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Chancellor Thomas Pascale
This course will complete the GPU specialization, focusing on the leading libraries distributed as part of the CUDA Toolkit. Students will learn how to use CuFFT, and linear algebra libraries to perform complex mathematical computations. The Thrust library’s capabilities in representing common data structures and associated algorithms will be introduced. Using cuDNN and cuTensor they will be able to develop machine learning applications that help with object detection, human language translation and image classification.
Register for this course and see more details by visiting:
OpenCourser.com/course/oonlqo/cuda
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What's inside
Syllabus
Course Overview
The purpose of this module is for students to understand how the course will be run, topics, how they will be assessed, and expectations.
Read more
Syllabus
Course Overview
The purpose of this module is for students to understand how the course will be run, topics, how they will be assessed, and expectations.
Read more
cuFFT
cuFFT provides the ability to perform fast Fourier transforms (FFTs) on large datasets. Students will learn of common use cases such as fast multiplication of large polynomials, signal processing, and matrix operations. They will use this library to develop software that process audio or video signals.
CUDA Linear Algebra
The CUDA Toolkit includes a number of linear algebra libraries, such as cuBLAS, NVBLAS, cuSPARSE, and cuSOLVER. Students will learn the different capabilities and limitations of many of them and apply that knowledge to compute matrix dot products, determinant, and finding solutions to complex linear systems.
The CUDA Thrust Library
Most developers utilize data structures beyond the primitives and pointers that make up the core of CUDA programmers, which makes pure CUDA development difficult. Students will learn about the Thrust library that adds the vector data structure and associated algorithms that allow for simplification of their code. Students will create software that transform, reduction, and sort large datasets.
CUDA Machine Learning
Data Scientists, Machine Learning, and Artificial intelligence experts are using neural networks to solve problems such as human language translation, image classification, and object detection/avoidance.
Using the cuDNN and cuTensor, students will be able to develop a variety of neural networks and similar structures. At the completion of this module students will be asked to develop a course-wide project that brings together their knowledge from all previous courses and lessons to develop a capstone software project.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops core machine learning, data science, and artificial intelligence skills, which are highly relevant in academic and industry settings
Uses real-world examples and case studies to help learners understand how these technologies are being used in the field
Includes interactive exercises and hands-on labs to help learners apply their knowledge and skills
Provides opportunities for learners to network with industry professionals and experts
Covers the latest trends and advancements in machine learning, data science, and artificial intelligence
Requires learners to have a strong foundation in computer science and programming
Save this course
Save CUDA Advanced Libraries to your list so you can find it easily later:
Save
Save
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 Advanced Libraries with these
activities:
CUDA Resources Compilation
Show steps
Create a compilation of useful CUDA resources, such as tutorials, documentation, and code samples.
Browse courses on
CUDA
Show steps
-
Search for CUDA resources online.
-
Organize the resources into a document or online repository.
-
Share your compilation with other learners.
Review Linear Algebra Concepts
Show steps
Review linear algebra concepts to strengthen your understanding of CUDA Linear Algebra.
Browse courses on
Linear Algebra
Show steps
-
Review your lecture notes or textbooks on linear algebra.
-
Solve practice problems and exercises.
-
Identify and focus on areas where you need additional practice.
CUDA by Example: An Introduction to General-Purpose GPU Programming
Show steps
Read CUDA by Example to gain a foundational understanding of CUDA programming and its applications.
View
Cuda by Example
on Amazon
Show steps
-
Purchase or borrow the book.
-
Read the book thoroughly.
-
Complete the exercises in the book.
-
Apply what you've learned to your own projects.
Six other activities
Expand to see all activities and additional details
Show all nine activities
CUDA Workshop
Show steps
Attend a CUDA workshop to learn the basics of CUDA programming and how to use it to accelerate computationally heavy tasks.
Browse courses on
CUDA
Show steps
-
Register for a CUDA workshop.
-
Prepare for the workshop by reviewing the prerequisites.
-
Attend the workshop and participate actively.
-
Complete the hands-on exercises during the workshop.
-
Follow up with the workshop instructors if you have any questions.
Develop a simple image processing application using cuFFT
Show steps
Apply the knowledge of cuFFT learned in the course by developing a practical image processing application.
Show steps
-
Design the application and its functionality
-
Obtain images and convert them into a suitable format for the application
-
Implement image processing algorithms using cuFFT
-
Build the user interface and integrate all components
-
Test and refine the application
Coding Challenges on LeetCode
Show steps
Practice coding problems on LeetCode to solidify your understanding of the CUDA Toolkit libraries and machine learning algorithms.
Browse courses on
CUDA
Show steps
-
Identify the problem statement and constraints.
-
Design a solution using CUDA libraries and techniques.
-
Implement the solution in code.
-
Test and debug your code.
NVIDIA Developer Tutorials
Show steps
Complete the NVIDIA Developer Tutorials to learn how to use the CUDA Toolkit libraries and machine learning algorithms.
Browse courses on
CUDA
Show steps
-
Identify the relevant tutorials for your learning goals.
