Johns Hopkins University
GPU Programming Specialization

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Johns Hopkins University

GPU Programming Specialization

Solve Challenges with Powerful GPUs. Develop mastery in high performance computing and apply to numerous fields.

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Intermediate level

Recommended experience

2 months
at 10 hours a week
Flexible schedule
Learn at your own pace
Build toward a degree
Get in-depth knowledge of a subject
Intermediate level

Recommended experience

2 months
at 10 hours a week
Flexible schedule
Learn at your own pace
Build toward a degree

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

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Taught in English

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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: Debugging
Category: Computer Programming
Category: Python Programming
Category: Computer Hardware
Category: Computer Architecture
Category: Software Development
Category: Algorithms
Category: Development Environment
Category: C and C++
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: Command-Line Interface
Category: Algorithms
Category: Computational Thinking
Category: Performance Testing
Category: Distributed Computing
Category: Data Structures
Category: Data Storage
Category: Computer Hardware
Category: Data Access
Category: C and C++
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: Performance Tuning
Category: Event-Driven Programming
Category: Scalability
Category: Algorithms
Category: System Programming
Category: Computer Graphics
Category: Distributed Computing
Category: Software Development
Category: Computer Vision
Category: Data Processing
Category: C and C++
Category: Image Analysis
Category: Hardware Architecture
CUDA Advanced Libraries

CUDA Advanced Libraries

Course 425 hours

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: Deep Learning
Category: Data Transformation
Category: Artificial Neural Networks
Category: Machine Learning Methods
Category: Data Processing
Category: Performance Tuning
Category: Image Analysis
Category: Numerical Analysis
Category: Data Structures

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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

Chancellor Thomas Pascale
Johns Hopkins University
4 Courses20,290 learners

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