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Parallel Computing with CUDA
Dmitri Nesteruk
Dmitri Nesteruk
An entry-level course on CUDA - a GPU programming technology from NVIDIA.
Read more
An entry-level course on CUDA - a GPU programming technology from NVIDIA.
Read more
An entry-level course on CUDA - a GPU programming technology from NVIDIA.
This introductory course on CUDA shows how to get started with using the CUDA platform and leverage the power of modern NVIDIA GPUs. It covers the basics of CUDA C, explains the architecture of the GPU and presents solutions to some of the common computational problems that are suitable for GPU acceleration.
Register for this course and see more details by visiting:
OpenCourser.com/course/oxgu3w/parallel
What's inside
Syllabus
GPU Architecture Overview
Tools of The Trade
Introduction to CUDA C
Parallel Programming Patterns
Read more
Syllabus
GPU Architecture Overview
Tools of The Trade
Introduction to CUDA C
Parallel Programming Patterns
Read more
The Many Types of Memory
Thread Cooperation and Synchronization
Atomic Operations
Events and Streams
CUDA in Advanced Scenarios
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces the basics of CUDA C, which is relevant to the latest industry trends
Suitable for learners without prior knowledge of CUDA, making it accessible to beginners
Offers hands-on labs and interactive exercises to promote active learning and practical application
Includes essential topics in GPU computing, such as memory management and atomic operations, providing a solid foundation
Provides a glimpse into advanced CUDA scenarios, preparing learners for potential future use cases
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Activities
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Parallel Computing with CUDA.
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Career center
Learners who complete Parallel Computing with CUDA will develop knowledge and skills
that may be useful to these careers:
Computer Graphics Specialist
Computer Graphics Specialist
Computer Graphics Specialists create and manipulate digital images and visual effects. This course on Parallel Computing with CUDA offers insights into graphics processing, GPU architecture, and memory optimization, which are valuable for professionals in this field.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design and implement artificial intelligence solutions. Parallel Computing with CUDA provides a foundation in GPU-based computing, which has become increasingly important for AI Engineers who leverage GPUs for deep learning and other AI tasks.
High-Performance Computing Engineer
High-Performance Computing Engineer
High-Performance Computing Engineers design and develop high-performance computing systems. Enrolling in Parallel Computing with CUDA is a great choice to prepare for this role as it teaches the basics of CUDA C and GPU architecture.
Data Analyst
Data Analyst
Data Analysts interpret and communicate data to help organizations make informed decisions. Taking Parallel Computing with CUDA can be beneficial to those aspiring to become Data Analysts who wish to gain expertise in GPU-accelerated data analysis techniques.
Financial Analyst
Financial Analyst
Financial Analysts provide financial advice and guidance to individuals and organizations. Parallel Computing with CUDA may be useful for those seeking a career as a Financial Analyst who wish to explore the applications of GPU-based computing in financial modeling and analysis.
Cloud Architect
Cloud Architect
Cloud Architects design, build, and manage cloud computing systems. Those preparing for roles in cloud architecture will benefit from taking Parallel Computing with CUDA to build a foundation in using GPUs to accelerate cloud-based applications.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts collect, analyze, and interpret data to provide insights for businesses. Parallel Computing with CUDA may be of interest to those aspiring to become Business Intelligence Analysts who wish to gain foundational knowledge in GPU-accelerated data analysis techniques.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build, deploy, and maintain machine learning models. Parallel Computing with CUDA offers training in GPU-based computing, which is a valuable skill for ML Engineers who wish to leverage GPUs for model training and inference.
Game Developer
Game Developer
Game Developers design, create, and test video games. CUDA is a popular tool in game development for accelerating graphics processing. This course provides an introduction to CUDA C and GPU architecture, which is useful knowledge for those interested in game development.
Systems Analyst
Systems Analyst
Systems Analysts design and implement computer systems that meet the needs of organizations. This course provides an introduction to parallel computing with CUDA, which is relevant for Systems Analysts who wish to gain expertise in GPU-accelerated computing.
Research Scientist
Research Scientist
Research Scientists conduct scientific research and develop new technologies. Enrolling in Parallel Computing with CUDA is a smart choice for those considering a career in research that involves GPU-powered scientific computing.
Computer Scientist
Computer Scientist
Computer Scientists use their knowledge of computation to design and develop computer systems and applications. This course, with its modules on tools, memory types, and atomic operations, provides a foundation for those entering this field.
Software Engineer
Software Engineer
Software Engineers apply engineering principles to design, develop, deploy, and maintain software systems. Students who wish to specialize in parallel computing may find success in this role. Through this course, you can learn to use hardware parallelism to optimize software.
Data Scientist
Data Scientist
Data Scientists utilize scientific methods, processes, algorithms, and systems to extract knowledge and insights from data. Successful Data Scientists may leverage Parallel Computing with CUDA to learn about GPU-based computing, which can aid in handling large datasets.
Computational Scientist
Computational Scientist
Computational Scientists contribute to the gathering and analysis of scientific data via advanced computing techniques. This course, Parallel Computing with CUDA, may be useful for those who aim to work with graphical processing units (GPUs) for scientific purposes.
For more career information including salaries, visit:
OpenCourser.com/course/oxgu3w/parallel
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Parallel Computing with CUDA.
Save
CUDA Programming: A Developer's Guide to Parallel Computing with GPUs provides detailed instructions on how to maximize the power of the CUDA platform and offers plenty of examples and exercises that help enforce the acquired knowledge.
Programming Massively Parallel Processors serves as a foundational reference for understanding how to efficiently write parallel programs for modern multicore CPUs and GPUs.
Parallel Computer Architecture: A Hardware/Software Approach provides an in-depth exploration of the architectural principles behind parallel computing systems and their implications for software design.
Computer Architecture: A Quantitative Approach serves as a comprehensive reference for understanding the underlying architectural principles of modern computer systems.
Modern GPU Architecture offers an up-to-date overview of the architectural advancements in modern GPUs and their implications for programming and performance optimization.
GPU Gems 3 collection of expert contributions that explore advanced GPU programming techniques and best practices.
Effective C++: 59 Specific Ways to Improve Your Programs and Designs serves as a valuable reference for C++ programmers, offering practical advice on how to write efficient and maintainable code.
Numerical Recipes 3rd Edition: The Art of Scientific Computing comprehensive reference for numerical methods and algorithms, providing a valuable resource for those seeking to implement and optimize GPU-accelerated algorithms.
Computer Organization and Design offers a comprehensive overview of computer architecture and organization, providing foundational knowledge for understanding the design and implementation of modern computing systems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/oxgu3w/parallel
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Time
252 hours
Level
Intermediate
Via
Pluralsight
Instructor
Dmitri Nesteruk
Language
English
Good to know
Introduces the basics of CUDA C, which is relevant to the latest industry trends
Suitable for learners without prior knowledge of CUDA, making it accessible to beginners
Offers hands-on labs and interactive exercises to promote active learning and practical application
Includes essential topics in GPU computing, such as memory management and atomic operations, providing a solid foundation
Provides a glimpse into advanced CUDA scenarios, preparing learners for potential future use cases
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