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GTC On-Demand

AI Application Deployment and Inference
Presentation
Media
Distributed and Scalable Video Analytics on Tegra X1/X2 Based Embedded Computer Cluster

A wide area and city surveillance system solution for running real-time video analytics on thousands of 1080p video streams will be presented. System hardware is an embedded computer cluster based on NVIDIA TX1/TX2 and NXP iMX6 modules. A custom designed system software manages job distribution, resulting in collection and system wide diagnostics including instantaneous voltage, power and temperature readings. System is fully integrated with a custom designed video management software, IP cameras and network video recorders. Instead of drawing algorithm results on the processed video frames, re-encoding and streaming back to the operator computer for display, only the obtained metadata is sent to the operator computer. Video management software streams video sources independently, and synchronizes decoded video frames with the corresponding metadata locally, before presenting the processed frames to the operator.

A wide area and city surveillance system solution for running real-time video analytics on thousands of 1080p video streams will be presented. System hardware is an embedded computer cluster based on NVIDIA TX1/TX2 and NXP iMX6 modules. A custom designed system software manages job distribution, resulting in collection and system wide diagnostics including instantaneous voltage, power and temperature readings. System is fully integrated with a custom designed video management software, IP cameras and network video recorders. Instead of drawing algorithm results on the processed video frames, re-encoding and streaming back to the operator computer for display, only the obtained metadata is sent to the operator computer. Video management software streams video sources independently, and synchronizes decoded video frames with the corresponding metadata locally, before presenting the processed frames to the operator.

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Keywords:
AI Application Deployment and Inference, Intelligent Video Analytics and Smart Cities, GTC Silicon Valley 2018 - ID S8409
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Computational Fluid Dynamics
Presentation
Media
Accelerating Finite-Volume Based Lattice Boltzmann Flow Solutions Using CUDA
The Lattice Boltzmann Method (LBM) is implemented through a finite-volume approach to perform 2-D, incompressible, and laminar fluid flow analyses on structured grids. Once the serial version is implemented and validated through the laminar 2-D lid-driven cavity problem and 2-D flow over a circular cylinder, the flow solver is accelerated on GPU by porting compute and memory bandwidth intense functions to CUDA. The CUDA accelerated implementation is compared against serial implementation and multi-threaded version running on dual Intel Xeon processors.
The Lattice Boltzmann Method (LBM) is implemented through a finite-volume approach to perform 2-D, incompressible, and laminar fluid flow analyses on structured grids. Once the serial version is implemented and validated through the laminar 2-D lid-driven cavity problem and 2-D flow over a circular cylinder, the flow solver is accelerated on GPU by porting compute and memory bandwidth intense functions to CUDA. The CUDA accelerated implementation is compared against serial implementation and multi-threaded version running on dual Intel Xeon processors.  Back
 
Keywords:
Computational Fluid Dynamics, GTC Silicon Valley 2014 - ID P4180
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Video and Image Processing
Presentation
Media
GPU Based Resolution and Contrast Enhancement for Infrared Cameras
A massively multi-threaded CUDA implementation of spatial resolution and dynamic range enhancement technique is presented. The implementation obtained by combining the multi-threaded super-resolution and CLAHE blocks runs at approximately 15 frames per second, resulting in near real-time performance.
A massively multi-threaded CUDA implementation of spatial resolution and dynamic range enhancement technique is presented. The implementation obtained by combining the multi-threaded super-resolution and CLAHE blocks runs at approximately 15 frames per second, resulting in near real-time performance.  Back
 
Keywords:
Video and Image Processing, GTC Silicon Valley 2013 - ID P3136
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