Introduction to Concurrent Programming with GPUs
Master GPU programming fundamentals with CUDA, Python, and C++. Learn parallel processing for high-performance computing.
Master GPU programming fundamentals with CUDA, Python, and C++. Learn parallel processing for high-performance computing.
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.
2.4
(26 ratings)
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What you'll learn
Develop concurrent software in Python and C/C++
Understand GPU hardware and software architectures
Implement parallel processing algorithms
Program using CUDA framework
Optimize code for GPU execution
Skills you'll gain
This course includes:
2.3 Hours PreRecorded video
4 quizzes, 8 programming assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course introduces students to concurrent programming with a focus on GPU computing. Starting with fundamental concepts of parallel programming on CPUs and GPUs, students learn about threading, synchronization, and common concurrency patterns. The curriculum covers both Python and C++ implementations before diving into NVIDIA GPU architecture and CUDA programming. Special emphasis is placed on practical applications with hands-on programming assignments and real-world examples.
Course Overview
Module 1 · 3 Hours to complete
Core Principles of Parallel Programming on CPUs and GPUs
Module 2 · 3 Hours to complete
Introduction to Parallel Programming with C and Python
Module 3 · 6 Hours to complete
NVidia GPU Hardware/Software
Module 4 · 3 Hours to complete
Introduction to GPU Programming
Module 5 · 4 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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2.4 course rating
26 ratings
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