This workshop teaches you the fundamental tools and techniques for running GPU-accelerated Python applications using CUDA® GPUs and the Numba compiler.

Learning Objectives

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

Technologies:

Numba, NumPy

Certificate:

Upon successful completion of the assessment, participants will receive an NVIDIA DLI certificate to recognize their subject matter competency and support professional career growth.

Hardware Requirements:

Desktop or laptop computer capable of running the latest version of Chrome or Firefox. Each participant will be provided with dedicated access to a fully configured, GPU-accelerated server in the cloud.

Workshop Outline

Introduction

• Meet the instructor.
• Create an account at courses.nvidia.com/join

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

• Review key learnings and wrap up questions.
• Complete the assessment to earn a certificate.
• Take the workshop survey.