Template:RidgeRun CUDA Optimisation Guide/Main contents: Difference between revisions
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# '''Pitfalls when optimising''': trying to optimise a non-optimisable application, some comments from people during code review, and so on. | # '''Pitfalls when optimising''': trying to optimise a non-optimisable application, some comments from people during code review, and so on. | ||
# '''Case studies''': stitching case in a brief. | # '''Case studies''': stitching case in a brief. | ||
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Revision as of 17:44, 6 October 2021
RidgeRun CUDA Optimisation Guide ! RidgeRun CUDA Optimisation Guide |
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RidgeRun CUDA Optimisation Guide | ||||||||||||||||||||||||||
The objective of this guide is to introduce the developers to accelerate algorithms that are currently working on GPU. This guide also examines some applications where GPU underperforms compared to other hardware accelerators such as the VIC (Vision Image Compositor), the NVDLA (NVIDIA Deep Learning Accelerator), ISP (Image Signal Processor) or, either, the CPU. It is highly recommended to be already familiar with the CUDA programming style. The manual will address:
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RidgeRun support | ||||||||||||||||||||||||||
RidgeRun provides support for embedded Linux development for NVIDIA's platforms, specializing in the use of hardware accelerators in multimedia applications. RidgeRun's products take full advantage of the accelerators that NVIDIA exposes to perform transformations on the video streams achieving great performance on complex processes.This page contains detailed guides on CUDA Optimisation and start using its full capabilities. To get up-to-speed with your RidgeRun CUDA Optimisation Guide, start by clicking below:
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