GTC ON-DEMAND

 
SEARCH SESSIONS
SEARCH SESSIONS

Search All
 
Refine Results:
 
Year(s)

SOCIAL MEDIA

EMAIL SUBSCRIPTION

 
 

GTC ON-DEMAND

Presentation
Media
Abstract:
We'll highlight Sentinel, a system for real-time in-situ intelligent video analytics on mobile computing platforms. Sentinel combines state-of-the-art techniques in HPC with dynamic mode decomposition (DMD), a proven method for data reduction and analysis. By leveraging CUDA, our early system prototype achieves significantly better-than-real-time performance for DMD-based background/foreground separation on high-definition video streams, thereby establishing the efficacy of DMD as the foundation on which to build higher level real-time computer vision techniques. We'll present an overview of the Sentinel system, including the application of DMD to background/foreground separation in video streams, and outline our ongoing efforts to enhance and extend the prototype system.
We'll highlight Sentinel, a system for real-time in-situ intelligent video analytics on mobile computing platforms. Sentinel combines state-of-the-art techniques in HPC with dynamic mode decomposition (DMD), a proven method for data reduction and analysis. By leveraging CUDA, our early system prototype achieves significantly better-than-real-time performance for DMD-based background/foreground separation on high-definition video streams, thereby establishing the efficacy of DMD as the foundation on which to build higher level real-time computer vision techniques. We'll present an overview of the Sentinel system, including the application of DMD to background/foreground separation in video streams, and outline our ongoing efforts to enhance and extend the prototype system.  Back
 
Topics:
Federal, Intelligent Video Analytics, In-Situ & Scientific Visualization, Artificial Intelligence and Deep Learning, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7685
Download:
Share:
 
Abstract:

We highlight Sentinel, a system for real-time in-situ intelligent video analytics on mobile computing platforms. Sentinel combines state-of-the-art techniques in HPC with Dynamic Mode Decomposition (DMD), a proven method for data reduction and analysis. By leveraging CUDA, our early system prototype achieves significantly better-than-real-time performance for DMD-based background/foreground separation on high-definition video streams, thereby establishing the efficacy of DMD as the foundation on which to build higher level real-time computer vision techniques. In this talk, we present an overview of the Sentinel system, including the application of DMD to background/foreground separation in video streams, and outline our current efforts to enhance and extend the prototype system.

We highlight Sentinel, a system for real-time in-situ intelligent video analytics on mobile computing platforms. Sentinel combines state-of-the-art techniques in HPC with Dynamic Mode Decomposition (DMD), a proven method for data reduction and analysis. By leveraging CUDA, our early system prototype achieves significantly better-than-real-time performance for DMD-based background/foreground separation on high-definition video streams, thereby establishing the efficacy of DMD as the foundation on which to build higher level real-time computer vision techniques. In this talk, we present an overview of the Sentinel system, including the application of DMD to background/foreground separation in video streams, and outline our current efforts to enhance and extend the prototype system.

  Back
 
Topics:
Federal, HPC and AI
Type:
Talk
Event:
GTC Washington D.C.
Year:
2016
Session ID:
DCS16132
Streaming:
Share:
 
Abstract:

We present a new method that accurately detects soft-body collisions, specifically the edge-edge collisions that most other methods would miss, interactively on modern GPUs. Our method guarantees that no pass-through will occur between objects by using interpolation equations to represent motion between time steps, yielding nearly exact collision times and responses. GPU acceleration via CUDA allows this method to operate at interactive rates.

We present a new method that accurately detects soft-body collisions, specifically the edge-edge collisions that most other methods would miss, interactively on modern GPUs. Our method guarantees that no pass-through will occur between objects by using interpolation equations to represent motion between time steps, yielding nearly exact collision times and responses. GPU acceleration via CUDA allows this method to operate at interactive rates.

  Back
 
Topics:
Combined Simulation & Real-Time Visualization, Computational Physics
Type:
Poster
Event:
GTC Silicon Valley
Year:
2013
Session ID:
P3203
Download:
Share:
 
Abstract:

We present the Visual Simulation Laboratory (VSL), an ongoing project aimed at bringing the power of GPU computing to a variety of DoD application domains. VSL is an open-source framework developed by the U.S. Army Research Laboratory and its collaborators designed to transform legacy workflows into immersive, end-to-end physics-based simulation and analysis tools. GPU computing facilitates combined simulation and visualization, enabling analysts to interact with a visual representation of not just the results, but of the computational mechanisms as well. This poster highlights VSL and demonstrates the potential of GPU computing to transform a variety of applications across the DoD.

We present the Visual Simulation Laboratory (VSL), an ongoing project aimed at bringing the power of GPU computing to a variety of DoD application domains. VSL is an open-source framework developed by the U.S. Army Research Laboratory and its collaborators designed to transform legacy workflows into immersive, end-to-end physics-based simulation and analysis tools. GPU computing facilitates combined simulation and visualization, enabling analysts to interact with a visual representation of not just the results, but of the computational mechanisms as well. This poster highlights VSL and demonstrates the potential of GPU computing to transform a variety of applications across the DoD.

  Back
 
Topics:
Combined Simulation & Real-Time Visualization, Real-Time Graphics
Type:
Poster
Event:
GTC Silicon Valley
Year:
2013
Session ID:
P3206
Download:
Share:
 
Abstract:

The fast and accurate rendering of transparent objects is an open problem in computer graphics as it necessitates expensive fragment sorting on the GPU. We present initial findings in optimizing existing order-independent transparency (OIT) algorithms by reducing costly global thread synchronization. We leverage these improvements in a novel application of OIT to ballistic simulations used in vulnerability/lethality analysis software.

