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Orange Owl

Self-driving cars, machine learning and augmented reality are some of the examples of modern applications that involve parallel computing.

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Self-driving cars, machine learning and augmented reality are some of the examples of modern applications that involve parallel computing.

With the availability of high performance GPUs and a language, such as CUDA, which greatly simplifies programming, everyone can have at home and easily use a supercomputer.

The aim of this course is to provide the basics of the architecture of a graphics card and allow a first approach to CUDA programming by developing simple examples with a growing degree of difficulty.

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What's inside

Learning objectives

  • Understanding the basics of parallel programming on gpu.
  • Understanding the basics of gpu architecture.
  • Writing simple programs in cuda language.

Syllabus

Introduction and Basics
Introduction to GPU Computing with CUDA
CUDA and the Related Parallelization Paradigms
Threads Blocks Cores and Streaming Multiprocessors
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Heterogeneous Computing and NVIDIA Compiler Driver
CUDA Download Installation and IDEs
Cuda Programming
CUDA Program Workflow and Simple Example - Part 01
Simple Example - Part 02

This Lecture is accompanied by the simpleExample.cu code. Examples are provided as .cu files instead of Visual Studio or Eclipse projects to avoid compatibility issues. Use the instructions detailed in Lecture #5 to create your own projects.

Memories and Performance
CUDA Memories
CUDA Profiling and the Visual Profiler

This Lecture is accompanied by the simpleExampleNonAligned.cu, simpleExampleNonConsecutive.cu and simpleExampleDouble.cu codes. Examples are provided as .cu files instead of Visual Studio or Eclipse projects to avoid compatibility issues. Use the instructions detailed in Lecture #5 to create your own projects.

This Lecture is accompanied by the adjacentDifferences.cu code. Examples are provided as .cu files instead of Visual Studio or Eclipse projects to avoid compatibility issues. Use the instructions detailed in Lecture #5 to create your own projects.

This Lecture is accompanied by the movingAverage.cu code. Examples are provided as .cu files instead of Visual Studio or Eclipse projects to avoid compatibility issues. Use the instructions detailed in Lecture #5 to create your own projects.

This Lecture is accompanied by the simpleExample2D.cu code. Examples are provided as .cu files instead of Visual Studio or Eclipse projects to avoid compatibility issues. Use the instructions detailed in Lecture #5 to create your own projects.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Develops the basics of parallel programming on GPU, which is foundational for machine learning and data analysis
Explores CUDA programming, a simplified language for parallel programming, which is highly relevant in industry
Examines the architecture of a graphics card, which provides insights into modern computing systems
Taught by Orange Owl, which is not as well-known in the field of CUDA programming
Focuses on CUDA programming and does not cover broader aspects of GPU computing
Provides hands-on examples and simple exercises to aid comprehension

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Reviews summary

Introduction to cuda compute for gpu

learners say this course is largely positive. They focus on the engaging content and clear explanations. Students find the instructors to be knowledgeable, supportive, and accessible. However, a few students have mentioned outdated codes and quality issues with the videos.
knowledgeable, supportive, and accessible
"Love the way he shared his knowledge"
"knowledgeable and easy to understand"
"Mark always delivers! I love his courses!"
well explained, easy to follow, and engaging
"Very clearly explained 👌"
"It’s very engaging so far"
"Easy to listen to and engaging"
issues with outdated codes
"I would be nice to add a chapter about LangFlow, its an awesome tool."
quality issues with the videos
"The quality of the videos is outrageous. It stops in the middle of his sentence. I hit play & nothing happens. Very disappointung."

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 Introduction to GPU computing with CUDA with these activities:
Follow tutorials on CUDA architecture
Develop a deeper understanding of the internal workings of a graphics card.
Show steps
  • Locate tutorials on CUDA architecture
  • Watch videos and read articles to learn about the architecture
  • Complete hands-on exercises to reinforce learning
Solve CUDA coding challenges
Improve your ability to write efficient CUDA code.
Show steps
  • Find online coding challenges or create your own
  • Attempt to solve the challenges using CUDA
  • Review solutions and learn from mistakes
Read 'CUDA by Example'
Supplement your CUDA learning with a comprehensive textbook.
View Cuda by Example on Amazon
Show steps
  • Purchase or borrow the book
  • Read the book and complete the exercises
Show all three activities

