GPU Acceleration of Particle AdvectionWorkloads in a Parallel, Distributed Memory Setting - eScholarship
David Camp · 2014
GPU Acceleration of Particle Advection Workloads in a Parallel, Distributed Memory Setting David Camp 1 , Hari Krishnan 1 , David Pugmire 2 , Christoph Garth 3 , Ian Johnson 1 , E. Wes Bethel 1 , Kenneth I. Joy 4 , and Hank Childs 1 Lawrence Berkeley National Laboratory, CA, USA Oak Ridge National Laboratory, TN, USA University of Kaiserslautern, Germany University of California, Davis, CA, USA DISCLAIMER: This document was prepared as an account of work sponsored by the United States Government. While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor the Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or the Regents of the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof or the Regents of the University of California. Acknowledgements: This work was supported by the Director, Office of Advanced Scientific Computing Research, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. This research used resources of the National Energy Research Scientific Computing Center (NERSC), which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.