices. We describe existing efforts that support our argument, how these requirements can impact SE, challenges we face, and new opportunities for using reproducibility requirements as a driver for higher quality CSE software. Copyright 2011 ACM.

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As computational science applications grow more parallel with multi-core supercomputers having hundreds of thousands of computational cores, it will become increasingly difficult for solvers to scale. Our approach is to use hybrid MPI/threaded numerical algorithms to solve these systems in order to reduce the number of MPI tasks and increase the parallel efficiency of the algorithm. However, we need efficient threaded numerical kernels to run on the multi-core nodes in order to achieve good parallel efficiency. In this paper, we focus on improving the performance of a multithreaded triangular solver, an important kernel for preconditioning. We analyze three factors that affect the parallel performance of this threaded kernel and obtain good scalability on the multi-core nodes for a range of matrix sizes. © 2011 Springer-Verlag Berlin Heidelberg.

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The Trilinos Project started approximately nine years ago as a small effort to enable research, development and ongoing support of small, related solver software efforts. The 'Tri' in Trilinos was intended to indicate the eventual three packages we planned to develop. In 2007 the project expanded its scope to include any package that was an enabling technology for technical computing. Presently the Trilinos repository contains over 55 packages covering a broad spectrum of reusable tools for constructing full-featured scalable scientific and engineering applications. Trilinos usage is now worldwide, and many applications have an explicit dependence on Trilinos for essential capabilities. Users come from other US laboratories, universities, industry and international research groups. Awareness and use of Trilinos is growing rapidly outside of Sandia. Members of the external research community are becoming more familiar with Trilinos, its design and collaborative nature. As a result, the Trilinos project is receiving an increasing number of requests from external community members who want to contribute to Trilinos as developers. To-date we have worked with external developers in an ad hoc fashion. Going forward, we want to develop a set of policies, procedures, tools and infrastructure to simplify interactions with external developers. As we go forward with multi-laboratory efforts such as CASL and X-Stack, and international projects such as IESP, we will need a more streamlined and explicit process for making external developers 'first-class citizens' in the Trilinos development community. This document is intended to frame the discussion for expanding the Trilinos community to all strategically important external members, while at the same time preserving Sandia's primary leadership role in the project.

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Trilinos is an object-oriented software framework to enabled the solution of large-scale, complex multiphysics engineering and scientific problems. Different Trilinos packages build on each other to create a stack providing the necessary capability: (1) Non-linear solver; (2) Linear solver/preconditioner; (3) Distributed linear algebra; and (4) Local linear algebra.

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