Difference between revisions of "Jetson Xavier NX/RidgeRun Products/GstCUDA"
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== Getting Started == | == Getting Started == | ||
| − | To know more about the element please refer to the [[GstCUDA | + | To know more about the element please refer to the [[GstCUDA | GstCUDA main]] wiki page. |
==How to Purchase== | ==How to Purchase== | ||
Revision as of 23:05, 19 August 2020
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Contents
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.
Promo Video
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 an 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 ad-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 GstCUDA main wiki page.
How to Purchase
Jetson_Xavier_NX/Contact_Us page has the RidgeRun contact details for purchasing or requesting for the evaluation version.
