PIConGPU - A Highly-Scalable Particle-in-Cell Implementation for GPU Clusters
PIConGPU - A Highly-Scalable Particle-in-Cell Implementation for GPU Clusters
Bussmann, M.; Burau, H.; Debus, A.; Hübl, A.; Kluge, T.; Pausch, R.; Schmeisser, N.; Schneider, B.; Steiniger, K.; Widera, R.; Wyderka, N.; Schramm, U.; Cowan, T. E.; Schmitt, F.; Grottel, S.; Gumhold, S.; Juckeland, G.; Nagel, W.
PIConGPU can handle large-scale simulations of laser plasma and astrophysical plasma dynamics on GPU clusters with thousands of GPUs. High data throughput allows to conduct large parameter surveys but makes it necessary to rethink data analysis and look for new ways of analyzing large simulation data sets. The speedup seen on GPUs enables scientists to add physical effects to their code that up until recently have been too computationally demanding. We present recent results obtained with PIConGPU, discuss scaling behaviour, the most important building blocks of the code and new physics modules recently added. In addition we give an outlook on data analysis, resiliance and load balancing with PIConGPU.
Keywords: gpu; particle-in-cell; pic; simulation
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Permalink: https://www.hzdr.de/publications/Publ-18671
Publ.-Id: 18671