As you can see from the transformation example, it is not always obvious whether the GPU will be efficient even for tasks that parallel well. The reason for this is the overhead for transferring data from the computer’s RAM to the video card memory (in game consoles, by the way, the memory is shared between the CPU and GPU, and there is no need to transfer data).

One of the characteristics of a graphics card is memory bandwidth, which determines the theoretical bandwidth of the card. For Tesla k80 it is 480 GB / s, for Tesla v100 it is already 900 GB / s.

Also, the bandwidth will be affected by the PCI Express version and the implementation of how you will transfer data to the card, for example, this can be done in several parallel streams.

Time to send data to GPU, sort and send data back to RAM in ms

HtoD – transferring data to the video card, GPU Execution – sorting on the video card, DtoH – copy data from video card to RAM

The first thing to note is that reading data from a video card is faster than writing it there.

Second, when working with a video card, you can get latency from 350 microseconds, and this may already be enough for some low latency applications.

If you figured out the resource limitation, then the next logical question is: what if the server has several video cards?

Containers and GPU

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Again, you can decide at the application level which GPU it will use.

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Another more convenient way is Docker containers. You can use regular containers, but NVIDIA offers its own NGC containers, with optimized versions of various software, libraries and drivers. For one container, you can limit the number of GPUs used and their visibility for the container. Overhead on container usage is about 3%.