About this Course

3,126 recent views

This course will aid in students in learning in 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. Also, students will walk through application of GPUs to sorting of data and processing images, implementing their own software using these techniques and libraries.
By the end of the course, you will be able to do the following:

- Develop software that can use multiple CPUs and GPUs
- Develop software that uses CUDA’s events and streams capability to create asynchronous workflows
- Use the CUDA computational model to to solve canonical programming challenges including data sorting and image processing

To be successful in this course, you should have an understanding of parallel programming and experience programming in C/C++.

This course will be extremely applicable to software developers and data scientists working in the fields of high performance computing, data processing, and machine learning.

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.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 3 of 4 in the

GPU Programming Specialization

Intermediate Level

Some experience in C/C++ programming

Approx. 28 hours to complete

English

What you will learn

Students will learn to develop software that can be run in computational environments that include multiple CPUs and GPUs.
Students will develop software that uses CUDA to create interactive GPU computational processing kernels for handling asynchronous data.
Students will use CUDA, hardware memory capabilities, and algorithms/libraries to solve programming challenges including image processing.

Skills you will gain

Cuda
Algorithms
C/C++
GPU
Nvidia

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.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 3 of 4 in the

GPU Programming Specialization

Intermediate Level

Some experience in C/C++ programming

Approx. 28 hours to complete

English

Instructor

Chancellor Thomas Pascale

Instructor and Software Engineer

Computer Science

5,110 Learners

4 Courses

Syllabus - What you will learn from this course

Week1

Week 1

4 hours to complete

Course Overview

4 hours to complete

3 videos (Total 11 min), 1 reading, 1 quiz

Week2

Week 2

10 hours to complete

Multiple CPU/GPU Systems

In professional settings, use of one CPU managing one GPU, is not a viable configuration to solve complex challenges. Students will apply CUDA capabilities for allowing multiple CPUs to communicate and manage software kernels on multiple GPUs. This will allow for scaling the size of input data and computational complexity. Students will learn the advantages and limitations of this form of synchronous processing.

10 hours to complete

7 videos (Total 32 min)

Week3

Week 3

4 hours to complete

CUDA Events and Streams

4 hours to complete

5 videos (Total 28 min), 2 readings, 1 quiz

Week4

Week 4

5 hours to complete

Sorting Using GPUs

5 hours to complete

11 videos (Total 57 min), 1 reading, 1 quiz

11 videos

Using Input Data to Develop GPU Pseudocode8mSorting Algorithm GPU Pseudocode Bubble Sort5mSorting Algorithm GPU Pseudocode Radix Sort9mSorting Algorithm GPU Pseudocode Quick Sort7mMemory and GPU Pseudocode Bubble Sort3mMemory and GPU Pseudocode Radix Sort2mMemory and GPU Pseudocode Quick Sort3mSorting Algorithms Lab Activity Bubble Sort3mSorting Algorithms Lab Activity Radix Sort4mSorting Algorithms Lab Activity Quick Sort6mSorting Algorithms Assignment3m

1 reading

GPU Sort Algorithm Reading List10m

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.
Can I program on my own desktop/laptop
Yes, but for grading purposes you will still need to upload any software artifacts (source code, header files, etc.) into the Coursera lab environment.

More questions? Visit the Learner Help Center.

CUDA at Scale for the Enterprise

About this Course

What you will learn

Skills you will gain