CUDA programming Masterclass with C++ from Udemy

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.

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

Let's investigate some background.

How to install CUDA toolkit and first look at CUDA program

Basic elements of CUDA program

Organization of threads in a CUDA program - threadIdx

Organization of thread in a CUDA program - blockIdx,blockDim,gridDim

Programming exercise 1

Unique index calculation using threadIdx blockId and blockDim

Unique index calculation for 2D grid 1

Unique index calculation for 2D grid 2

Memory transfer between host and device

Programming exercise 2

Sum array example with validity check

Sum array example with error handling

Sum array example with timing

Extend sum array implementation to sum up 3 arrays

Device properties

Summary

CUDA Execution model

Understand the device better

All about warps

Warp divergence

Resource partitioning and latency hiding 1

Resource partitioning and latency hiding 2

Occupancy

Profile driven optimization with nvprof

Parallel reduction as synchronization example

Parallel reduction as warp divergence example

Parallel reduction with loop unrolling

Parallel reduction as warp unrolling

Reduction with complete unrolling

Performance comparison of reduction kernels

CUDA Dynamic parallelism

Reduction with dynamic parallelism

CUDA memory model

Different memory types in CUDA

Memory management and pinned memory

Zero copy memory

Unified memory

Global memory access patterns

Global memory writes

AOS vs SOA

Matrix transpose

Matrix transpose with unrolling

Matrix transpose with diagonal coordinate system

CUDA Shared memory and constant memory

Introduction to CUDA shared memory

Shared memory access modes and memory banks

Row major and Column major access to shared memory

Static and Dynamic shared memory

Shared memory padding

Parallel reduction with shared memory

Synchronization in CUDA

Matrix transpose with shared memory

CUDA constant memory

Matrix transpose with Shared memory padding

CUDA warp shuffle instructions

Parallel reduction with warp shuffle instructions

CUDA Streams

Introduction to CUDA streams and events

How to use CUDA asynchronous functions

How to use CUDA streams

Overlapping memory transfer and kernel execution

Stream synchronization and blocking behavious of NULL stream

Explicit and implicit synchronization

CUDA events and timing with CUDA events

Creating inter stream dependencies with events

Performance Tuning with CUDA instruction level primitives

Introduction to different types of instructions in CUDA

Floating point operations

Standard and Instrict functions

Atomic functions

Parallel Patterns and Applications

Scan algorithm introduction

Simple parallel scan

Work efficient parallel exclusive scan

Work efficient parallel inclusive scan

Parallel scan for large data sets

Parallel Compact algorithm

Bonus: Introduction to Image processing with CUDA

Introduction part 1

Introduction part 2

Digital image processing

Digital image fundametals : Human perception

Digital image fundamentals : Image formation

OpenCV installation

Good to know

Know what's good

, what to watch for

, 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

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

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

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

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

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

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

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

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

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

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

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

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

CUDA programming Masterclass with C++

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What's inside

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Syllabus

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