NVIDIA Xavier - RidgeRun Products - GstCUDA

NVIDIA®Jetson Xavier™
Introduction
SoM Overview Carrier Boards NVIDIA Xavier Developer Kit Connect Tech Rogue Connect Tech Mimic Software Support NVIDIA JetPack
JetPack 5.0.2
Getting Started Installing JetPack Flashing the board with GUI installer JetPack Components Compiling Source Code Flashing the Board from Cmdline Performance Tuning Evaluating Performance Set Values Manually Tuning Power Maximizing Performance
Development in the Board
Getting into the Board Using Serial Console Using SSH Using Ubuntu Login Installing Packages
Video Capture and Display
Description Supported Cameras Imx219 Driver Software Support Using GStreamer Using Libargus
Processors
HD Audio and Video Subystem Video Encoder Video Decoder JPEG Encoder and Decoder Video Image Compositor Audio Processing Engine Deep Learning Accelerator Volta GPU Description Using CUDA Using OpenGL
Interfaces
I2C SD/eMMC Controller USB Controller
GStreamer Pipelines
Capture and Display H264 H265 VP9 MPEG4 Simultaneous Streams Compositor
Deep Learning
TensorRT Using TensorRT integrated with Tensorflow Parsing a Tensorflow model for TensorRT Parsing Caffe model for TensorRT Building TensorRT API examples Deep Learning Accelerator Software Support Deep Learning Tutorials Jetson Inference Jetson Reinforcement
RidgeRun Products
GstPTZR GstCUDA GstColorTransfer GstWebRTC GstRtspSink GstInterpipe GstInference GStreamer Daemon GstShark
Reference Documentation
Contact Us

GstCUDA Project General Characteristics

GstCUDA characteristics:

• Easy CUDA algorithm integration into GStreamer pipelines.
• Complexity abstraction of both CUDA and GStreamer - allowing the developer to focus on the CUDA algorithm.
• Optimal performance assurance for GStreamer/CUDA applications on Jetson platforms.

Features

• Offers a framework allowing users to develop custom GStreamer elements that can execute any CUDA algorithm.
• Zero memory copy interface between CUDA and GStreamer.
• GstCUDA supports two modes of memory handling:
  • NVMM direct mapping mode: use the GstCUDA API's to directly handle NVMM memory buffers. This method provides the best possible performance on the Tegra platforms.
  • Unified memory allocator mode: avoids the use of NVMM memory buffers by providing a memory allocator that directly passes the buffer to the GPU, providing zero memory copies and maintaining excellent performance.
• Supports heavy CUDA algorithms and large amounts of data to be processed on the GPU without performance being affected due to copies or memory conversions.
• Provides a set of video filter quick prototyping GStreamer elements, with different input/output combinations, that allows video frames to be processed by the GPU using a custom CUDA library algorithm.
• Provides integrated add-on elements; that consist of a complete shared library which executes a specific CUDA algorithm.

Getting Started

To know more about the element please refer to the Features and Limitations wiki page.

How to Purchase

Xavier/Contact_Us page has the RidgeRun contact details for purchasing or requesting for the evaluation version.