This course is part of the GPU Programming Specialization

Chancellor Thomas Pascale

Offered By

GPU Programming SpecializationJohns Hopkins University

About this Course

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. The Thrust library’s capabilities in representing common data structures and associated algorithms will be introduced. Using cuDNN and cuTensor they will be able to develop machine learning applications that help with object detection, human language translation and image classification.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 4 of 4 in the

GPU Programming Specialization

Intermediate Level

Data Science, Algorithms, and/or programming background

Approx. 25 hours to complete

English

Subtitles: English

What you will learn

You will learn to develop software that performs high-level mathematics operations using libraries such as cuFFT and cuBLAS.

You will learn to use the Thrust library to perform a number of data manipulation and data structures that abstract away memory management.

You will learn to develop machine learning software for a variety of purposes using neural networks modeled using the cuTensor and cuDNN libraries.

Skills you will gain

Data Science
Artificial Neural Network
Machine Learning
C++
Linear Algebra

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 4 of 4 in the

GPU Programming Specialization

Intermediate Level

Data Science, Algorithms, and/or programming background

Approx. 25 hours to complete

English

Subtitles: English

Instructor

Chancellor Thomas Pascale

Computer Science

297 Learners

4 Courses

Offered by

Johns Hopkins University

The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.

Syllabus - What you will learn from this course

Week

Week 1

5 hours to complete

Course Overview

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

5 hours to complete

4 videos (Total 19 min)

Week

Week 2

4 hours to complete

cuFFT

cuFFT provides the ability to perform fast Fourier transforms (FFTs) on large datasets. Students will learn of common use cases such as fast multiplication of large polynomials, signal processing, and matrix operations. They will use this library to develop software that process audio or video signals.

4 hours to complete

6 videos (Total 33 min), 1 reading, 1 quiz

Week

Week 3

4 hours to complete

CUDA Linear Algebra

The CUDA Toolkit includes a number of linear algebra libraries, such as cuBLAS, NVBLAS, cuSPARSE, and cuSOLVER. Students will learn the different capabilities and limitations of many of them and apply that knowledge to compute matrix dot products, determinant, and finding solutions to complex linear systems.

4 hours to complete

6 videos (Total 26 min), 1 reading, 1 quiz

Week

Week 4

3 hours to complete

The CUDA Thrust Library

Most developers utilize data structures beyond the primitives and pointers that make up the core of CUDA programmers, which makes pure CUDA development difficult. Students will learn about the Thrust library that adds the vector data structure and associated algorithms that allow for simplification of their code. Students will create software that transform, reduction, and sort large datasets.

3 hours to complete

7 videos (Total 32 min), 1 reading, 1 quiz

About the GPU Programming Specialization

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.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

CUDA Advanced Libraries