In recent years, the use of Graphics Processing Units (GPUs) has gained significant popularity in parallel computing due to their ability to perform massively parallel computations. One of the most commonly used GPU programming languages is CUDA, which enables developers to harness the full potential of NVIDIA GPUs for high-performance computing. As online learning continues to gain traction, there is a growing demand for quality courses that provide comprehensive training in CUDA programming. In this article, we will review some of the best online courses available that cover CUDA programming from basic concepts to advanced techniques.
Here’s a look at the Best Cuda Courses and Certifications Online and what they have to offer for you!
Cuda Programming Online Course
- Cuda Programming Online Course
- 1. CUDA programming Masterclass with C++ by Kasun Liyanage (Udemy) (Our Best Pick)
- 2. Learn CUDA with Docker! by Scientific Programmer™ Team, Scientific Programming School (Udemy)
- 3. Beginning CUDA Programming: Zero to Hero, First Course! by Scientific Programmer™ Team, Scientific Programming School (Udemy)
- 4. 【Pythonで学ぶ 】CUDA プログラミング入門 by Tetsuya T (Udemy)
- 5. Introduction to GPU computing with CUDA by Orange Owl (Udemy)
- 6. Learning CUDA 10 Programming by Packt Publishing (Udemy)
- 7. Arquitetura e Programação de GPUs by Esteban Clua (Udemy)
- 8. CUDA GPU Programming Beginner To Advanced by The Startup Central Co., Muhammad Adil (Udemy)
- 9. Cuda Basics by HPC Specialist (Udemy)
- 10. GPU Programlama by Muhammed Fatih Bayraktar (Udemy)
1. CUDA programming Masterclass with C++ by Kasun Liyanage (Udemy) (Our Best Pick)
The CUDA Programming Masterclass with C++ is a course designed to teach individuals parallel programming on GPUs using CUDA. The course is split into several sections, starting with an introduction to basic concepts such as the CUDA programming model, execution model, and memory model. Participants will then learn how to implement advanced algorithms using CUDA, with a focus on performance and optimization techniques. Various profiling techniques and tools will also be covered, including nvprof, nvvp, CUDA Memcheck, and CUDA-GDB tools in the CUDA toolkit.
The course includes programming exercises and quizzes to help participants better understand the concepts discussed. Additionally, it is the first course of the CUDA master class series, making the knowledge gained here essential for following courses.
The course is divided into several sections, including an introduction to CUDA programming and the CUDA programming model, CUDA execution model, CUDA memory model, CUDA shared memory and constant memory, CUDA streams, performance tuning with CUDA instruction level primitives, parallel patterns and applications, and a bonus section on image processing with CUDA.
The course Learn CUDA with Docker! is being offered by the Scientific Programming School and the Scientific Programmer™ Team. It aims to teach individuals how to code with CUDA using GPGPU-Simulators and Docker. The course will provide an understanding of the NVIDIA CUDA parallel architecture and programming model. It will also provide access to powerful libraries for machine learning, image processing, linear algebra, and parallel algorithms. The course is designed to be easily understood and will be updated continually with new lessons and exercises.
The course will cover topics such as virtualization basics, Docker essentials, GPU basics, CUDA installation, CUDA toolkit, CUDA threads and blocks in various combinations, and CUDA coding examples. Additionally, the course will provide a Zoom live class lecture series that will explain different aspects of Parallel and Distributed Computing and the High-Performance Computing (HPC) systems software stack. Live classes will be delivered through the Scientific Programming School’s interactive e-learning platform, which allows students to access scientific code playgrounds.
Students purchasing the course will receive free access to the interactive version of the course from the Scientific Programming School (SCIENTIFIC PROGRAMMING IO). The course will provide instructions on how to join the platform in the bonus content section. The course includes sections on Introduction, CUDA foundation, CUDA threads, blocks and grid, CUDA memory models, CUDA vector addition, CUDA matrix multiplication, CUDA streams, NVIDIA Docker Container Toolkit, CUDA for Dummies, and additional contents.
It is important to note that some of the images used in the course are copyrighted to NVIDIA.
3. Beginning CUDA Programming: Zero to Hero, First Course! by Scientific Programmer™ Team, Scientific Programming School (Udemy)
The course titled Beginning CUDA Programming: Zero to Hero, First Course! is being offered by the Scientific Programmer™ Team at the Scientific Programming School. This course aims to teach CUDA programming using GPGPU to help students kickstart their Big Data and Data Science careers. It is the first CUDA programming course offered on the Udemy platform and the first course of the Scientific Computing Essentials master class. The course is designed to introduce NVIDIA’s CUDA parallel architecture and programming model in an easy-to-understand manner.
