With GPU-accelerated simulations, frames-per-second, in-situ visualization and visual analytics becoming a reality, it increases scalability of codes which allows to reduce the time to obtain a solution significantly. This also makes it possible to run large-scale parameter surveys for optimization. We will present recent activities on integrating complex particle accelerator simulations into a reconstruction loop for matching experimental measurements to simulation. This requires to put simulations in a loop with large-scale data analysis, sythetic diagnostics, image reconstruction techniques and interactive in-situ visualization. We will show how the different building blocks of such a tool chain can be accelerated using GPUs and discuss the combination of these tools.
With PIConGPU, new physics phenomena previously not accessible within laser plasma simulations can be studied, which will help us optimize laser-driven radiation sources. Presents results on laser wakefield acceleration of electrons simulated on the Oakridge TITAN system and discuss in detail which techniques help us to get the most out of these clusters. Finally showing how to add fault-tolerance and load-balancing to a large hybridh CPU-GPU code such as PIConGPU to achieve optimum performance.
Laser-driven radiation sources can potentially help us to cure cancer or understand the dynamics of matter on the atomistic scale. With GPUs we today can simulate these sources at a frames-per-second rate. This in turn enables us to make them affordable to more users than ever before.
With powerful lasers breaking the Petawatt barrier, applications for laser-accelerated particle beams are gaining more interest than ever. Ion beams accelerated by intense laser pulses foster new ways of treating cancer and make them available to more people than ever before. Laser-generated electron beams can drive new compact x-ray sources to create snapshots of ultrafast processes in materials. With PIConGPU laser-driven particle acceleration can be computed in hours compared to weeks on standard CPU clusters. We present the techniques behind PIConGPU, detailed performance analysis and the benefits of PIConGPU for real-world physics cases.