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GTC ON-DEMAND

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Abstract:
In large-scale scientific simulations, I/O has become a bottleneck that can slow down the exploration of unknown physical scenarios. We show that it is vital to view a HPC system not only in its ability to simulate the system but also to visualize the simulated data. By keeping the data of the simulation in the GPU memory, remote analysis via a Wi-Fi connection can work at frame rates well above 10 fps while latencies are not of importance, even when spanning continents. This presentation includes a live demo.
In large-scale scientific simulations, I/O has become a bottleneck that can slow down the exploration of unknown physical scenarios. We show that it is vital to view a HPC system not only in its ability to simulate the system but also to visualize the simulated data. By keeping the data of the simulation in the GPU memory, remote analysis via a Wi-Fi connection can work at frame rates well above 10 fps while latencies are not of importance, even when spanning continents. This presentation includes a live demo.  Back
 
Topics:
In-Situ & Scientific Visualization, Computational Physics, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2016
Session ID:
S6294
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Abstract:

We'll present results on our INCITE project "Targeting Cancer with High Power Lasers," which aims to deliver beams of ions for cancer therapy accelerated by high power lasers. With a novel target design in which the target is levitated in a trap to isolate it from its environment, we study the properties of the generated ion beams and their potential for radiation therapy of cancer. In the discussion, we'll also present performance results of our own plasma simulation code PIConGPU on the Titan system, which has been used to study the laser plasma interaction in 3D.

We'll present results on our INCITE project "Targeting Cancer with High Power Lasers," which aims to deliver beams of ions for cancer therapy accelerated by high power lasers. With a novel target design in which the target is levitated in a trap to isolate it from its environment, we study the properties of the generated ion beams and their potential for radiation therapy of cancer. In the discussion, we'll also present performance results of our own plasma simulation code PIConGPU on the Titan system, which has been used to study the laser plasma interaction in 3D.

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Topics:
Computational Physics, Computational Biology & Chemistry, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2016
Session ID:
S6300
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Abstract:
We present our open source code HASEonGPU for computing the amplified spontaneous emission (ASE) in laser gain media using a Monte-Carlo approach. With multi-GPU acceleration and optimized sampling techniques ASE can now be computed at high resolution within minutes instead of days. This speed up allows for integrating realistic ASE computations into the design tool chain for the development of high power lasers.
We present our open source code HASEonGPU for computing the amplified spontaneous emission (ASE) in laser gain media using a Monte-Carlo approach. With multi-GPU acceleration and optimized sampling techniques ASE can now be computed at high resolution within minutes instead of days. This speed up allows for integrating realistic ASE computations into the design tool chain for the development of high power lasers.  Back
 
Topics:
Computational Physics, Developer - Algorithms
Type:
Poster
Event:
GTC Silicon Valley
Year:
2015
Session ID:
P5122
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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
Streaming:
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