The course begins by explaining what CUDA is – a parallel computing platform and application programming interface (API) model created by NVIDIA. It then progresses to cover the basics of CUDA programming by developing simple examples with a growing degree of difficulty. The course covers topics such as GPU Basics, CUDA Installation, CUDA Toolkit, CUDA Threads and Blocks in various combinations, and CUDA Coding Examples. The course includes the first-ever online CUDA programming playgrounds, which students can access for free upon purchasing the course.
In addition to the online playgrounds, the course also offers bonus content that provides access to live class lecture series on the Parallel and distributed computing and the High Performance Computing (HPC) systems software stack: Slurm, PBS Pro, OpenMP, MPI, and CUDA. The live classes will be delivered through the Scientific Programming School, an interactive and advanced e-learning platform for learning scientific coding.
It is important to note that some of the images used in this course are copyrighted to NVIDIA. Overall, the course aims to provide a comprehensive introduction to CUDA programming and GPGPU, making it an excellent starting point for students looking to pursue careers in Big Data and Data Science.
The course Introduction to CUDA Programming with Python introduces the PyCUDA programming language for GPU parallel computing. CUDA is a key technology for supporting GPU and HPC, providing 10 to 100 times faster processing than traditional CPU sequential processing. PyCUDA simplifies memory management compared to CUDA C, and allows for file input/output and visualization using Python’s libraries. The course covers GPU hardware and software knowledge, as well as basic CUDA terminology such as threads, blocks, grids, and warps. The course is conducted using Google Colab, a free interactive Python environment that supports GPU computing.
The course requires a minimum of Python programming skills, and knowledge of numerical calculations is recommended but not required. The course includes five sections: Introduction, Basic Knowledge of GPU, Basic Knowledge of CUDA, PyCUDA Programming (1) Basic Programming, PyCUDA Programming (2) Use of Various Memory Libraries, and a bonus section on Desktop PC Environment Configuration.
Introduction to GPU computing with CUDA is a course offered by Orange Owl that aims to provide participants with the basics of parallel computing using CUDA technology. The course is designed to introduce the architecture of a graphics card and simplify programming using CUDA. The course is structured into three sections: Introduction and Basics, Cuda Programming, and Memories and Performance.
The growing demand for high-performance computing in modern applications, such as self-driving cars, machine learning, and augmented reality, has paved the way for parallel computing. The course highlights the availability of high-performance GPUs and the simplicity of programming offered by CUDA. Participants will be able to use a supercomputer from the comfort of their homes.
The course aims to provide a gradual learning experience, starting with simple examples and progressing towards more complex tasks. Participants will learn about coalescence, halo region, shared memory, and other key concepts of parallel computing. The course is suitable for anyone interested in parallel computing, regardless of their level of experience.
The course is designed to offer a clear and concise explanation of the basics of parallel computing with CUDA. By the end of the course, participants should have a solid understanding of the architecture of a graphics card and will be able to use CUDA for parallel computing. The course is an excellent opportunity to gain valuable knowledge and skills in the field of parallel computing.
The Learning CUDA 10 Programming course, offered by Packt Publishing, aims to equip learners with the ability to accelerate their applications using GPUs. The course uses CUDA 10, a framework that is widely used to develop high-performance, GPU-accelerated applications.
The course adopts a hands-on approach, providing learners with examples to facilitate their understanding of CUDA programming. In addition, CUDA offers a general-purpose programming model that grants access to the immense computational power of modern GPUs. The framework also provides powerful libraries for machine learning, image processing, linear algebra, and parallel algorithms.
By the end of the course, learners will have grasped the fundamentals of CUDA programming and will be able to use it in their applications immediately.
Nathan Weston, the author of the course, is a software developer who has been involved in the visual effects industry, where he has extensively used CUDA. He has also worked in software engineering research and scientific applications and currently works as a consultant with local and remote clients.
The course comprises seven sections, namely Introduction to CUDA, Programming with CUDA, Performance Optimizations, Parallel Algorithms, GPU Accelerated Libraries, Advanced CUDA Topics, and Summary and Next Steps.
The Arquitetura e Programação de GPUs course, instructed by Esteban Clua, teaches students about GPUs and how to program them. The course covers basic concepts of parallel computing and high-performance programming, allowing students to develop practical applications by the end of the course. The course also delves into the CUDA language used for programming NVIDIA GPUs. Upon completion, students will be able to develop various practical applications and utilize GPUs for different purposes in fields such as e-science, big data, machine learning, and engineering.
