Center for Computing Research
NVIDIA has invited SNL to present results of a GPU performant shock hydrodynamics code at their Super Computing (SC17) booth.
NVIDIA has invited SNL to present results of a GPU performant coupled hydrodynamics, low Magnetic Reynolds number (low Rm) code at their Super Computing 17 (SC17) booth. Researchers at Sandia are developing a new shock hydrodynamics capability, based on adaptive Lagrangian techniques targeted at next generation architectures. The code simulates shock hydrodynamics on GPU architectures using the Kokkos library to provide portability across architectures. Mesh and field data management, as well as adaptive Lagrangian operations are being developed to run exclusively on the GPU. New algorithms using tetrahedral elements and a predictor-corrector time integrator have been implemented. Low Rm physics is solved using NVIDIA’s AmgX GPU-aware, algebraic multigrid solver. Using an exemplar problem provided by our NW partners we have demonstrated good scaling and performance on next generation architectures. Notably, the exemplar problem demonstrates the advantages of a device-centric design philosophy, where the hydrodynamics physics solve, including adaptivity and remapping, are hosted on the coprocessor with exceptional performance on the GPU relative to traditional multi-core architectures. Additionally, solve times for the low Rm physics with the AmgX software demonstrate sub-second solve times for million degree of freedom problems. Next steps include full-scale testing on Trinity (on both the Haswell and KNL partitions) as well as Sierra as it becomes available, the addition of robust treatment for material/material interactions and the inclusion of more comprehensive MHD physics.
Contact: Hansen, Glen
The Next Platform Highlights CCR Work on Memory-Centric Programming
A recent article from The Next Platform, an online publication that offers in-depth coverage of high-end computing, recently featured an article entitled “New Memory Challenges Legacy Approaches to HPC Code.” The article discusses a paper co-authored by CCR researcher Ron Brightwell that was published last November as part of the Workshop on Memory Centric Programming for HPC at the SC’17 conference. In the article, Brightwell and one of his co- authors, Yonghong Yan from the University of South Carolina, discuss the programming challenges created by recent advances in memory technology and the deepening memory hierarchy. The article examines the notion of memory-centric programming and how programming systems need to evolve to provide better abstractions to help insulate application developers from the complexities associated with current and future advances in memory technology for high-performance computing systems.
Contact: Brightwell, Ronald B. (Ron)
DOE award to develop new quantum algorithms for simulation, optimization, and machine learning
The Department of Energy's Office of Science recently awarded $4.5M over three years to a multi-institutional and multi-disciplinary team led by Dr. Ojas Parekh (1464) to explore the abilities of quantum computers in three interrelated areas: quantum simulation, optimization, and machine learning, each highly relevant to the DOE mission. The QOALAS (Quantum Optimization and Learning and Simulation) project brings together some the world’s top experts in quantum algorithms, quantum simulation, theoretical physics, applied mathematics, and discrete optimization from Sandia National Laboratories, Los Alamos National Laboratory, California Institute of Technology, and University of Maryland. The QOALAS team will leverage and unearth connections between simulation, optimization, and machine learning to fuel new applications of quantum information processing to science and technology as well as further investigate the potential of quantum computers to solve certain problems dramatically faster or with better fidelity than possible with classical computers.
Contact: Parekh, Ojas D.
Released VTK-m user's guide, version 1.1
Researchers at Sandia National Laboratories, in collaboration with Kitware Inc., Oak Ridge National Laboratory, Los Alamos National Laboratory, and the university of Oregon, are proud to release VTK-m version 1.1. The VTK-m library provides highly parallel code to execute visualization on many-core processors like GPUs, multi-core CPUs, and other hardware we are likely to see at for Exascale HPC. This release of VTK-m includes critical core features including filter structures and key reduction. Also provided by this release are several new filters including external faces, gradients, clipping, and point merging. Also provided with this release is a comprehensive VTK-m User’s Guide providing detailed instruction and reference for using and editing VTK-m.
Contact: Moreland, Kenneth D.