The fast and accurate rendering of transparent objects is an open problem in computer graphics as it necessitates expensive fragment sorting on the GPU. We present initial findings in optimizing existing order-independent transparency (OIT) algorithms by reducing costly global thread synchronization. We leverage these improvements in a novel application of OIT to ballistic simulations used in vulnerability/lethality analysis software.

  Back
 
Topics:
Real-Time Graphics, Combined Simulation & Real-Time Visualization
Type:
Poster
Event:
GTC Silicon Valley
Year:
2013
Session ID:
P3207
Download:
Share:
 
Abstract:
Rayforce is a high performance ray tracing engine designed for highly parallel computing architectures including manycore GPUs, multicore CPUs, and hybrid CPU/GPU processors. Rayforce leverages a novel graph-based acceleration structure that permits high-performance first-hit, any-hit, and multi-hit traversal algorithms required to solve a variety of problems in rendering and physically based simulation. These building blocks are exposed via a programmable interface to enable the implementation of computer graphics and scientific computing applications. This poster introduces the Rayforce engine, provides initial metrics for each of the traversal algorithms enabled by the graph-based acceleration structure, and highlights some areas of future work.
Rayforce is a high performance ray tracing engine designed for highly parallel computing architectures including manycore GPUs, multicore CPUs, and hybrid CPU/GPU processors. Rayforce leverages a novel graph-based acceleration structure that permits high-performance first-hit, any-hit, and multi-hit traversal algorithms required to solve a variety of problems in rendering and physically based simulation. These building blocks are exposed via a programmable interface to enable the implementation of computer graphics and scientific computing applications. This poster introduces the Rayforce engine, provides initial metrics for each of the traversal algorithms enabled by the graph-based acceleration structure, and highlights some areas of future work.   Back
 
Topics:
Rendering & Ray Tracing, Real-Time Graphics
Type:
Poster
Event:
GTC Silicon Valley
Year:
2013
Session ID:
P3208
Download:
Share:
 
Abstract:

Prediction of radio frequency (RF) energy propagation in complex urban environments is of great interest when planning, optimizing and analyzing wireless networks. A tool for fast prediction could improve network coverage, provide estimates of signal strength, estimate time delay of multipath signals, and provide data for power allocation in the deployed transmitters. This poster highlights the Manta-RF system, which enables RF prediction through modifications to the Manta interactive ray tracing framework. We are actively migrating the current implementation to NVIDA's CUDA architecture. Ray tracing scales well with core count, and we expect to achieve near-interactive RF simulations as a result.

Prediction of radio frequency (RF) energy propagation in complex urban environments is of great interest when planning, optimizing and analyzing wireless networks. A tool for fast prediction could improve network coverage, provide estimates of signal strength, estimate time delay of multipath signals, and provide data for power allocation in the deployed transmitters. This poster highlights the Manta-RF system, which enables RF prediction through modifications to the Manta interactive ray tracing framework. We are actively migrating the current implementation to NVIDA's CUDA architecture. Ray tracing scales well with core count, and we expect to achieve near-interactive RF simulations as a result.

  Back
 
Topics:
Rendering & Ray Tracing, Combined Simulation & Real-Time Visualization
Type:
Poster
Event:
GTC Silicon Valley
Year:
2013
Session ID:
P3209
Download:
Share:
 
Abstract:

Explore recent advances in high-performance GPU ray tracing for applications other than optical rendering. In this session, we dive into the details of Rayforce, a CUDA ray tracing engine that leverages a new graph-based spatial indexing structure to achieve performance in excess of one billion rays per second in some non-trivial scenarios. We then explore several example applications that leverage Rayforce in a framework for cognition-driven simulation (CDS) that enables analysts to experience an immersive physics-based simulation environment. Compared to traditional means of analysis, CDS represents a next-generation approach to simulation and analysis across a broad range of application domains.

Explore recent advances in high-performance GPU ray tracing for applications other than optical rendering. In this session, we dive into the details of Rayforce, a CUDA ray tracing engine that leverages a new graph-based spatial indexing structure to achieve performance in excess of one billion rays per second in some non-trivial scenarios. We then explore several example applications that leverage Rayforce in a framework for cognition-driven simulation (CDS) that enables analysts to experience an immersive physics-based simulation environment. Compared to traditional means of analysis, CDS represents a next-generation approach to simulation and analysis across a broad range of application domains.

  Back
 
Topics:
Rendering & Ray Tracing, Combined Simulation & Real-Time Visualization, AEC & Manufacturing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2013
Session ID:
S3157
Streaming:
Download:
Share:
 
 
Previous
  • Amazon Web Services
  • IBM
  • Cisco
  • Dell EMC
  • Hewlett Packard Enterprise
  • Inspur
  • Lenovo
  • SenseTime
  • Supermicro Computers
  • Synnex
  • Autodesk
  • HP
  • Linear Technology
  • MSI Computer Corp.
  • OPTIS
  • PNY
  • SK Hynix
  • vmware
  • Abaco Systems
  • Acceleware Ltd.
  • ASUSTeK COMPUTER INC
  • Cray Inc.
  • Exxact Corporation
  • Flanders - Belgium
  • Google Cloud
  • HTC VIVE
  • Liqid
  • MapD
  • Penguin Computing
  • SAP
  • Sugon
  • Twitter
Next