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CUDA programming Masterclass with C++

Kasun Liyanage

4.0

Based on 1,850 ratings

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Kasun Liyanage

This course is all about CUDA programming. We will start our discussion by looking at basic concepts including CUDA programming model, execution model, and memory model. Then we will show you how to implement advance algorithms using CUDA. CUDA programming is all about performance. So through out this course you will learn multiple optimization techniques and how to use those to implement algorithms. Also we will extensively discuss profiling techniques and some of the tools including nvprof, nvvp, CUDA Memcheck, CUDA-GDB tools in the CUDA toolkit. This course contains following sections.

Answering all those will help you to digest the concepts we discuss here.

This course is the first course of the CUDA master class series we are current working on. So the knowledge you gain here is essential of following those course as well.

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What's inside

Learning objectives

All the basic knowladge about cuda programming
Ability to desing and implement optimized parallel algorithms

Basic work flow of parallel algorithm design
Advance cuda concepts

All the basic knowladge about cuda programming
Ability to desing and implement optimized parallel algorithms
Basic work flow of parallel algorithm design
Advance cuda concepts

Syllabus

Introduction to CUDA programming and CUDA programming model

Very very important

Introduction to parallel programming

Parallel computing and Super computing

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Develops strong programming skills, which are necessary for careers in software and engineering

Teaches advanced algorithms in CUDA, which helps learners become stronger programmers

Taught by Kasun Liyanage, who is recognized for their work in CUDA

Covers advanced concepts like resource partitioning, latency hiding, and occupancy

Examines performance tuning with nvprof, helping learners understand how to optimize their code

Bonus content on image processing with CUDA, adding value to the course

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Reviews summary

Comprehensive cuda for c++ developers

According to learners, this course provides a comprehensive and highly practical introduction to CUDA programming with C++, making it ideal for those seeking to optimize parallel algorithms. Students frequently highlight the instructor's deep knowledge and clear explanations, especially in complex areas like the CUDA memory model, execution model, and optimization techniques. The inclusion of numerous hands-on exercises and practical examples is consistently praised for solidifying understanding. While the course covers a wide range of topics from basics to advanced concepts, some learners note that a strong C++ foundation is essential, and the pace can be quick for those entirely new to parallel computing. Overall, it's considered a highly valuable resource for professional skill development.

Deep dive into performance tuning and parallel algorithm optimization.

"The sections on profiling and optimization techniques were incredibly valuable for real-world performance gains."

"I now have a much better understanding of how to identify bottlenecks and optimize my CUDA kernels."

"The detailed analysis of parallel reduction examples with various unrolling techniques was a highlight."

"Learning to use tools like nvprof effectively has directly impacted my work."

Covers a broad spectrum from fundamental CUDA concepts to advanced optimization.

"It covers everything from the basic programming model to intricate details of optimization and profiling tools like nvprof."

"This course is truly a masterclass; it delves into all the essential aspects of CUDA, including streams and shared memory."

"I found the progression logical, starting with basics and building up to advanced topics like warp shuffle instructions."

"The breadth of topics, from memory management to parallel patterns, is impressive and highly relevant."

Strong emphasis on practical exercises and real-world coding applications.

"The hands-on coding and projects are the strongest part of the course for me, allowing me to immediately apply concepts."

"I really appreciated the practical exercises; they helped solidify my understanding more than just theoretical lectures."

"The course is packed with useful code examples and assignments that mirror real-world optimization problems."

"I learned how to write efficient CUDA kernels because of the practical approach and well-explained demos."

Instructor's profound knowledge and clear explanations are highly valued.

"The instructor possesses deep knowledge of CUDA and explains complex topics with great clarity."

"I found the explanations of the memory model and execution flow particularly insightful; the instructor made it easy to grasp."

"His teaching style is very effective, making even challenging concepts like warp divergence understandable."

"The instructor is truly a master in CUDA; I learned so much from his precise and detailed explanations."

Some advanced topics or bonus sections could be more fleshed out.

"Could use more in-depth coverage on specific advanced optimization techniques or alternative examples for some concepts."

"The bonus image processing section felt a bit tangential and not as integrated as the core material."

"While excellent, some very niche topics could be expanded upon for a true 'masterclass' level."

Requires solid C++ background; not suitable for programming beginners.

"You definitely need a strong C++ background to keep up; this isn't a beginner's C++ course."

"I struggled a bit at first because my C++ wasn't as strong as it should have been for this level of content."

"This course assumes you are already proficient in C++ and comfortable with advanced programming concepts."

"I recommend brushing up on C++ pointers and memory management before diving in."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in CUDA programming Masterclass with C++ with these activities:

Organize and Review Course Materials

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Maximize your learning potential by organizing and reviewing essential course materials regularly.

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Gather lecture notes, slides, assignments, and quizzes.
Create a structured system for organizing and storing these materials.
Review your organized materials periodically to reinforce your understanding.

Review Parallel Computing Concepts

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Strengthen your foundation in parallel computing concepts to enhance your CUDA programming skills.

