We may earn an affiliate commission when you visit our partners.

GPU Programming

This specialization is intended for data scientists and software developers to create software that uses commonly available hardware. Students will be introduced to CUDA and libraries that allow for performing numerous computations in parallel and rapidly. Applications for these skills are machine learning, image/audio signal processing, and data processing.

Enroll now

Or subscribe to Coursera Plus

And get unlimited access to Coursera

Help others find Specialization from Coursera by sharing it with your friends and followers:

Facebook

Copy Link

What's inside

Four courses

Introduction to Concurrent Programming with GPUs

(0 hours)

This course introduces concurrent programming with GPUs. It covers CPU/GPU architectures, multithreaded programming in C and Python, and CUDA software/hardware.

View more details for this course

Introduction to Parallel Programming with CUDA

(0 hours)

This course prepares students to develop code that processes large amounts of data in parallel on Graphics Processing Units (GPUs). Students will learn to implement software that solves complex problems using Nvidia CUDA on consumer to enterprise-grade GPUs. The focus is on hardware and software capabilities, including the use of 100s to 1000s of threads and various forms of memory.

View more details for this course

CUDA at Scale for the Enterprise

(0 hours)

This course will aid students in learning concepts that scale the use of GPUs and the CPUs that manage their use beyond the most common consumer-grade GPU installations. They will learn how to manage asynchronous workflows, sending and receiving events to encapsulate data transfers and control signals.

View more details for this course

CUDA Advanced Libraries

(0 hours)

This course will complete the GPU specialization, focusing on the leading libraries distributed as part of the CUDA Toolkit. Students will learn how to use CuFFT, and linear algebra libraries to perform complex mathematical computations.

View more details for this course

Learning objectives

Develop cuda software for running massive computations on commonly available hardware

Utilize libraries that bring well-known algorithms to software without need to redevelop existing capabilities

Develop cuda software for running massive computations on commonly available hardware
Utilize libraries that bring well-known algorithms to software without need to redevelop existing capabilities

Save this collection

Save GPU Programming to your list so you can find it easily later:

Save

Time

71 hours

Level

Intermediate

Via

Coursera

Institution

Johns Hopkins University

Instructor

Chancellor Thomas Pascale

Level

Intermediate

Share Specialization from Coursera

Facebook

Link

Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.