Johns Hopkins University
CUDA at Scale for the Enterprise
Johns Hopkins University

CUDA at Scale for the Enterprise

This course is part of GPU Programming Specialization

Taught in English

Some content may not be translated

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

28 hours (approximately)
Flexible schedule
Learn at your own pace

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.

Details to know

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Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

28 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

This course is part of the GPU Programming Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
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There are 5 modules in this course

The purpose of this module is for students to understand how the course will be run, topics, how they will be assessed, and expectations.

What's included

3 videos1 reading1 programming assignment2 discussion prompts1 ungraded lab

In professional settings, use of one CPU managing one GPU, is not a viable configuration to solve complex challenges. Students will apply CUDA capabilities for allowing multiple CPUs to communicate and manage software kernels on multiple GPUs. This will allow for scaling the size of input data and computational complexity. Students will learn the advantages and limitations of this form of synchronous processing.

What's included

7 videos2 programming assignments1 peer review1 discussion prompt2 ungraded labs

Students will learn to utilize CUDA events and streams in their programs, to allow for asynchronous data and control flows. This will allow more interactive and long-lasting software, including analytic user interfaces, near live-streaming video or financial feeds, and dynamic business processing systems.

What's included

5 videos2 readings1 programming assignment1 discussion prompt1 ungraded lab

The purpose of this module is for students to understand the basis in hardware and software that CUDA uses. This is required to appropriately develop software to optimally take advantage of GPU resources.

What's included

11 videos1 reading1 programming assignment1 discussion prompt1 ungraded lab

The purpose of this module is for students to understand the principles of developing CUDA-based software.

What's included

7 videos1 peer review1 discussion prompt1 ungraded lab

Instructor

Chancellor Thomas Pascale
Johns Hopkins University
4 Courses10,553 learners

Offered by

Recommended if you're interested in Software Development

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