Browse courses on Parallel Computing

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Review textbooks or online resources on parallel computing principles.
Practice writing simple parallel programs in C++ or Python.

Read 'CUDA by Example'

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Gain a comprehensive understanding of CUDA programming concepts and techniques through this foundational book.

View Cuda by Example on Amazon

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Read and understand the concepts presented in each chapter.
Work through the code examples and exercises provided in the book.

Three other activities

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Practice Parallel Programming in C++ using CUDA

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Sharpen your skills in parallel programming with CUDA by engaging in regular practice drills.

Browse courses on Parallel Programming

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Implement basic CUDA kernels for vector addition, matrix multiplication, and reduction operations.
Optimize CUDA kernels for improved performance.
Troubleshoot common CUDA errors and performance issues.

Explore Advanced Techniques in CUDA Programming

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Delve deeper into advanced CUDA techniques to enhance your understanding and proficiency.

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Follow tutorials on topics such as shared memory optimization, warp synchronization, and atomic operations.
Experiment with different CUDA programming models and their applications.
Implement advanced algorithms like radix sort and prefix sum using CUDA.

Develop a CUDA-Based Project

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Solidify your understanding by applying your CUDA skills to a real-world project.

Browse courses on High-Performance Computing

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Identify a problem or application that can benefit from parallel processing.
Design and implement a CUDA solution for the problem.
Optimize and evaluate the performance of your CUDA implementation.
Present your project findings and share your code with the community.

Career center

Learners who complete CUDA programming Masterclass with C++ will develop knowledge and skills that may be useful to these careers:

High-Performance Computing Architect

High Performance Computing Architects design and build high-performance computing systems, including hardware, software, and networks. They work to optimize system performance, scalability, and efficiency to meet the demands of data-intensive applications. This course in CUDA programming can be highly beneficial for High Performance Computing Architects who want to develop high-performance computing solutions. By understanding CUDA programming, Architects can create more efficient and effective systems for various applications, including scientific research, engineering simulations, and financial modeling.

See salaries and explore the career path for High-Performance Computing Architect

Computational Scientist

Computational Scientists use advanced computational techniques to solve complex scientific problems. They develop and apply mathematical models and algorithms to simulate and analyze physical, biological, and social systems. This course in CUDA programming can be beneficial for Computational Scientists who want to develop high-performance computing applications for scientific research. By leveraging CUDA, Computational Scientists can simulate and analyze complex systems more efficiently, enabling them to make groundbreaking discoveries and advance scientific knowledge.

See salaries and explore the career path for Computational Scientist

Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models to solve real-world problems. They use various techniques, including data analysis, statistical modeling, and optimization, to create models that can learn from data and make predictions. This course in CUDA programming can help Machine Learning Engineers develop the skills needed to create high-performance computing applications for machine learning. By leveraging CUDA, Machine Learning Engineers can train and deploy models more efficiently, enabling them to solve complex problems and drive innovation.

See salaries and explore the career path for Machine Learning Engineer

Deep Learning Engineer

Deep Learning Engineers specialize in developing and deploying deep learning models, a type of machine learning that uses artificial neural networks. They design, implement, and optimize deep learning algorithms to solve complex problems in various domains. This course in CUDA programming can be beneficial for Deep Learning Engineers who want to develop high-performance deep learning applications. By leveraging CUDA, Deep Learning Engineers can train and deploy models more efficiently, enabling them to push the boundaries of deep learning and drive innovation.

See salaries and explore the career path for Deep Learning Engineer

Software Engineer

Software Engineers create the applications, programs, and software that run on computers, mobile phones, and other digital devices. They design, develop, test, and maintain these products, ensuring they meet user needs and function properly. This course helps build a foundation in CUDA programming, which is essential for developing high-performance computing applications. By understanding CUDA programming, Software Engineers can create more efficient and effective software solutions.

See salaries and explore the career path for Software Engineer

Computer Programmer

Computer Programmers write, test, and maintain the code that makes computers and software work. They translate designs and specifications into instructions that computers can execute. This course in CUDA programming can help Computer Programmers develop the skills needed to create high-performance computing applications, which are in high demand in various industries.

See salaries and explore the career path for Computer Programmer

Computer Hardware Engineer

Computer Hardware Engineers design, develop, and test computer hardware components, including processors, memory, and storage devices. They work to improve the performance, efficiency, and reliability of computer systems. This course in CUDA programming can be helpful for Computer Hardware Engineers who want to develop high-performance computing hardware. By understanding CUDA programming, Engineers can design and optimize hardware components that support high-performance computing applications.

See salaries and explore the career path for Computer Hardware Engineer

Data Scientist

Data Scientists use scientific methods, processes, algorithms, and systems to extract knowledge and insights from data. They develop and build models to analyze data and make predictions. This course in CUDA programming can be helpful for Data Scientists who want to develop high-performance computing applications for data analysis and modeling. By leveraging CUDA, Data Scientists can process large datasets more efficiently and uncover valuable insights.