Career center

Learners who complete Introduction to GPU computing with CUDA will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists use data to solve problems and make decisions. They work in many different industries, including finance, healthcare, and retail. This course may be helpful for Data Scientists who want to learn more about GPU computing and CUDA, which can be used to accelerate the processing of large datasets.
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. They work in many different industries, including finance, healthcare, and retail. This course may be helpful for Machine Learning Engineers who want to learn more about GPU computing and CUDA, which can be used to improve the performance of machine learning models.
Business Analyst
Business Analysts help businesses identify and solve problems. They work in many different industries, including finance, healthcare, and retail. This course may be helpful for Business Analysts who want to learn more about GPU computing and CUDA, which can be used to improve the performance of business applications.
Operations Research Analyst
Operations Research Analysts use mathematical and statistical techniques to help businesses make decisions. They work in many different industries, including manufacturing, transportation, and healthcare. This course may be helpful for Operations Research Analysts who want to learn more about GPU computing and CUDA, which can be used to improve the performance of operations research models.
Statistician
Statisticians collect, analyze, and interpret data. They work in many different industries, including finance, healthcare, and retail. This course may be helpful for Statisticians who want to learn more about GPU computing and CUDA, which can be used to accelerate the processing of large datasets.
Software Engineer
Software Engineers design, develop, test, and maintain computer software. They work in many different industries, including computer systems design, software publishing, and computer programming. This course may be helpful for Software Engineers who want to learn more about GPU computing and CUDA, which can be used to improve the performance of software applications.
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make informed decisions. They work in many different industries, including finance, healthcare, and retail. This course may be helpful for Data Analysts who want to learn more about GPU computing and CUDA, which can be used to accelerate the processing of large datasets.
Financial Analyst
Financial Analysts help businesses make financial decisions. They work in many different industries, including finance, healthcare, and retail. This course may be helpful for Financial Analysts who want to learn more about GPU computing and CUDA, which can be used to improve the performance of financial models.
Risk Analyst
Risk Analysts help businesses identify and manage risk. They work in many different industries, including finance, healthcare, and retail. This course may be helpful for Risk Analysts who want to learn more about GPU computing and CUDA, which can be used to improve the performance of risk management models.
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. They work in many different industries, including insurance, finance, and healthcare. This course may be helpful for Actuaries who want to learn more about GPU computing and CUDA, which can be used to improve the performance of actuarial models.
Computer Scientist
Computer Scientists conduct research to advance our understanding of computing technology. They work in many different areas, including artificial intelligence, computer graphics, and software engineering. This course may be helpful for Computer Scientists who want to learn more about GPU computing and CUDA, which can be used to solve complex computational problems.
Computer Programmer
Computer Programmers write, test, and maintain the code that makes computer software work. They work in many different industries, including computer systems design, software publishing, and computer programming. This course may be helpful for Computer Programmers who want to learn more about GPU computing and CUDA, which can be used to improve the performance of software applications.
Investment Analyst
Investment Analysts help businesses make investment decisions. They work in many different industries, including finance, healthcare, and retail. This course may be helpful for Investment Analysts who want to learn more about GPU computing and CUDA, which can be used to improve the performance of investment models.
Healthcare Analyst
Healthcare Analysts help healthcare providers make decisions about patient care. They work in many different areas, including hospitals, clinics, and insurance companies. This course may be helpful for Healthcare Analysts who want to learn more about GPU computing and CUDA, which can be used to improve the performance of healthcare applications.
Retail Analyst
Retail Analysts help retailers make decisions about product development, marketing, and sales. They work in many different areas, including department stores, grocery stores, and online retailers. This course may be helpful for Retail Analysts who want to learn more about GPU computing and CUDA, which can be used to improve the performance of retail applications.

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 Introduction to GPU computing with CUDA.
Comprehensive guide to CUDA programming and parallel computing. Covering a wide range of topics including GPU architecture, algorithm design and optimization, this text presents a thorough grounding in CUDA concepts.
Provides a comprehensive overview of parallel programming concepts and techniques, including CUDA programming. It offers a solid foundation for understanding the challenges and opportunities of parallel computing.
Covers parallel programming concepts and techniques in C++, including CUDA programming. It provides a solid foundation for understanding the principles of parallel computing.
A concise and beginner-friendly introduction to CUDA programming. It covers essential CUDA concepts and provides practical examples to help learners get started quickly.
A beginner-friendly introduction to CUDA programming. It uses practical examples and step-by-step instructions to help learners quickly grasp the basics of CUDA.
Covers general-purpose numerical methods including parallel computing and its application in solving scientific and engineering problems.
Is commonly used as a textbook in computer architecture courses, providing a detailed understanding of computer design and organization, including parallel processing and GPU architectures.
Serves as a good foundation for beginners to understand general programming concepts, data structures, algorithms, and object-oriented programming, although it does not delve into GPU programming.

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