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CUDA at Scale for the Enterprise

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CUDA at Scale for the Enterprise

This course is part of GPU Programming Specialization

Instructor: Chancellor Thomas Pascale

4,207 already enrolled

Included with

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5 modules

Gain insight into a topic and learn the fundamentals.

26 reviews

Intermediate level

Recommended experience

3 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

26 reviews

Intermediate level

Recommended experience

3 weeks to complete

at 10 hours a week

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.

Skills you'll gain

Tools you'll learn

C++ (Programming Language)

Details to know

Shareable certificate

Add to your LinkedIn profile

Taught in English

91%

of learners achieved a positive career outcome

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

There are 5 modules in this course

This course will aid in students in learning in concepts that scale the use of GPUs and the CPUs that manage their use beyond the most common consumer-grade GPU installations. They will learn how to manage asynchronous workflows, sending and receiving events to encapsulate data transfers and control signals. Also, students will walk through application of GPUs to sorting of data and processing images, implementing their own software using these techniques and libraries.

By the end of the course, you will be able to do the following: - Develop software that can use multiple CPUs and GPUs - Develop software that uses CUDA’s events and streams capability to create asynchronous workflows - Use the CUDA computational model to to solve canonical programming challenges including data sorting and image processing To be successful in this course, you should have an understanding of parallel programming and experience programming in C/C++. This course will be extremely applicable to software developers and data scientists working in the fields of high performance computing, data processing, and machine learning.

Module details

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

3 videosTotal 11 minutes

GPU Specialization Overview3 minutes
Course Expectations3 minutes
Coursera Lab and Assignment Overview5 minutes

1 readingTotal 10 minutes

Development Tools and C/C++ Refresher Material10 minutes

1 programming assignmentTotal 120 minutes

CUDA Device Memory Analysis Assignment120 minutes

2 discussion promptsTotal 20 minutes

Enterprise Data Processing Discussion Prompt10 minutes
Canonical Algorithms Discussion Prompt10 minutes

1 ungraded labTotal 60 minutes

CUDA Device Memory Lab60 minutes

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

7 videosTotal 32 minutes

Multiple CPU Architectures5 minutes
Multiple CPU Lab7 minutes
Multiple CPU Assignment4 minutes
Multiple CPUs vs Multiple GPUs6 minutes
CUDA Multiple GPU Programming Model4 minutes
Multiple GPU Activity4 minutes
Multiple GPU Assignment2 minutes

2 programming assignmentsTotal 180 minutes

Multiple CPU Assignment120 minutes
CUDA GPU identification Assignment60 minutes

1 peer reviewTotal 240 minutes

Competing GPUs240 minutes

1 discussion promptTotal 15 minutes

Multiple CPU/Distributed Computing Discussion15 minutes

2 ungraded labsTotal 120 minutes

Multiple CPU Lab60 minutes
CUDA GPU Identification Laboratory Activity60 minutes

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

5 videosTotal 28 minutes

CUDA Streams and Events5 minutes
CUDA Streams Syntax5 minutes
CUDA Events Syntax6 minutes
CUDA Streams and Events Use Cases5 minutes
CUDA Streams and Events Assignment Walkthrough7 minutes

2 readingsTotal 20 minutes

CUDA Streams External Resources10 minutes
CUDA Streams and Events External Resources10 minutes

1 programming assignmentTotal 120 minutes

CUDA Streams and Events Assignment120 minutes

1 discussion promptTotal 10 minutes

CUDA Streams and Events Discussion10 minutes

1 ungraded labTotal 60 minutes

CUDA Streams and Events Lab Activity60 minutes

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

11 videosTotal 57 minutes

Using Input Data to Develop GPU Pseudocode8 minutes
Sorting Algorithm GPU Pseudocode Bubble Sort5 minutes
Sorting Algorithm GPU Pseudocode Radix Sort9 minutes
Sorting Algorithm GPU Pseudocode Quick Sort7 minutes
Memory and GPU Pseudocode Bubble Sort4 minutes
Memory and GPU Pseudocode Radix Sort3 minutes
Memory and GPU Pseudocode Quick Sort3 minutes
Sorting Algorithms Lab Activity Bubble Sort4 minutes
Sorting Algorithms Lab Activity Radix Sort4 minutes
Sorting Algorithms Lab Activity Quick Sort7 minutes
Sorting Algorithms Assignment3 minutes

1 readingTotal 10 minutes

GPU Sort Algorithm Reading List10 minutes

1 programming assignmentTotal 180 minutes

CUDA Merge Sort Algorithm Assignment180 minutes

1 discussion promptTotal 10 minutes

Fundamental Algorithms and Programming Discussion10 minutes

1 ungraded labTotal 60 minutes

Sorting Algorithms Lab Activity60 minutes

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

7 videosTotal 52 minutes

NPP Image Processing Syntax6 minutes
NPP Image Processing Code Demonstration7 minutes
NPP Signal Processing Syntax5 minutes
NPP Signals Processing Syntax Demonstration5 minutes
Course Independent Project Lab Overview10 minutes
Course Independent Project Overview11 minutes
Course Independent Project Rubric Overview9 minutes

1 peer reviewTotal 240 minutes

CUDA at Scale Independent Project240 minutes

1 discussion promptTotal 10 minutes

Image and Signal Processing Discussion10 minutes

1 ungraded labTotal 60 minutes

NPP Box Filter Laboratory60 minutes

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Instructor

Instructor ratings

(6 ratings)

Chancellor Thomas Pascale

Johns Hopkins University

4 Courses27,592 learners

Offered by

Johns Hopkins University

CUDA at Scale for the Enterprise

CUDA at Scale for the Enterprise

What you'll learn

Skills you'll gain

Tools you'll learn

Details to know

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

Build your subject-matter expertise

There are 5 modules in this course

Course Overview

What's included

Multiple CPU/GPU Systems

What's included

CUDA Events and Streams

What's included

Sorting Using GPUs

What's included

Image Processing using Nvidia Programming Primitives

What's included

Earn a career certificate

Instructor

Offered by

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Frequently asked questions

Can I program on my own desktop/laptop

When will I have access to the lectures and assignments?

What will I get if I subscribe to this Specialization?

More questions