Johns Hopkins University
Introduction to Concurrent Programming with GPUs
Johns Hopkins University

Introduction to Concurrent Programming with GPUs

This course is part of GPU Programming Specialization

Taught in English

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8,115 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

Beginner level

Recommended experience

19 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • Students will learn how to develop concurrent software in Python and C/C++ programming languages.

  • Students will gain an introductory level of understanding of GPU hardware and software architectures.

Details to know

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Assessments

4 quizzes

Course

Gain insight into a topic and learn the fundamentals

Beginner level

Recommended experience

19 hours (approximately)
Flexible schedule
Learn at your own pace

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Build your subject-matter expertise

This course is part of the GPU Programming Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
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There are 5 modules in this course

The purpose of this module is for students to understand how the course will be run, topics, how they will be assessed, and expectations.

What's included

3 videos4 readings1 programming assignment2 discussion prompts1 ungraded lab

In order to create software that process greater amounts of data at faster speeds, software operating systems, programming languages, and frameworks require strategies for accessing and modification of data in a manner that maximizes speed, while minimizing the possibility of data being in incorrect states. In this module, students will be presented canonical concurrency problems such as the Dining Philosophers. Additionally, they will learn how operating systems and programming languages handle these problems, and discuss real world big data concurrency applications.

What's included

6 videos3 readings2 programming assignments1 discussion prompt1 ungraded lab

Modern programming languages allow developers to create software with complex logic for manipulation of data in parallel, taking advantage of the multiple CPU cores in most computers. Students will develop simple software, written in the C++ and Python 3 programming languages, that process data sets concurrently.

What's included

6 videos2 readings2 quizzes2 programming assignments1 discussion prompt2 ungraded labs

The purpose of this module is for students to understand the basis in hardware and software that CUDA uses. This is required to appropriately develop software to optimally take advantage of GPU resources.

What's included

9 videos1 reading1 quiz2 programming assignments1 discussion prompt1 ungraded lab

The purpose of this module is for students to understand the principles of developing CUDA-based software.

What's included

6 videos1 reading1 quiz2 programming assignments1 discussion prompt1 ungraded lab

Instructor

Instructor ratings
2.5 (23 ratings)
Chancellor Thomas Pascale
Johns Hopkins University
4 Courses10,800 learners

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