It is a computer Architecture question which entails a long answer. I will try to keep it very simple on the risk of being inaccurate. You basically self-answered your question by asking do CUDA core handle branch prediction, the answer is NO. A CPU core has to handle each single operation a computer does, calculation, … Read more
In the specific case you mention, shared memory is not useful, for the following reason: each data element is used only once. For shared memory to be useful, you must use data transferred to shared memory several times, using good access patterns, to have it help. The reason for this is simple: just reading from … Read more
This type of error message frequently refers to the launch configuration parameters (grid/threadblock dimensions in this case, could also be shared memory, etc. in other cases). When you see a message like this it’s a good idea just to print out your actual config parameters before launching the kernel, to see if you’ve made any … Read more
Like eval said, it is because pytorch1.13 automatically install nvidia_cublas_cu11, nvidia_cuda_nvrtc_cu11, nvidia_cuda_runtime_cu11 and nvidia_cudnn_cu11. While I have my own CUDA toolKit already installed, I have the same problem. In my case, I used pip uninstall nvidia_cublas_cu11 and solved the problem. I think the PyTorch team should solve this issue, since users often have their own … Read more
You need to ensure that your driver version matches or exceeds your CUDA Toolkit version. For 2.3 you need a 190.x driver, for 3.0 you need 195.x and for 3.1 you need 256.x (actually anything up to the next multiple of five is ok, e.g. 258.x for 3.1). You can check your driver version by … Read more
CUDA now supports printfs directly in the kernel. For formal description see Appendix B.16 of the CUDA C Programming Guide.
Met this question when installing cudatoolkit of 10.1 with PyTorch 1.4. There is a conda-forge package https://anaconda.org/conda-forge/cudatoolkit-dev. After installing this, nvcc as well as other CUDA libraries will be then available at /home/li/anaconda3/envs/<env_name>/pkgs/cuda-toolkit in bin/ and lib/.
These are all barriers. Barriers prevent code execution beyond the barrier until some condition is met. cudaDeviceSynchronize() halts execution in the CPU/host thread (that the cudaDeviceSynchronize was issued in) until the GPU has finished processing all previously requested cuda tasks (kernels, data copies, etc.) cudaThreadSynchronize() as you’ve discovered, is just a deprecated version of cudaDeviceSynchronize. … Read more
Running sudo apt-get purge nvidia-* and reinstalling the drivers using sudo apt-get install nvidia-375 solved it. Just for the record, the first time I updated the drivers using the GUI (Additional Drivers tab in Software & Updates).
I tried the following and it worked: Change in FindCUDA.cmake the nppi library to the several splitted ones. This has to be done in 3 places. Remember this change is just to make it work with CUDA 9.0, I am not doing checks for version or anything, which should be done if you plan to … Read more