So 4 blocks each requiring 2,048 Bytes gives a total requirement of 8,192 KB of shared memory which is 50% of the available shared memory per streaming. Use pip install -pre cupy-cudaXXX if you want to install pre-release (development) versions.| If you remember from the previous article about the CUDA thread execution model, thread blocks of size 16 x 16 will allow 4 resident blocks to be scheduled per streaming multiprocessor. | If you installed CuPy via wheels, you can use the installer command below to setup these libraries in case you don't have a previous installation: $ python -m _library -cuda 11.2 -library cutensor. There is just one point which is not yet. Using the book CudaByExample I could play with its code examples in order to understand the capabilities of CUDA on old as well as on newer GPUs. Hello, I have fundamental experience in c/c++ and started to learn CUDA C some time ago.
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