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Fundamentals of Accelerated Computing with CUDA Python

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.

Course 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

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