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GTC ON-DEMAND

Presentation
Media
Abstract:
To enable our users to Image, Design and Create Anything, using a vast gamut of product offerings, consistent visualization and design experience has been the main focus. This session provides a quick tour of Autodesk's internal graphics system for interactive GPU rendering. It lists some of the challenges faced and how these were overcome.
To enable our users to Image, Design and Create Anything, using a vast gamut of product offerings, consistent visualization and design experience has been the main focus. This session provides a quick tour of Autodesk's internal graphics system for interactive GPU rendering. It lists some of the challenges faced and how these were overcome.  Back
 
Topics:
Product & Building Design, Real-Time Graphics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8388
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Abstract:

We'll explore NVIDIA GVDB Voxels, a new open source SDK framework for generic representation, computation, and rendering of voxel-based data. We'll introduce the features of the new SDK and cover applications and examples in motion pictures, scientific visualization, and 3D printing. NVIDIA GVDB Voxels, based on GVDB Sparse Volume technology and inspired by OpenVDB, manipulates large volumetric datasets entirely on the GPU using a hierarchy of grids. The second part of the talk will cover in-depth use of the SDK, with code samples, and coverage of the design aspects of NVIDIA GVDB Voxels. A sample code walk-through will demonstrate how to build sparse volumes, render high-quality images with NVIDIA OptiX integration, produce dynamic data, and perform compute-based operations.

We'll explore NVIDIA GVDB Voxels, a new open source SDK framework for generic representation, computation, and rendering of voxel-based data. We'll introduce the features of the new SDK and cover applications and examples in motion pictures, scientific visualization, and 3D printing. NVIDIA GVDB Voxels, based on GVDB Sparse Volume technology and inspired by OpenVDB, manipulates large volumetric datasets entirely on the GPU using a hierarchy of grids. The second part of the talk will cover in-depth use of the SDK, with code samples, and coverage of the design aspects of NVIDIA GVDB Voxels. A sample code walk-through will demonstrate how to build sparse volumes, render high-quality images with NVIDIA OptiX integration, produce dynamic data, and perform compute-based operations.

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Topics:
In-Situ & Scientific Visualization, Computational Physics, AEC & Manufacturing, Media and Entertainment
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7424
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Abstract:

Improvements in 3D printing allow for unique processes, finer details, better quality control, and a wider range of materials as printing hardware improves. With these improvements comes the need for greater computational power and control over 3D-printed objects. We introduce NVIDIA GVDB Voxels as an open source SDK for voxel-based 3D printing workflows. Traditional workflows are based on processing polygonal models and STL files for 3D printing. However, such models don't allow for continuous interior changes in color or density, for descriptions of heterogeneous materials, or for user-specified support lattices. Using the new NVIDIA GVDB Voxels SDK, we demonstrate practical examples of design workflows for complex 3D printed parts with high-quality ray-traced visualizations, direct data manipulation, and 3D printed output.

Improvements in 3D printing allow for unique processes, finer details, better quality control, and a wider range of materials as printing hardware improves. With these improvements comes the need for greater computational power and control over 3D-printed objects. We introduce NVIDIA GVDB Voxels as an open source SDK for voxel-based 3D printing workflows. Traditional workflows are based on processing polygonal models and STL files for 3D printing. However, such models don't allow for continuous interior changes in color or density, for descriptions of heterogeneous materials, or for user-specified support lattices. Using the new NVIDIA GVDB Voxels SDK, we demonstrate practical examples of design workflows for complex 3D printed parts with high-quality ray-traced visualizations, direct data manipulation, and 3D printed output.

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Topics:
AEC & Manufacturing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7425
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Abstract:

We introduce GVDB Sparse Volumes as a new offering with NVIDIA DesignWorks to focus on high quality raytracing of sparse volumetric data for motion pictures. Based on the VDB topology of Museth, with a novel GPU-based data structure and API, GVDB is designed for efficient compute and raytracing on a sparse hierarchy of grids. Raytracing on the GPU is accelerated with indexed memory pooling, 3D texture atlas storage and a new hierarchical traversal algorithm. GVDB integrates with NVIDIA OptiX, and is developed as an open source library as a part of DesignWorks.

We introduce GVDB Sparse Volumes as a new offering with NVIDIA DesignWorks to focus on high quality raytracing of sparse volumetric data for motion pictures. Based on the VDB topology of Museth, with a novel GPU-based data structure and API, GVDB is designed for efficient compute and raytracing on a sparse hierarchy of grids. Raytracing on the GPU is accelerated with indexed memory pooling, 3D texture atlas storage and a new hierarchical traversal algorithm. GVDB integrates with NVIDIA OptiX, and is developed as an open source library as a part of DesignWorks.

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Topics:
Best of GTC Talks
Type:
Talk
Event:
SIGGRAPH
Year:
2016
Session ID:
SIG1664
Streaming:
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Abstract:

We introduce GVDB Sparse Volumes as a new offering with NVIDIA DesignWorks to focus on high quality raytracing of sparse volumetric data for motion pictures. Based on the VDB topology of Museth, with a novel GPU-based data structure and API, GVDB is designed for efficient compute and raytracing on a sparse hierarchy of grids. Raytracing on the GPU is accelerated with indexed memory pooling, 3D texture atlas storage and a new hierarchical traversal algorithm. GVDB integrates with NVIDIA OptiX, and is developed as an open source library as a part of DesignWorks.

