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GTC ON-DEMAND

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Abstract:
Radiation therapy with ion beams precisely targets the tumor, leaving surrounding healthy tissue unharmed. Usually, ion accelerators are huge in size and thus only found in few facilities worldwide. Using high-power laser systems for accelerating the ions could reduce the size and cost of such systems, potentially increasing the number of treatment facilities and thus giving more patients access to this promising therapy method. In order to bring laser acceleration of ions to application, realistic simulations of the acceleration process are needed. We present PIConGPU, a relativistic particle-in-cell plasma simulation code implemented on GPUs that is ideal for optimizing laser ion acceleration.
Radiation therapy with ion beams precisely targets the tumor, leaving surrounding healthy tissue unharmed. Usually, ion accelerators are huge in size and thus only found in few facilities worldwide. Using high-power laser systems for accelerating the ions could reduce the size and cost of such systems, potentially increasing the number of treatment facilities and thus giving more patients access to this promising therapy method. In order to bring laser acceleration of ions to application, realistic simulations of the acceleration process are needed. We present PIConGPU, a relativistic particle-in-cell plasma simulation code implemented on GPUs that is ideal for optimizing laser ion acceleration.  Back
 
Topics:
Computational Physics, Life & Material Science, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2015
Session ID:
S5193
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Abstract:

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 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.

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Topics:
Visualization - In-Situ & Scientific, Artificial Intelligence and Deep Learning, Computational Physics, Medical Imaging & Radiology
Type:
Talk
Event:
GTC Silicon Valley
Year:
2015
Session ID:
S5199
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Abstract:

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.

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.

  Back
 
Topics:
Computational Physics, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2013
Session ID:
S3026
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Abstract:

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.

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.

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Topics:
Medical Imaging & Radiology
Type:
Talk
Event:
Supercomputing
Year:
2012
Session ID:
SC2023
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Abstract:

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.

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.

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Topics:
Computational Physics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2012
Session ID:
S2067
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Speakers:
Guido Juckeland, Michael Bussmann
- TU Dresden - ZIH, Forschungszentrum Dresden-Rossendorf
Abstract:
Dive deep into a multi-parallel Particle in Cell code that utilizes MPI, pthreads, and CUDA. Around this specific application a general C++ framework for transparent data transfers between GPUs has been developed and will be presented. Further techniques employed include interleaving of communication and computation, particle tiling and a study of how well CUDA performance can be transferred to OpenCL.
Dive deep into a multi-parallel Particle in Cell code that utilizes MPI, pthreads, and CUDA. Around this specific application a general C++ framework for transparent data transfers between GPUs has been developed and will be presented. Further techniques employed include interleaving of communication and computation, particle tiling and a study of how well CUDA performance can be transferred to OpenCL.  Back
 
Topics:
Physics Simulation, Astronomy & Astrophysics, HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2010
Session ID:
S102090
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