I will give an overview of CuPy, an open-source Python library that serves as a high-level interface to the proprietary NVIDIA CUDA language. It makes writing lightning-fast GPU accelerated code almost as easy as using Python’s standard NumPy library, as it re-implements much of NumPy’s API. Due to the ubiquitous nature of NumPy in scientific Python applications, CuPy will likely make porting your existing CPU code to a GPU as painless as possible.
This tutorial will include demos of essential features of CuPy, along with some general overview of GPUs, and some of the standard profiling tools you can use to measure your speed improvements and find the lines of code that are slowing you down.
People are encouraged to bring their laptops if they have access (possibly remotely) to a machine with a modern NVIDIA GPU, along with CUDA and CuPy installed. It looks like the RIT research clusters (rc.rit.edu) only have older versions of CUDA (CuPy works in CUDA 8 and 9, but they have 7 and below), so this may be hit or miss.