2013/10/05 by T. Dannert, Tilman Dannert, Dannert, Tilman +4
Computer Science · Physics and Astronomy · #Advanced Data Storage Technologies #Computational Physics (physics.comp-ph) #Distributed #FOS: Computer and information sciences #FOS: Physical sciences #Magnetic confinement fusion research #Parallel #Parallel Computing and Optimization Techniques #Plasma Physics (physics.plasm-ph) #Solar and Stellar Astrophysics (astro-ph.SR) #and Cluster Computing (cs.DC) #astro-ph.SR #cs.DC #physics.comp-ph #physics.plasm-ph
paper · pdf · doi:10.48550/arxiv.1310.1485
10 pages, accepted for publication in ParCo 2013
arxiv created 2013/10/05 · openalex publication_date 2013/10/05 · arxiv updated 2013/10/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We have developed GPU versions for two major high-performance-computing (HPC) applications originating from two different scientific domains. GENE is a plasma microturbulence code which is employed for simulations of nuclear fusion plasmas. VERTEX is a neutrino-radiation hydrodynamics code for "first principles"-simulations of core-collapse supernova explosions. The codes are considered state of the art in their respective scientific domains, both concerning their scientific scope and functionality as well as the achievable compute performance, in particular parallel scalability on all relevant HPC platforms. GENE and VERTEX were ported by us to HPC cluster architectures with two NVidia Kepler GPUs mounted in each node in addition to two Intel Xeon CPUs of the Sandy Bridge family. On such platforms we achieve up to twofold gains in the overall application performance in the sense of a reduction of the time to solution for a given setup with respect to a pure CPU cluster. The paper describes our basic porting strategies and benchmarking methodology, and details the main algorithmic and technical challenges we faced on the new, heterogeneous architecture.