See salaries and explore the career path for Data Scientist

Software Architect

Software Architects design, build, and maintain software systems. They work with stakeholders to understand business requirements and translate them into technical solutions. This course in CUDA programming can be helpful for Software Architects who want to develop high-performance computing software systems. By understanding CUDA programming, Architects can design and implement systems that can handle complex and data-intensive tasks efficiently.

See salaries and explore the career path for Software Architect

Technical Lead

Technical Leads lead and manage teams of software engineers and computer programmers. They provide technical guidance, set project goals, and ensure that projects are completed on time and within budget. This course in CUDA programming can be helpful for Technical Leads who want to develop high-performance computing solutions. By understanding CUDA programming, Technical Leads can provide better guidance to their teams and ensure that projects are implemented efficiently.

See salaries and explore the career path for Technical Lead

Data Analyst

Data Analysts collect, clean, and analyze data to extract meaningful insights. They use data analysis techniques and tools to identify trends, patterns, and relationships in data. This course in CUDA programming may be helpful for Data Analysts who want to develop high-performance computing applications for data analysis. By leveraging CUDA, Data Analysts can process large datasets more efficiently, enabling them to uncover valuable insights and drive data-driven decision-making.

See salaries and explore the career path for Data Analyst

Business Intelligence Analyst

Business Intelligence Analysts use data analysis and visualization techniques to provide insights and recommendations to businesses. They help businesses understand their data, make informed decisions, and improve their operations. This course in CUDA programming may be helpful for Business Intelligence Analysts who want to develop high-performance computing applications for business intelligence. By leveraging CUDA, Business Intelligence Analysts can process large datasets more efficiently, enabling them to provide more valuable insights and drive business growth.

See salaries and explore the career path for Business Intelligence Analyst

Financial Analyst

Financial Analysts provide insights and recommendations on financial matters, such as investments, stocks, and bonds. They use financial data and models to evaluate the performance of companies and make investment decisions. This course in CUDA programming may be helpful for Financial Analysts who want to develop high-performance computing applications for financial modeling and analysis. By leveraging CUDA, Financial Analysts can process large financial datasets more efficiently, enabling them to make more informed investment decisions.

See salaries and explore the career path for Financial Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical techniques to analyze financial data and make investment decisions. They develop and implement quantitative models to identify trading opportunities and manage risk. This course in CUDA programming may be helpful for Quantitative Analysts who want to develop high-performance computing applications for quantitative modeling and analysis. By leveraging CUDA, Quantitative Analysts can process large financial datasets more efficiently, enabling them to make more informed investment decisions.

See salaries and explore the career path for Quantitative Analyst

Reading list

We've selected seven 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 CUDA programming Masterclass with C++.

CUDA Programming

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Provides a comprehensive overview of CUDA programming and is written by Shane Cook, one of the leading experts on CUDA programming. It covers a wide range of topics, from basic concepts to advanced techniques. It valuable resource for anyone who wants to learn about CUDA programming.

CUDA by Example

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Provides a comprehensive overview of CUDA programming with C and C++. It covers a wide range of topics, from basic concepts to advanced techniques. It valuable resource for anyone who wants to learn about CUDA programming with C and C++.

CUDA by Example: An Introduction to General-Purpose...

Paperback

CUDA by Example: An Introduction to General-Purpose...

Unknown Binding

$$$

CUDA by Example: An Introduction to General-Purpose...

Kindle Edition

Cuda by Example

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Provides a hands-on introduction to CUDA programming. It covers a wide range of topics, from basic concepts to advanced techniques. It valuable resource for anyone who wants to learn about CUDA programming.

CUDA Programming

Save

Focuses on the low-level details of CUDA programming with C. It practical book that provides a hands-on introduction to CUDA programming. It includes code samples and exercises for readers to follow along.

CUDA Programming: A Developer's Guide to Parallel...

Paperback

CUDA Programming: A Developers Guide to Parallel...

Paperback

CUDA Programming: A Developer's Guide to Parallel...

Kindle Edition

High Performance Parallelism Pearls Volume Two

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Provides a practical introduction to CUDA programming. It covers a wide range of topics, from basic concepts to advanced techniques. It includes code samples and exercises for readers to follow along and provides a good balance of theory and practice, making it a good choice for those who want to learn CUDA programming.

High Performance Parallelism Pearls Volume Two:...

Paperback

$$$

High Performance Parallelism Pearls Volume Two:...

Kindle Edition

$$$

Monte Carlo Methods for Applied Scientists

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Provides a comprehensive overview of parallel and distributed computation. It covers a wide range of topics, from basic concepts to advanced techniques. It valuable resource for anyone who wants to learn about parallel and distributed computation or use it to power their own applications.

Monte Carlo Methods For Applied Scientists

Paperback

$$$$

Parallel Computing

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Provides a comprehensive overview of parallel programming. It covers a wide range of topics, from basic concepts to advanced techniques. It valuable resource for anyone who wants to learn about parallel programming.

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