The course is currently in production, with new lessons being added each week. The content is divided into sections, beginning with an introduction and covering topics such as the history and function of GPUs, parallel computing concepts, CUDA introduction, optimizing blocks and shared memory, memory coalescence and atomic operators, and algorithms for reduce and scan.
Other sections include exploring concurrency tasks within the GPU using streams, memory constants and texture memory, sparse matrices, and compact and sort algorithms. The course is ideal for developers seeking to improve performance through GPU utilization.
The Startup Central Co. offers a CUDA GPU Programming Beginner to Advanced course instructed by Muhammad Adil. The course aims to teach students the fundamental concepts of Parallel Computing and GPU programming with CUDA. The course is designed to help beginning programmers gain theoretical knowledge as well as practical skills in GPU programming with CUDA to further their career. The course includes practical exercises for students to test their knowledge and skills.
The course covers topics such as the background of GPU programming, NVIDIA GPUs for General Purpose and their Application Areas, CUDA Memory Models, CUDA Functional Pipeline, Programming Pipeline & CUDA Toolkit, Parallelism Models (mpi, open MP, CUDA), CUDA Performance Benchmarking, and much more. Students will lay the foundation for future CUDA GPU Programming jobs or promotions with their new GPU programming skills.
CUDA (Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model created by Nvidia. It allows software developers and software engineers to use a CUDA-enabled graphics processing unit (GPU) for general-purpose processing. GPU programming enables GPUs to be used in scientific computing. With the discovery of the ability of GPUs in number crunching, it has become mainstream to use them for scientific application development.
The top three benefits of learning GPU programming with CUDA are high demand, a usable skill, and the potential to further your career. Skilled GPU programmers with CUDA are in high demand, and companies all around the world are actively seeking competent GPU programmers.
Students do not need their own GPU for this course. They can use cloud-based solutions, or if they do not want to purchase cloud-based GPU environment, they can still take this course to get theoretical knowledge of CUDA and programming experience of other open-source libraries like mpi/openMP. The course also exposes students to cutting-edge research fields in which GPU programming is in use these days.
The course offers a 30-day money-back guarantee.
The Cuda Basics course is designed for programmers with a basic understanding of C or C++ who seek to learn the fundamentals of Cuda C programming. The course provides a combination of lectures and example programs to help participants design their own algorithms and take advantage of the full performance benefits of GPGPU programming.
The course includes a range of topics, divided into different sections. The first section offers an introduction to Cuda C and covers the process of installing Cuda. The second section focuses on Cuda hardware design, while the third section provides an overview of the Cuda execution model.
The course offers a range of example programs that help participants understand key concepts, including adding vectors, occupancy, shared memory, and memory coalescence. Additionally, participants will learn about constant memory, atomic functions, warp level primitives, and dynamic parallelism.
The final sections of the course provide an introduction to pinned, zero copy, and unified memory, as well as streams and multi-GPU programs. By the end of the course, participants will have the skills and knowledge necessary to design their own algorithms and take full advantage of the performance benefits of GPGPU programming.
The course titled GPU Programlama is instructed by Muhammed Fatih Bayraktar. The course focuses on the CUDA Runtime API and its application in artificial intelligence and graphics programming. The course emphasizes the ever-increasing size of data and the need for efficient processing through the use of graphics processing units (GPUs). Students will learn to harness the power of GPUs to develop new algorithms and achieve exceptional results.
A GPU is a specialized electronic circuit designed to quickly process and manipulate memory in a frame buffer to produce output for a display device. They are utilized in embedded systems, cell phones, personal computers, workstations, and gaming consoles. With their highly parallel structure, modern GPUs are efficient in processing large blocks of data in parallel, making them more efficient than general-purpose central processing units (CPUs). GPUs are commonly found in personal computers as an individual graphics card or embedded on the mainboard.
The term GPU originally referred to a programmable processing unit designed to operate independently of the CPU and manage graphic processing and output. The term gained popularity in 1994 when Sony referred to its Toshiba-designed Sony GPU for the PlayStation console. Nvidia later marketed the GeForce 256 as the world’s first GPU in 1999. The course covers various topics related to GPU programming, including basics, memory, first GPU code, occupancy, debugging, performance measurement, project files, memory transfer, simultaneous operations, cache levels, shared memory, and closure.
The course consists of several sections, beginning with the basics and fundamentals of GPU programming, followed by memory management, code development, performance measurement, and debugging. Students will learn to develop efficient and high-performance algorithms that take advantage of the GPU’s parallel structure. The course also covers advanced topics such as cache levels, shared memory, and closure. Through the course, students will develop the necessary skills to utilize GPUs effectively and efficiently.