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.
Introduction to Parallel Programming with CUDA
Introduction to Parallel Programming with CUDA
This course is part of GPU Programming Specialization
Instructor: Chancellor Thomas Pascale
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Intermediate level
Recommended experience
21 hours to complete
3 weeks at 7 hours a week
Flexible schedule
Learn at your own pace
What you'll 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'll gain
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Assessments
5 assignments
Taught in English
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This course is part of the GPU Programming Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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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 assignment1 discussion prompt1 ungraded lab
The single most important concept for using GPUs to solve complex and large-scale problems, is management of threads. CUDA provides two- and three-dimensional logical abstractions of threads, blocks and grids. Students will develop programs that utilize threads, blocks, and grids to process large 2 to 3-dimensional data sets.
What's included
8 videos1 reading2 assignments2 programming assignments1 ungraded lab
To manage the access and modification of data in physical memory effectively, students will need to load data into CPU (host) and GPU (global) general-purpose memory. Students will create software that allocates host memory and transfers it into global memory for use by threads. Students will also learn the capabilities and speeds of these types of memories.
What's included
8 videos1 assignment1 programming assignment1 discussion prompt2 ungraded labs
To improve performance in GPU software, students will need to utilized mutable (shared) and static (constant) memory. They will use them to apply masks to all items of a data set, to manage the communication between threads, and use for caching in complex programs.
What's included
6 videos1 assignment1 programming assignment1 discussion prompt1 ungraded lab
In this module, students will learn the benefits and constraints of GPUs most hyper-localized memory, registers. While using this type of memory will be natural for students, gaining the largest performance boost from it, like all forms of memory, will require thoughtful design of software. Students will develop implementations of algorithms using each type of memory and generate performance analysis.
What's included
5 videos1 assignment1 programming assignment1 discussion prompt1 ungraded lab
Instructor
Instructor ratings
2.2 (11 ratings)Offered by
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Frequently asked questions
Yes, but for grading purposes you will still need to upload any software artifacts (source code, header files, etc.) into the Coursera lab environment.
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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.
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.