Tesla C2050 versus Core i5-760 2.8Ghz, SSE, TBB Reference link is provided in the end which describes the test and conditions In general, it is a good idea to develop the application using the CPU part of OpenCV, and then accelerate it with the GPU module.Ī comparison of performance on CPU and GPU provided below: Although the user has to write some additional code to start using the GPU, this approach is both flexible and allows more efficient computations. ![]() This design provides the user an explicit control on how data is moved between CPU and GPU memory. The GPU module is designed as host API extension. ![]() OpenCV GPU module is written using CUDA, therefore it benefits from the CUDA ecosystem. Other parts also support massive parallel computations and often naturally map to GPU architectures. Significant part of Computer Vision is image processing, the area that graphics accelerators were originally designed for. From a user's perspective, applications simply run much faster. GPU-accelerated computing offloads compute-intensive portions of the application to the GPU, while the remainder of the code still runs on the CPU. Operating System => Linux Ubuntu 16.04 LTS.Goal: Compile OpenCV 3.X using CUDA and FFMpeg to accelerate Deep Learning applications consisting image/video processing Compiling OpenCV with CUDA and FFMpeg on Ubuntu 16.04
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