This specialization is intended for data scientists and software developers to create software that uses commonly available hardware. Students will be introduced to CUDA and libraries that allow for performing numerous computations in parallel and rapidly. Applications for these skills are machine learning, image/audio signal processing, and data processing.
GPU Programming Specialization
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GPU Programming Specialization
Solve Challenges with Powerful GPUs. Develop mastery in high performance computing and apply to numerous fields.
Instructor: Chancellor Thomas Pascale
11,932 already enrolled
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Get in-depth knowledge of a subject
Intermediate level
Recommended experience
2 months at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
Get in-depth knowledge of a subject
Intermediate level
Recommended experience
2 months at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
What you'll learn
Develop CUDA software for running massive computations on commonly available hardware
Utilize libraries that bring well-known algorithms to software without need to redevelop existing capabilities
Overview
Skills you'll gain
- Artificial Neural Networks
- Computer Graphics
- Event-Driven Programming
- Performance Tuning
- Deep Learning
- Numerical Analysis
- Machine Learning Methods
- Machine Learning
- Distributed Computing
- Software Development
- Computer Vision
- Data Structures
- Image Analysis
- Linear Algebra
- System Programming
- Programming Principles
- Data Processing
- Scalability
- Performance Testing
- C and C++
What’s included
Shareable certificate
Add to your LinkedIn profile
Taught in English
9 practice exercises
Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from Johns Hopkins University
Specialization - 4 course series
What you'll learn
Students will learn how to develop concurrent software in Python and C/C++ programming languages.
Students will gain an introductory level of understanding of GPU hardware and software architectures.
Skills you'll gain
Category: Computer Programming
Category: Debugging
Category: Python Programming
Category: Computer Architecture
Category: Computer Hardware
Category: C and C++
Category: Programming Principles
Category: Software Development
Category: Algorithms
Category: Distributed Computing
Category: System Programming
What you'll learn
Students will learn how to utilize the CUDA framework to write C/C++ software that runs on CPUs and Nvidia GPUs.
Students will transform sequential CPU algorithms and programs into CUDA kernels that execute 100s to 1000s of times simultaneously on GPU hardware.
Skills you'll gain
Category: Performance Tuning
Category: Debugging
Category: Program Development
Category: Algorithms
Category: Command-Line Interface
Category: C and C++
Category: Data Storage
Category: Data Structures
Category: Computer Hardware
Category: Computational Thinking
Category: Distributed Computing
Category: Performance Testing
Category: Data Access
Category: Computer Architecture
What you'll learn
Students will learn to develop software that can be run in computational environments that include multiple CPUs and GPUs.
Students will develop software that uses CUDA to create interactive GPU computational processing kernels for handling asynchronous data.
Students will use CUDA, hardware memory capabilities, and algorithms/libraries to solve programming challenges including image processing.
Skills you'll gain
Category: Scalability
Category: Performance Tuning
Category: Algorithms
Category: Event-Driven Programming
Category: Computer Vision
Category: Data Structures
Category: System Programming
Category: Hardware Architecture
Category: Computer Graphics
Category: C and C++
Category: Distributed Computing
Category: Data Processing
Category: Image Analysis
Category: Software Development
What you'll learn
You will learn to develop software that performs high-level mathematics operations using libraries such as cuFFT and cuBLAS.
You will learn to use the Thrust library to perform a number of data manipulation and data structures that abstract away memory management.
You will learn to develop machine learning software for a variety of purposes using neural networks modeled using the cuTensor and cuDNN libraries.
Skills you'll gain
Category: Software Development
Category: Linear Algebra
Category: Algorithms
Category: Machine Learning
Category: Image Analysis
Category: Data Processing
Category: Artificial Neural Networks
Category: Data Science
Category: Numerical Analysis
Category: Machine Learning Methods
Category: Data Transformation
Category: Deep Learning
Category: Performance Tuning
Category: Data Structures
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
Build toward a degree
When you complete this Specialization, you may be able to have your learning recognized for credit if you are admitted and enroll in one of the following online degree programs.¹
Instructor
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Frequently asked questions
Each course in the specialization is aimed to be completed in 1 month. The full specialization should be completed in 4 months.
Prospective students should have a minimum of 1 year of programming experience. A high level of comfort in programming in C/C++ will aid in the absorbtion of material and completion of assignments.
Each course in the specialization should be completed in the following order:
Introduction to Concurrent Programming with GPUs
Introduction to Parallel Programming with CUDA
CUDA at Scale for the Enterprise
CUDA Advanced Libraries
More questions
Financial aid available,