-
Study the tutorials and take notes.
-
Complete the hands-on exercises in the tutorials.
-
Apply what you've learned to your own projects.
CUDA Study Group
Show steps
Join a CUDA study group to collaborate with other learners and reinforce your understanding of the material.
Browse courses on
CUDA
Show steps
-
Find or start a CUDA study group.
-
Meet regularly with your study group.
-
Discuss the course material and work on projects together.
Neural Network Project
Show steps
Develop a neural network using cuDNN and cuTensor to solve a real-world problem, such as object detection or natural language translation.
Show steps
-
Define the problem and gather data.
-
Design the neural network architecture.
-
Train the neural network.
-
Evaluate the performance of the neural network.
-
Deploy the neural network for real-world use.
CUDA Resources Compilation
Show steps
Create a compilation of useful CUDA resources, such as tutorials, documentation, and code samples.
Browse courses on
CUDA
Show steps
Show steps
Show steps
- Search for CUDA resources online.
- Organize the resources into a document or online repository.
- Share your compilation with other learners.
Review Linear Algebra Concepts
Show steps
Review linear algebra concepts to strengthen your understanding of CUDA Linear Algebra.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review your lecture notes or textbooks on linear algebra.
- Solve practice problems and exercises.
- Identify and focus on areas where you need additional practice.
CUDA by Example: An Introduction to General-Purpose GPU Programming
Show steps
Read CUDA by Example to gain a foundational understanding of CUDA programming and its applications.
View
Cuda by Example
on Amazon
Show steps
Show steps
Show steps
- Purchase or borrow the book.
- Read the book thoroughly.
- Complete the exercises in the book.
- Apply what you've learned to your own projects.
Six other activities
Expand to see all activities and additional details
Show all nine activities
CUDA Workshop
Show steps
Attend a CUDA workshop to learn the basics of CUDA programming and how to use it to accelerate computationally heavy tasks.
Browse courses on
CUDA
Show steps
Show steps
Show steps
- Register for a CUDA workshop.
- Prepare for the workshop by reviewing the prerequisites.
- Attend the workshop and participate actively.
- Complete the hands-on exercises during the workshop.
- Follow up with the workshop instructors if you have any questions.
Develop a simple image processing application using cuFFT
Show steps
Apply the knowledge of cuFFT learned in the course by developing a practical image processing application.
Show steps
Show steps
Show steps
- Design the application and its functionality
- Obtain images and convert them into a suitable format for the application
- Implement image processing algorithms using cuFFT
- Build the user interface and integrate all components
- Test and refine the application
Coding Challenges on LeetCode
Show steps
Practice coding problems on LeetCode to solidify your understanding of the CUDA Toolkit libraries and machine learning algorithms.
Browse courses on
CUDA
Show steps
Show steps
Show steps
- Identify the problem statement and constraints.
- Design a solution using CUDA libraries and techniques.
- Implement the solution in code.
- Test and debug your code.
NVIDIA Developer Tutorials
Show steps
Complete the NVIDIA Developer Tutorials to learn how to use the CUDA Toolkit libraries and machine learning algorithms.
Browse courses on
CUDA
Show steps
Show steps
Show steps
- Identify the relevant tutorials for your learning goals.
- Study the tutorials and take notes.
- Complete the hands-on exercises in the tutorials.
- Apply what you've learned to your own projects.
CUDA Study Group
Show steps
Join a CUDA study group to collaborate with other learners and reinforce your understanding of the material.
Browse courses on
CUDA
Show steps
Show steps
Show steps
- Find or start a CUDA study group.
- Meet regularly with your study group.
- Discuss the course material and work on projects together.
Neural Network Project
Show steps
Develop a neural network using cuDNN and cuTensor to solve a real-world problem, such as object detection or natural language translation.
Show steps
Show steps
Show steps
- Define the problem and gather data.
- Design the neural network architecture.
- Train the neural network.
- Evaluate the performance of the neural network.
- Deploy the neural network for real-world use.
Career center
Learners who complete CUDA Advanced Libraries will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their understanding of data and related techniques to solve a wide range of problems. They can work in many industries, including healthcare, finance, and manufacturing. The CUDA Advanced Libraries course would be a valuable addition to the training of a Data Scientist because it teaches how to use FFTs, linear algebra libraries, and data structures to perform complex mathematical computations.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. They work on a variety of tasks, including data preparation, model training, and model deployment. The CUDA Advanced Libraries course would be beneficial to Machine Learning Engineers because it teaches how to use cuDNN and cuTensor to develop neural networks and similar structures.
Developer
Developer
Developers use CUDA to develop software applications that run on GPUs. They work in a variety of industries, including gaming, finance, and healthcare. The CUDA Advanced Libraries course would be valuable to Developers because it teaches how to use Thrust library adds the vector data structure and associated algorithms that allow for simplification of their code.
Software Architect
Software Architect
Software Architects design and develop software applications. They work on a variety of tasks, including requirements gathering, system design, and code development. The CUDA Advanced Libraries course would be useful to Software Architects because it teaches how to use advanced CUDA libraries to develop high-performance software applications.
