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Python Getting Started

3 Labs · 2 Credits · 1h 53m

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To get a basic understanding the main approaches to GPU Compute programming using Python

Introduction to Accelerated Computing

Learn about the three techniques for accelerating code on a GPU; Libraries, Directives like OpenACC, and writing code directly in CUDA-enabled langauges. In 45 minutes, you will work through a few different exercises demonstrating the potential speed-ups and ease of use of porting to the GPU.

Icon  intro Introductory Free 45 Minutes

Accelerating Applications with GPU-Accelerated Libraries in Python

Learn how to accelerate your Python application using GPU drop-in libraries to harness the massively parallel power of NVIDIA GPUs. In less than an hour, you will work through three exercises, including:

  • Use a Python profiler to determine which part of the code is consuming the most amount of time
  • Use a cuRAND API call to optimize this portion of code
  • Profile again and use the CUDA Runtime API to optimize data movement to achieve more application speed-up

Please read instructions below before starting lab!

Icon  intro Introductory 1 Credit 50 Minutes

Accelerating Applications with CUDA Python

Learn how to accelerate your Python application using CUDA to harness the massively parallel power of NVIDIA GPUs. In less than an hour, you will work through three exercises, including:

  • Hello Parallelism!
  • Accelerate the simple SAXPY algorithm
  • Accelerate a basic Matrix Multiply algorithm with CUDA

Icon  intro Introductory 1 Credit 50 Minutes