Fundamentals of Accelerated Computing with CUDA Python

This workshop teaches you the fundamental tools and techniques for running GPU-accelerated Python applications using CUDA® GPUs and the Numba compiler. You’ll work though dozens of hands-on coding exercises and, at the end of the training, implement a new workflow to accelerate a fully functional linear algebra program originally designed for CPUs, observing impressive performance gains. After the workshop ends, you’ll have additional resources to help you create new GPU-accelerated applications on your own.

• GPU-accelerate NumPy ufuncs with a few lines of code.
• Configure code parallelization using the CUDA thread hierarchy.
• Write custom CUDA device kernels for maximum performance and flexibility.
• Use memory coalescing and on-device shared memory to increase CUDA kernel bandwidth.

• Basic Python competency, including familiarity with variable types, loops, conditional statements, functions, and array manipulations
• NumPy competency, including the use of ndarrays and ufuncs
• No previous knowledge of CUDA programming is required