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Fundamentals of Accelerated Computing with CUDA Python

This workshop teaches you the fundamental tools and techniques for running GPU-accelerated Python applications using CUDA® GPUs and the Numba compiler. You’ll work though dozens of hands-on coding...

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$500 USD
Course Code NV-ACC-CUDA-PYTH
Duration 1 day
Available Formats Classroom

This workshop teaches you the fundamental tools and techniques for running GPU-accelerated Python applications using CUDA® GPUs and the Numba compiler. You’ll work though dozens of hands-on coding exercises and, at the end of the training, implement a new workflow to accelerate a fully functional linear algebra program originally designed for CPUs, observing impressive performance gains. After the workshop ends, you’ll have additional resources to help you create new GPU-accelerated applications on your own.

Skills Gained

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.

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

Course Details

Workshop Outline

Introduction

Introduction to CUDA Python with Numba

  • Begin working with the Numba compiler and CUDA programming in Python.
  • Use Numba decorators to GPU-accelerate numerical Python functions.
  • Optimize host-to-device and device-to-host memory transfers.

Custom CUDA Kernels in Python with Numba

  • Learn CUDA’s parallel thread hierarchy and how to extend parallel program possibilities.
  • Launch massively parallel custom CUDA kernels on the GPU.
  • Utilize CUDA atomic operations to avoid race conditions during parallel execution.

Multidimensional Grids, and Shared Memory for CUDA Python with Numba

  • Learn multidimensional grid creation and how to work in parallel on 2D matrices.
  • Leverage on-device shared memory to promote memory coalescing while reshaping 2D matrices.

Final Review