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GTC ON-DEMAND

Presentation
Media
Abstract:
Large-scale scientific simulation campaigns involve collaboration efforts sharing diversity in expertise and geographic location. Collaboration extends through the life cycle of the data from computation to analysis and visualization. This talk will present how SIGHT, an exploratory visualization tool for large-scale atomistic datasets, leverages Web technologies and GPU based solutions for collaborative scientific visualization in HPC environments. Our approach includes a multi-streaming prototype that takes advantage of NvPipe for close to zero latency video compression and, NVIDIA Optix to generate simultaneous and independent user views, all to facilitate common analysis tasks in Materials Science.
Large-scale scientific simulation campaigns involve collaboration efforts sharing diversity in expertise and geographic location. Collaboration extends through the life cycle of the data from computation to analysis and visualization. This talk will present how SIGHT, an exploratory visualization tool for large-scale atomistic datasets, leverages Web technologies and GPU based solutions for collaborative scientific visualization in HPC environments. Our approach includes a multi-streaming prototype that takes advantage of NvPipe for close to zero latency video compression and, NVIDIA Optix to generate simultaneous and independent user views, all to facilitate common analysis tasks in Materials Science.  Back
 
Topics:
In-Situ & Scientific Visualization, Rendering & Ray Tracing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9490
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Abstract:

Learn to leverage the visualization capabilities of the NVIDIA DGX-1 system to visualize particle data. We'll cover techniques suitable for exploratory visualization such as parallel dataset reading and reduction on demand with ADIOS I/O library, GPU-based optimization techniques for particle rendering such as radar view frustum culling, occlusion culling, texture-less point sprites, and OpenGL near zero driver overhead methods. We'll also include implementation details to take advantage of the eight NVIDIA Pascal? GPUs included in the NVIDIA DGX-1.

Learn to leverage the visualization capabilities of the NVIDIA DGX-1 system to visualize particle data. We'll cover techniques suitable for exploratory visualization such as parallel dataset reading and reduction on demand with ADIOS I/O library, GPU-based optimization techniques for particle rendering such as radar view frustum culling, occlusion culling, texture-less point sprites, and OpenGL near zero driver overhead methods. We'll also include implementation details to take advantage of the eight NVIDIA Pascal? GPUs included in the NVIDIA DGX-1.

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Topics:
In-Situ & Scientific Visualization, Real-Time Graphics, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7175
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Abstract:
Markov decision processes have been used in real-world path planning, where environment information is incomplete or dynamic. The problem with the MDP formalism is that its state space grows exponentially with the number of domain variables, and its inference methods grow with the number of available actions. To overcome this issue, we formulate an MDP solver in terms of matrix multiplications, based on the value iteration algorithm; thus we can take advantage of GPUs to produce interactively obstacle-free paths in the form of an optimal policy. We'll present a performance analysis of our technique using Jetson TK1, CPU, and GPU platforms. Our algorithm presents 90x speed-up in GPUs, and 30x speed-up in the Jetson TK1 in contrast with its CPU multi-threaded version.
Markov decision processes have been used in real-world path planning, where environment information is incomplete or dynamic. The problem with the MDP formalism is that its state space grows exponentially with the number of domain variables, and its inference methods grow with the number of available actions. To overcome this issue, we formulate an MDP solver in terms of matrix multiplications, based on the value iteration algorithm; thus we can take advantage of GPUs to produce interactively obstacle-free paths in the form of an optimal policy. We'll present a performance analysis of our technique using Jetson TK1, CPU, and GPU platforms. Our algorithm presents 90x speed-up in GPUs, and 30x speed-up in the Jetson TK1 in contrast with its CPU multi-threaded version.  Back
 
Topics:
Algorithms & Numerical Techniques, Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Silicon Valley
Year:
2016
Session ID:
S6268
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Speakers:
,,
Abstract:

Industry trends in the coming years in the race to exascale imply the availability of cluster computing with hundreds to thousands of cores per chip. Programming presents a challenge due to the heterogeneous architecture. Using novel programming models that facilitate this process is necessary. In this talk we present the case of simulation and visualization of crowds. We analyze and compare the use of two programming models: OmpSs and CUDA and show that OmpSs allows us to exploit all the resources combining the use of CPU and GPU taking care of memory management, scheduling, communications and synchronization automatically. We will present experimental results obtained in the Barcelona Supercomputing Center GPU Cluster as well as describe several modes used for visualizing the results.

Industry trends in the coming years in the race to exascale imply the availability of cluster computing with hundreds to thousands of cores per chip. Programming presents a challenge due to the heterogeneous architecture. Using novel programming models that facilitate this process is necessary. In this talk we present the case of simulation and visualization of crowds. We analyze and compare the use of two programming models: OmpSs and CUDA and show that OmpSs allows us to exploit all the resources combining the use of CPU and GPU taking care of memory management, scheduling, communications and synchronization automatically. We will present experimental results obtained in the Barcelona Supercomputing Center GPU Cluster as well as describe several modes used for visualizing the results.

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Topics:
Visualization - In-Situ & Scientific, Data Center & Cloud Infrastructure, Real-Time Graphics, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2015
Session ID:
S5381
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Abstract:

We discuss several steps in the process for simulating and visualizing large and varied crowds, in real time, for consumer-level computers and graphic cards (GPUs). We discuss methods for simulating, generating, animating and rendering crowds of varied aspect and a diversity of behaviors.

We discuss several steps in the process for simulating and visualizing large and varied crowds, in real time, for consumer-level computers and graphic cards (GPUs). We discuss methods for simulating, generating, animating and rendering crowds of varied aspect and a diversity of behaviors.

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Topics:
Media & Entertainment Summit, Combined Simulation & Real-Time Visualization, Rendering & Ray Tracing, Visual Effects & Simulation
Type:
Talk
Event:
GTC Silicon Valley
Year:
2014
Session ID:
S4229
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