Technical Lead
Technical Lead
Technical Leads lead teams of developers. They are responsible for the technical direction of a project and for ensuring that the project is completed on time and within budget. The CUDA Advanced Libraries course would be useful to Technical Leads because it teaches how to use advanced CUDA libraries to develop high-performance software applications.
Database Administrator
Database Administrator
Database Administrators design, develop, and maintain database systems. They work on a variety of tasks, including data management, performance tuning, and security. The CUDA Advanced Libraries course would be useful to Database Administrators because it teaches how to use linear algebra libraries to perform complex mathematical computations.
Network Engineer
Network Engineer
Network Engineers design, develop, and maintain computer networks. They work on a variety of tasks, including network design, network implementation, and network troubleshooting. The CUDA Advanced Libraries course would be useful to Network Engineers because it teaches how to use CUDA libraries to develop high-performance software applications.
Systems Engineer
Systems Engineer
Systems Engineers design, develop, and maintain computer systems. They work on a variety of tasks, including hardware selection, software installation, and system troubleshooting. The CUDA Advanced Libraries course would be useful to Systems Engineers because it teaches how to use CUDA libraries to develop high-performance software applications.
Computer Scientist
Computer Scientist
Computer Scientists research and develop new computer technologies. They work on a variety of tasks, including algorithm design, software development, and hardware design. The CUDA Advanced Libraries course would be useful to Computer Scientists because it teaches how to use advanced CUDA libraries to develop high-performance software applications.
Electrical Engineer
Electrical Engineer
Electrical Engineers design, develop, and maintain electrical systems. They work on a variety of tasks, including power generation, transmission, and distribution. The CUDA Advanced Libraries course may be useful to Electrical Engineers because it teaches how to use CUDA libraries to develop high-performance software applications.
Chemist
Chemist
Chemists study the composition, structure, and properties of matter. They work on a variety of tasks, including synthesizing new compounds, studying the reactions of chemicals, and developing new materials. The CUDA Advanced Libraries course may be useful to Chemists because it teaches how to use CUDA libraries to perform complex mathematical computations.
Mathematician
Mathematician
Mathematicians develop new mathematical theories and solve mathematical problems. They work on a variety of tasks, including pure mathematics, applied mathematics, and statistics. The CUDA Advanced Libraries course may be useful to Mathematicians because it teaches how to use CUDA libraries to perform complex mathematical computations.
Astronomer
Astronomer
Astronomers study the universe. They work on a variety of tasks, including observing the stars, planets, and galaxies. The CUDA Advanced Libraries course may be useful to Astronomers because it teaches how to use CUDA libraries to perform complex mathematical computations.
Biologist
Biologist
Biologists study living organisms. They work on a variety of tasks, including studying the structure, function, and evolution of organisms. The CUDA Advanced Libraries course may be useful to Biologists because it teaches how to use CUDA libraries to perform complex mathematical computations.
Physicist
Physicist
Physicists study the laws of nature. They work on a variety of tasks, including theoretical physics, experimental physics, and applied physics. The CUDA Advanced Libraries course may be useful to Physicists because it teaches how to use CUDA libraries to perform complex mathematical computations.
For more career information including salaries, visit:
OpenCourser.com/course/oonlqo/cuda
Reading list
We've selected eight 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 Advanced Libraries.
Provides a deep dive into parallel programming, including topics such as GPU programming and CUDA.
Provides a comprehensive overview of deep learning using Python and includes chapters on using CUDA for deep learning.
Provides a comprehensive collection of numerical recipes and algorithms and includes chapters on using CUDA for numerical computing.
Save
Introduces the fundamentals of CUDA programming and is particularly useful for providing background knowledge on the CUDA programming model.
Classic introduction to the C programming language and is useful for those who want to learn the basics of C programming before learning CUDA.
Provides a comprehensive overview of programming massively parallel processors and useful resource for understanding the concepts and techniques covered in the course.
Provides a comprehensive overview of parallel and distributed computing and useful resource for understanding the concepts and techniques covered in the course.
Save
Provides a practical introduction to CUDA programming and useful resource for understanding the concepts and techniques covered in the course.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/oonlqo/cuda
Share
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Effort
4, 4-8 hours/week
Level
Intermediate
Via
Coursera
Institution
Johns Hopkins University
Instructor
Chancellor Thomas Pascale
Language
English
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This course belongs to a
collection
called
GPU Programming. It's comprised of four courses:
- CUDA at Scale for the Enterprise
- CUDA Advanced Libraries (viewing)
- Introduction to Concurrent Programming with GPUs
- Introduction to Parallel Programming with CUDA
Good to know
Develops core machine learning, data science, and artificial intelligence skills, which are highly relevant in academic and industry settings
Uses real-world examples and case studies to help learners understand how these technologies are being used in the field
Includes interactive exercises and hands-on labs to help learners apply their knowledge and skills
Provides opportunities for learners to network with industry professionals and experts
Covers the latest trends and advancements in machine learning, data science, and artificial intelligence
Requires learners to have a strong foundation in computer science and programming
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