This workshop teaches you the fundamental tools and techniques for running GPU-accelerated Python applications using CUDA® GPUs and the Numba compiler.
Learning Objectives
At the conclusion of the workshop, you’ll have an understanding of the fundamental tools and techniques for GPU-accelerated Python applications with CUDA and Numba:
- 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.
Workshop Details
Duration: 8 hours
Prerequisites:
- 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
Technologies: Numba, NumPy
Certificate: Upon successful completion of the assessment, participants will receive an NVIDIA DLI certificate to recognize their subject matter competency and support professional career growth.
Hardware Requirements: Desktop or laptop computer capable of running the latest version of Chrome or Firefox. Each participant will be provided with dedicated access to a fully configured, GPU-accelerated server in the cloud.
Language: English
Price: No course fees apply.
Registration Deadline: 20. July 2023 at 23:59
