Fundamentals of Accelerated Computing with CUDA Python

Price
$500.00 USD

Duration
1 Day

Delivery Methods
Virtual Instructor Led
Private Group

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Course Overview

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.

Course Objectives

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.

Who Should Attend?

Developers who use Python

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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

Agenda

Course Introduction
Introduction to CUDA Python with Numba
Custom CUDA Kernels in Python with Numba
Multidimensional Grids and Shared Memory for CUDA Python with Numba
Final Review

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