If you use GPU regularly, runtime durations will become shorter and shorter and disconnections more frequent. The cooldown period before you can connect to another GPU will extend from hours to days to weeks. Google tracks everything. They not only know your accounts’ usage but also the usage of accounts that appear related to your … Read more
I solved “NVIDIA-SMI has failed because it couldn’t communicate with the NVIDIA driver” on my ASUS laptop with GTX 950m and Ubuntu 18.04 by disabling Secure Boot Control from BIOS.
I’m one of the rendering software architects at a large VFX and animated feature studio with a proprietary renderer (not Pixar, though I was once the rendering software architect there as well, long, long ago). Almost all high-quality rendering for film (at all the big studios, with all the major renderers) is CPU only. There … Read more
Yes they do. AMD even provides the specification up to the HD4000 series at the moment. Take a look here at AMD’s R700 instruction set reference guide. There is also an open source project called Nouveau that does reverse engineering of the Nvidia instruction sets. Note that Nvidia has a slightly different architecture than AMD … Read more
I am answering my own question. Easiest way to do is use connect to Local Runtime (https://research.google.com/colaboratory/local-runtimes.html) then select hardware accelerator as GPU as shown in (https://medium.com/deep-learning-turkey/google-colab-free-gpu-tutorial-e113627b9f5d).
Check out CUDA by NVidia, IMO it’s the easiest platform to do GPU programming. There are tons of cool materials to read. http://www.nvidia.com/object/cuda_home.html Hello world would be to do any kind of calculation using GPU. Hope that helps.