Engineering topics
Publications and source records attributed to Nelluvelil, Eappen Sebastian.
The thermal radiative transfer equations (TRT) model many important phenomena of interest at LANL. The TRT equations model time-dependent thermal radiation through a medium, i.e., how thermal radiation loses energy by absorption, gains energy via emission processes, and redistributes energy via scattering.
Since 2011, the Los Alamos National Laboratory Computational Physics Student Summer Workshop has been bringing together a highly talented and diverse group of students every summer. Students work in teams of two, alongside typically two mentors, on research projects reflecting a broad range of topics within computational physics. In addition, students attend a series of lectures on topics within computational physics, facility tours, and networking events. The program lasts ten weeks, with this year’s workshop running from June 7 to August 13. At the end of the summer, students give a final presentation, along with a written report. Those reports are what make up the remaining sections of this document. Admission to the workshop is by a competitive process, with the mentors forming the selection committee. One of the important accomplishments of the workshop has been to create a student pipeline from diverse schools that sometimes are not normally tapped by LANL recruiting. Many workshop students maintain a continuing relationship with LANL, returning as student interns, post-doctoral researchers, and staff members. Additionally, workshop alumni act as ambassadors for LANL. The result is a wider awareness both of LANL as a potential employer, and of the technical work that happens at LANL. This year, the workshop was once again in an off-site, virtual format. Students worked on LANL virtual desktop systems remotely, also accessing LANL HPC resources. In order to facilitate communication, student were given accounts on both Webex, a video teleconferencing platform, and Mattermost, an online team collaboration and chat platform, similar to Slack. Daily communication between students and mentors was primarily on Mattermost, with Webex conferencing as needed. The lectures were all done on Webex. Given the difficulty of the virtual format, and a concern that students might have video teleconferencing burn-out, all lectures were optional this year. In spite of this, the attendance was generally high. Lecturers were asked to try to move to a more high-level, ”What is it?,” format. Once again, the students did a tremendous job. Over the course of ten weeks, they did important research across a staggering array of disciplines. The following pages contain the final report for each team’s research efforts. Enjoy!
Legendre truncations to multigroup scattering distributions are not amenable to Monte Carlo sampling due to negative values. We have implemented two moment-preserving methods in MGMC that capture the shape of the truncation, are non-negative over [-1, 1], and can be efficiently sampled on CPUs and GPUs. MGMC can now simulate neutrons with anisotropic scattering mechanics and MGMC shows good agreement with LANL production codes PARTISN.
There is a need for simple, fast, and memory-efficient multidimensional data structures for dense and sparse storage that arise with numerical methods and in software applications. The data structures must perform equally well across multiple computer architectures, including CPUs and GPUs. For this purpose, we developed MATAR, a C++ software library that allows for simple creation and use of intricate data structures that is also portable across disparate architectures using Kokkos. Here, the performance aspect is achieved by forcing contiguous memory layout (or as close to contiguous as possible) for multidimensional and multi-size dense or sparse MATrix and ARray (hence, MATAR) types. Our results show that MATAR has the capability to improve memory utilization, performance, and programmer productivity in scientific computing. This is achieved by fitting more work into the available memory, minimizing memory loads required, and by loading memory in the most efficient order. This document describes the purpose of the work, the implementation of each of the data types, and the resulting performance both in some simple baseline test cases and in an application code.
There are two fundamental aspects of a simulation: the movement of data from memory, and the computation. Both or one of these aspects will be a limiting factor for the simulation.