Johns Hopkins University
GPU Programming Specialization
Johns Hopkins University

GPU Programming Specialization

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

Chancellor Thomas Pascale

Instructor: Chancellor Thomas Pascale

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Get in-depth knowledge of a subject
Intermediate level

Recommended experience

Flexible schedule
2 months at 10 hours a week
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Build toward a degree
Earn a Certificate
With paid plans
Get in-depth knowledge of a subject
Intermediate level

Recommended experience

Flexible schedule
2 months at 10 hours a week
Learn at your own pace
Build toward a degree
Earn a Certificate
With paid plans

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
9 practice exercises

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Specialization - 4 course series

Introduction to Concurrent Programming with GPUs

Introduction to Concurrent Programming with GPUs

Course 119 hours

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

C++ (Programming Language), Python Programming, Debugging, Computer Programming, Computer Hardware, Computer Architecture, Software Development, Development Environment, C and C++, System Programming, Programming Principles, and Distributed Computing
Introduction to Parallel Programming with CUDA

Introduction to Parallel Programming with CUDA

Course 221 hours

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

Performance Tuning, Debugging, Program Development, C and C++, Algorithms, Computer Architecture, Data Structures, Hardware Architecture, Distributed Computing, Performance Testing, and OS Process Management
CUDA at Scale for the Enterprise

CUDA at Scale for the Enterprise

Course 328 hours

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

Scalability, Performance Tuning, Event-Driven Programming, Algorithms, Data Structures, Computer Graphics, Hardware Architecture, System Programming, C and C++, Data Processing, Image Analysis, Distributed Computing, Software Development, and Computer Vision
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

Software Development, Linear Algebra, Algorithms, Deep Learning, Data Transformation, Performance Tuning, Data Science, Data Structures, Artificial Neural Networks, Machine Learning Methods, Machine Learning, and Image Analysis

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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 Courses23,028 learners

Offered by

Johns Hopkins University

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