GPU Processing

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GPU Processing

GPUs have thousands of cores to process parallel workloads efficiently.

Bikal EyeSoft and Bikal EyeSphere Video Management and Video Analytic software is programmed to off load to the Nvidia GPU. The Operating System (OS) will run on the CPU.

Benefits of this are:

- Manage more cameras with a small NVR and Server footprint (fewer servers)
- Apply more sophisticated video analytics
- Apply more data, such as Big Data, from different sources to sophisticated predictive algorithms.

GPU accelerated computing is the utilization of a Graphics Processor Unit (GPU) together with a CPU to quicken experimental, investigation, and undertaking applications. Spearheaded in 2007 by NVIDIA, GPU accelerates agents now power processing datacenters in government labs, colleges, ventures, and small and-medium organizations around the globe. GPUs are accelerates applications in stages extending from autos, to cell telephones, tablets, robots and CCTV (Surveillance Cameras).


GPU accelerated computing offers remarkable application execution by offloading register the serious parts of the application to the GPU, while the rest of the code still runs on the CPU. From a client's point of view, applications basically run fundamentally speedier.


A simple way to understand the difference between a CPU and GPU is to compare how they process tasks. A CPU consists of a few cores optimized for sequential serial processing while a GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously.

GPUs have thousands of cores to process parallel workloads efficiently.

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