Introduction to GPU computing with CUDA from Udemy

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.

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

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.

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.

Traffic lights

Read about what's good

what should give you pause

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

Reviews summary

Foundational cuda programming with practical examples

Learners seeking a foundational understanding of GPU computing with CUDA would likely find this course beneficial. It aims to introduce the basics of GPU architecture and provide a first approach to CUDA programming through simple, hands-on examples. The course's structure, which includes detailed instructions for setting up environments and using provided `.cu` files for practical exercises, suggests a focus on enabling students to write their first CUDA programs. It also covers important aspects like CUDA memories and profiling tools, indicating a practical approach to performance. While designed for beginners, the technical nature of GPU computing means some prior programming exposure might be implicitly helpful for maximizing the learning experience.

Course focuses on basics; prior programming knowledge is implicitly helpful.

"As an introduction, I found it gives a good start, but I know I'll need more advanced courses."

"While it's 'Introduction', I feel a solid background in C/C++ is really helpful to follow along."

"Don't expect deep dives into complex algorithms; it's truly for getting your feet wet."

Instructions are provided for setting up the necessary development environment.

"The guidance in Lecture #5 on creating projects from `.cu` files was crucial for getting started."

"I appreciated the note about compatibility and providing `.cu` files, making setup smoother."

"It was helpful to have explicit instructions on how to download, install, and use CUDA with IDEs."

Explores important topics like memory management and performance profiling.

"Learning about CUDA Memories was essential for understanding how to optimize my code."

"The section on the Visual Profiler will be very useful for identifying bottlenecks in my GPU programs."

"I found the focus on performance aspects to be a valuable inclusion for practical development."

Offers practical examples for immediate application of CUDA programming.

"The `.cu` files provided with lectures were really useful for direct practice."

"I liked working through the simple examples; they demonstrated concepts clearly."

"The exercises with growing difficulty helped build my coding skills progressively."

Provides essential initial understanding of GPU computing concepts.

"I appreciate how the course covers the basics of GPU architecture before diving into code."

"It really helped me grasp the core concepts of parallel programming and CUDA."

"I feel confident about the fundamentals after completing the introductory modules."

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

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Develop a deeper understanding of the internal workings of a graphics card.

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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

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Improve your ability to write efficient CUDA code.

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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'

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Supplement your CUDA learning with a comprehensive textbook.

View Cuda by Example on Amazon

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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:

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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Computer Scientist

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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