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Development Programming Languages CUDA

Introduction to GPU computing with CUDA

What does coalescence mean? What is Halo region? And shared memory? Learn the basics of Parallel Computing with CUDA.
Rating: 3.2 out of 53.2 (124 ratings)
624 students
Created by Orange Owl
Last updated 1/2018
English
English
30-Day Money-Back Guarantee

What you'll learn

  • Understanding the basics of parallel programming on GPU.
  • Understanding the basics of GPU architecture.
  • Writing simple programs in CUDA language.

Course content

3 sections • 14 lectures • 1h 49m total length

  • Preview05:25
  • Preview03:18
  • Preview02:20
  • Heterogeneous Computing and NVIDIA Compiler Driver
    03:52
  • CUDA Download Installation and IDEs
    06:30

  • CUDA Program Workflow and Simple Example - Part 01
    09:33
  • Simple Example - Part 02
    04:13
  • Error Checking and Simple Example - Part 03
    09:59

  • CUDA Memories
    13:17
  • CUDA Profiling and the Visual Profiler
    08:33
  • Coalesced Global Memory Accesses
    12:31
  • Shared Memory and Adjacent Differences
    13:17
  • Shared Memory and Moving Average Example
    09:11
  • Two Dimensional Grids
    07:06

Requirements

  • Experience with C/C++

Description

Self-driving cars, machine learning and augmented reality are some of the examples of modern applications that involve parallel computing. 

With the availability of high performance GPUs and a language, such as CUDA, which greatly simplifies programming, everyone can have at home and easily use a supercomputer. 

The aim of this course is to provide the basics of the architecture of a graphics card and allow a first approach to CUDA programming by developing simple examples with a growing degree of difficulty.

Who this course is for:

  • People aiming at a first approach to parallel programming on GPUs.

Instructor

Orange Owl
Consultants
Orange Owl
  • 3.2 Instructor Rating
  • 124 Reviews
  • 624 Students
  • 1 Course

We consult on and develop software for solving challenging technical computing problems in scientific or industrial areas.

We have a 10+ years experience in solving scientific problems by using the appropriate numerical techniques.

Based on our experience on programming parallel architectures, we face computationally intensive problems, optimizing our algorithms for running on multi-core computers, Graphics Processing Units (GPUs) or hybrid resources.

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