About this Course

9,835 recent views

This course will help prepare students for developing code that can process large amounts of data in parallel on Graphics Processing Units (GPUs). It will learn on how to implement software that can solve complex problems with the leading consumer to enterprise-grade GPUs available using Nvidia CUDA. They will focus on the hardware and software capabilities, including the use of 100s to 1000s of threads and various forms of memory.

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 2 of 4 in the

GPU Programming Specialization

Intermediate Level

Some experience in C/C++ programming

Approx. 21 hours to complete

English

What you will learn

Students will learn how to utilize the CUDA framework to write C/C++ software that runs on CPUs and Nvidia GPUs.
Students will transform sequential CPU algorithms and programs into CUDA kernels that execute 100s to 1000s of times simultaneously on GPU hardware.

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 2 of 4 in the

GPU Programming Specialization

Intermediate Level

Some experience in C/C++ programming

Approx. 21 hours to complete

English

Instructor

Chancellor Thomas Pascale

Instructor and Software Engineer

Computer Science

5,030 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), 4 readings, 1 quiz

Week2

Week 2

5 hours to complete

Threads, Blocks and Grids

5 hours to complete

8 videos (Total 50 min), 1 reading, 4 quizzes

8 videos

Kernel Execution6mDivide and Conquer to GPU Algorithms5mModule 2 Lab Overview Video4mModule 2 Randomized Data Search Assignment Overview Video4mThreads and Blocks7mThreads, Blocks, and Grids6mMultidimensional Gaussian Blur Kernel Example7mModule2 Image Processing Assignment Overview Videos6m

1 reading

Nvidia CUDA Software and Hardware Reading Materials10m

2 practice exercises

CPU to GPU Algorithm Conversion Quiz30mMultidimensional Data and Computation on the GPU Quiz30m

Week3

Week 3

5 hours to complete

Host and Global Memory

5 hours to complete

8 videos (Total 23 min)

8 videos

Nvidia GPU Device Global Memory3mLinux CLI GPU Device Identification1mGPU Device Global Memory Investigation2mNvidia GPU Device Investigation Commands Lab Overview Video1mHost Memory Allocation5mDevice Global Memory Allocation3mHost and Device Global Memory Allocation Lab Overview Video3mAllocation and Assignment of Different Types of Host and Global Memory Overview Video2m

1 practice exercise

CPU and GPU Global Memory Quiz30m

Week4

Week 4

4 hours to complete

Shared and Constant Memory

4 hours to complete

6 videos (Total 22 min)

6 videos

Nvidia GPU Device Shared and Constant Memory Video Lecture3mGPU Device Shared and Constant Memory Investigation3mGPU Device Shared Memory Allocation3mGPU Device Constant Memory Allocation4mCUDA Shared and Constant Memory Image Processing Lab Overview Video3mCUDA Shared and Constant Memory Image Manipulation Assignment Overview Video4m

1 practice exercise

CUDA Constant and Shared Memory Quiz15m

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.

Introduction to Parallel Programming with CUDA

About this Course

What you will learn

Skills you will gain