Engineering topics
Publications and source records attributed to Heroux, Michael.
The goal of this milestone is to demonstrate readiness of the Sandia Parallel Aerodynamics Reentry Code (SPARC) and the Electro-Magnetic Plasma in Realistic Environments (EMPIRE) code for use on the forthcoming El Capitan exascale platform, which will enable simulation of weapons-relevant problems at unprecedented scale and fidelity.
The goal of this milestone is to demonstrate readiness of LLNL Next-Generation simulation capability for use on the forthcoming El Capitan exascale platform, which will enable simulation of weapons-relevant problems at unprecedented scale and fidelity.
As displayed in the FY23 ASC Implementation Plan (IP) and the Milestone Reporting Tool (MRT), the milestone description and completion criteria state: Demonstrate the capability to execute mission-relevant calculations with a multiphysics code from the Ristra project at scale on the pre-El Capitan Early Access System 3 (EAS-3). These calculations must use the GPU hardware available on EAS-3, the AMD MI-250s, to achieve any reasonable performance.
Abstract not provided.
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Sparse solvers provide essential functionality for a wide variety of scientific applications. Highly parallel sparse solvers are essential for continuing advances in high-fidelity, multi-physics and multi-scale simulations, especially as we target exascale platforms. This paper describes the challenges, strategies and progress of the US Department of Energy Exascale Computing project towards providing sparse solvers for exascale computing platforms. We address the demands of systems with thousands of high-performance node devices where exposing concurrency, hiding latency and creating alternative algorithms become essential. The efforts described here are works in progress, highlighting current success and upcoming challenges.
This paper presents the Fast Adaptive Composite Grid Method for Time Dependent Problems (TDFAC), a local grid refinement scheme for nonlinear, time dependent partial differential equations. The method is presented in a one dimensional setting to illustrate the basic features and components. Computational results which show the effectiveness of TDFAC as a computational tool are also included.