We introduce GVDB Sparse Volumes as a new offering with NVIDIA DesignWorks to focus on high quality raytracing of sparse volumetric data for motion pictures. Based on the VDB topology of Museth, with a novel GPU-based data structure and API, GVDB is designed for efficient compute and raytracing on a sparse hierarchy of grids. Raytracing on the GPU is accelerated with indexed memory pooling, 3D texture atlas storage and a new hierarchical traversal algorithm. GVDB integrates with NVIDIA OptiX, and is developed as an open source library as a part of DesignWorks.

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Topics:
Best of GTC Theater
Type:
Talk
Event:
SIGGRAPH
Year:
2016
Session ID:
SIG1643
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Abstract:

We present a novel technique for visualization of scientific data with compute operators and multi-scatter ray tracing entirely on GPU. Our source data consists of a high-resolution simulation using point-based wavelets, a representation not supported by existing tools. To visualize this data, and consider dynamic time-based rendering, our approach is inspired by OpenVDB from motion pictures, which uses a hierarchy of grids similar to AMR. We develop GVDB, a ground-up implementation with tree traversal, compute, and ray tracing via OptiX all on the GPU. GVDB enables multi-scatter rendering at 200 million rays/sec, and full-volume compute operations in a few milliseconds on datasets up to 4,200^3 entirely in GPU memory.

We present a novel technique for visualization of scientific data with compute operators and multi-scatter ray tracing entirely on GPU. Our source data consists of a high-resolution simulation using point-based wavelets, a representation not supported by existing tools. To visualize this data, and consider dynamic time-based rendering, our approach is inspired by OpenVDB from motion pictures, which uses a hierarchy of grids similar to AMR. We develop GVDB, a ground-up implementation with tree traversal, compute, and ray tracing via OptiX all on the GPU. GVDB enables multi-scatter rendering at 200 million rays/sec, and full-volume compute operations in a few milliseconds on datasets up to 4,200^3 entirely in GPU memory.

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Topics:
In-Situ & Scientific Visualization, Computational Fluid Dynamics, Rendering & Ray Tracing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2016
Session ID:
S6199
Streaming:
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Abstract:
Graphics state has increased in complexity with advances in the GPU pipeline over time. Large graphics applications now have to record and track vertex buffers, frame buffers, constant buffers, textures, various shaders, and raster state. Existing solutions for state bucketing only observe API switches. A novel tool and technique, the StateViewer, is presented which can independently trace and visualize deep changes in state, which includes deltas in mapped buffer values. Data visualization of these values allows the user to visually identify patterns in graphics usage not previously observed. This can directly suggest focus areas in large applications that would benefit from redesign with an emphasis on next generation command-based graphics APIs.
Graphics state has increased in complexity with advances in the GPU pipeline over time. Large graphics applications now have to record and track vertex buffers, frame buffers, constant buffers, textures, various shaders, and raster state. Existing solutions for state bucketing only observe API switches. A novel tool and technique, the StateViewer, is presented which can independently trace and visualize deep changes in state, which includes deltas in mapped buffer values. Data visualization of these values allows the user to visually identify patterns in graphics usage not previously observed. This can directly suggest focus areas in large applications that would benefit from redesign with an emphasis on next generation command-based graphics APIs.  Back
 
Topics:
Tools & Libraries, Performance Optimization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2015
Session ID:
S5186
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Abstract:
Nearest neighbor search is the key to efficient simulation of many discrete physical models. This talk focuses on a novel, efficient fixed-radius NNS by introducing counting sort accelerated with atomic GPU operations which require only two kernel calls. As a sample application, fluid simulations based on smooth particles hydrodynamics (SPH) make use of NNS to determine interacting fluid particles. The Counting-sort NNS method achieves a performance gain of 3-5x over previous Radix-sort NNS, which allows for interactive SPH fluids of 4 million particles at 4 fps on current hardware. The technique presented is generic and easily adapted to other domains, such as molecular interactions or point cloud reconstructions.
Nearest neighbor search is the key to efficient simulation of many discrete physical models. This talk focuses on a novel, efficient fixed-radius NNS by introducing counting sort accelerated with atomic GPU operations which require only two kernel calls. As a sample application, fluid simulations based on smooth particles hydrodynamics (SPH) make use of NNS to determine interacting fluid particles. The Counting-sort NNS method achieves a performance gain of 3-5x over previous Radix-sort NNS, which allows for interactive SPH fluids of 4 million particles at 4 fps on current hardware. The technique presented is generic and easily adapted to other domains, such as molecular interactions or point cloud reconstructions.   Back
 
Topics:
Computational Fluid Dynamics, Numerical Algorithms & Libraries, Performance Optimization, Molecular Dynamics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2014
Session ID:
S4117
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Abstract:

The future of computing is parallelism, and good parallelism is achieved by knowing how to optimize your code. In this talk, I will cover some common best practices for CUDA that give large amounts of parallelism while being both easy to write and optimize. While established optimization techniques will be covered, we will also explore some more esoteric best practices that are now commonly available on newer hardware. CUDA provides a programming model that rewards attention to details to give great performance. This talk will cover practices, such as memory transactions and atomic ops, that are focused on giving you the tools you need to get the most out of your parallel code.

The future of computing is parallelism, and good parallelism is achieved by knowing how to optimize your code. In this talk, I will cover some common best practices for CUDA that give large amounts of parallelism while being both easy to write and optimize. While established optimization techniques will be covered, we will also explore some more esoteric best practices that are now commonly available on newer hardware. CUDA provides a programming model that rewards attention to details to give great performance. This talk will cover practices, such as memory transactions and atomic ops, that are focused on giving you the tools you need to get the most out of your parallel code.

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Topics:
Programming Languages, Tools & Libraries
Type:
Talk
Event:
SIGGRAPH
Year:
2013
Session ID:
SG3111
Streaming:
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