Prerequisites
The participant should:
- be familiar with Python
- be comfortable working in Jupyter
- have the ability to read and understand C code
Preferred:
- knowledge of NumPy
- familiarity with high-performance computing concepts
Prerequisites
The participant should:
- be familiar with Python
- be comfortable working in Jupyter
- have the ability to read and understand C code
Preferred:
- knowledge of NumPy
- familiarity with high-performance computing concepts
| Setup | Download files required for the lesson | |
| 00:00 | 1. Introduction |
What is a Graphics Processing Unit?
Can a GPU be used for anything else than graphics? Are GPUs faster than CPUs? |
| 00:15 | 2. Using your GPU with CuPy |
How can I increase the performance of code that uses NumPy?
How can I copy NumPy arrays to the GPU? |
| 02:15 | 3. Accelerate your Python code with Numba | How can I run my own Python functions on the GPU? |
| 03:15 | 4. A Better Look at the GPU | How does a GPU work? |
| 03:35 | 5. Your First GPU Kernel |
How can I parallelize a Python application on a GPU?
How to write a GPU program? What is CUDA? |
| 04:45 | 6. Registers, Global, and Local Memory |
What are registers?
How to share data between host and GPU? Which memory is accessible to threads and thread blocks? |
| 05:30 | 7. Shared Memory and Synchronization |
Is there a way to share data between threads of a same block?
Can threads inside a block wait for other threads? |
| 06:25 | 8. Constant Memory | Is there a way to have a read-only cache in CUDA? |
| 07:05 | 9. Concurrent access to the GPU | Is it possible to concurrently execute more than one kernel on a single GPU? |
| 07:45 | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.