Speaker
Description
PIConGPU, like many other codes, is ready for the next Exascale supercomputers. Heterogeneous programming as the main ingredient enables effective use of these machines. Important challenges still ahead are timely analysis of large scale simulation data and complex workflows for multi-physics simulations and machine learning.
As experimental capabilities progress and high-repetition rate sources have become widely available, the role of simulations is shifting. Predictive capabilities are put to the test more often than ever. While simulations for high intensities still push the boundaries of known physics, the task to predict experimental outcomes for sources existing now has become more and more pressing.
The question arises which steps will be necessary to achieve good comparison with experiments. We will present our approach to work closely with experiment, lessons learned from this approach, what can be done better and how Exascale computing might help.
Acknowledgments
This work was partly funded by the Center of Advanced Systems Understanding (CASUS) which is financed by Germany’s Federal Ministry of Education and
Research (BMBF) and by the Saxon Ministry for Science, Culture and Tourism (SMWK) with tax funds on
the basis of the budget approved by the Saxon State Parliament.
