Template:RidgeRun CUDA Optimisation Guide/Main contents: Difference between revisions
mNo edit summary |
mNo edit summary |
||
Line 31: | Line 31: | ||
# '''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. | ||
|- | |- |
Latest revision as of 07:55, 30 August 2022
RidgeRun CUDA Optimisation Guide ! RidgeRun CUDA Optimisation Guide |
| |||||||||||||||||||||||||
| ||||||||||||||||||||||||||
RidgeRun CUDA Optimisation Guide | ||||||||||||||||||||||||||
This manual does not try to replace either the Best Practices Guide or the Programming Guide. Instead, it summarises some of their contents and encourages the developer to have a look at it in case of doubts. Additionally, it summarises some tips/hints presented in seminars and lectures in High-Performance Computing. 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:
| ||||||||||||||||||||||||||
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:
|