Introduction to Parallel Programming with CUDA
Master high-performance GPU programming with CUDA framework. Transform CPU algorithms into parallel GPU implementations.
Master high-performance GPU programming with CUDA framework. Transform CPU algorithms into parallel GPU implementations.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full GPU Programming Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
2.2
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What you'll learn
Develop C/C++ software for CPUs and NVIDIA GPUs using CUDA
Transform sequential algorithms into parallel GPU implementations
Manage different types of GPU memory for optimal performance
Implement multi-dimensional thread and block configurations
Optimize code for massive parallel execution
Skills you'll gain
This course includes:
2.1 Hours PreRecorded video
5 quizzes, 5 programming assignments
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FullTime access
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There are 5 modules in this course
This comprehensive course focuses on parallel programming using NVIDIA's CUDA framework. Students learn to transform sequential CPU algorithms into parallel GPU implementations that can execute hundreds to thousands of times simultaneously. The curriculum covers essential concepts including thread management, memory hierarchies (global, shared, constant, and register), and performance optimization techniques. Through hands-on programming assignments, students develop practical skills in GPU computing for large-scale data processing.
Course Overview
Module 1 · 3 Hours to complete
Threads, Blocks and Grids
Module 2 · 5 Hours to complete
Host and Global Memory
Module 3 · 5 Hours to complete
Shared and Constant Memory
Module 4 · 3 Hours to complete
Register Memory
Module 5 · 3 Hours to complete
Fee Structure
Instructor
Innovator in Computer Science Education
Chancellor Pascale has been a faculty member at Johns Hopkins University's Whiting School of Engineering for over 10 years, specializing in the Computer Science department. He earned his undergraduate degree in Computer Science from Drexel University and a Master’s degree from Johns Hopkins University. With over 15 years of experience in software and service development, he focuses on web applications, AI/ML for image and language translation, and network management. Chancellor Pascale was awarded the Fulbright-Nehru Fellowship, where he led a three-week workshop on Linux API development at Stella Maris College in Chennai, India.
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2.2 course rating
11 ratings
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