You’ll learn the fundamental tools and techniques for running GPU-accelerated Python applications in this workshop using CUDA and the NUMBA compiler GPUs. You’ll use a live, cloud-based GPU-enabled development environment to work though dozens of hands-on coding exercises.
Learn how to:
- Write code for a GPU accelerator
- Configure code parallelization using the CUDA thread hierarchy
- Manage and optimize memory migration between the CPU and GPU accelerator
- Generate random numbers on the GPU
- Intermediate GPU memory management techniques
Finish by implementing your new workflow to accelerate a fully functional linear algebra program originally designed for CPUs to observe impressive performance gains.
After the workshop ends, you’ll have additional resources enabling you to create new GPU-accelerated applications on your own.