Introduction to Concurrent Programming with GPUs from Coursera

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Syllabus

Course Overview

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

Core Principles of Parallel Programming on CPUs and GPUs

In order to create software that process greater amounts of data at faster speeds, software operating systems, programming languages, and frameworks require strategies for accessing and modification of data in a manner that maximizes speed, while minimizing the possibility of data being in incorrect states. In this module, students will be presented canonical concurrency problems such as the Dining Philosophers. Additionally, they will learn how operating systems and programming languages handle these problems, and discuss real world big data concurrency applications.

Introduction to Parallel Programming with C and Python

Modern programming languages allow developers to create software with complex logic for manipulation of data in parallel, taking advantage of the multiple CPU cores in most computers. Students will develop simple software, written in the C++ and Python 3 programming languages, that process data sets concurrently.

NVidia GPU Hardware/Software

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.

Introduction to GPU Programming

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

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Relevant to an academic setting and builds a strong foundation for users to continue their study of computer science

Taught by Professor Chancellor Thomas Pascale, who is recognized for his work in computer science

Explores foundational aspects of concurrent programming, including CPU/GPU architectures, multithreaded programming, and CUDA software/hardware

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Introduction to Concurrent Programming with GPUs with these activities:

Refresh Introduction to C++

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Review your familiarity with the core concepts of the C++ programming language to ensure your knowledge is up-to-date

Browse courses on C++

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Review the basic syntax of C++
Practice writing simple C++ programs
Complete a few online tutorials on C++

Refresh Introduction to Python

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Review your familiarity with the core concepts of the Python programming language to ensure your knowledge is up-to-date

Browse courses on Python

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Review the basic syntax of Python
Practice writing simple Python programs
Complete a few online tutorials on Python

Explore CUDA Software and Hardware

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Seek out tutorials to expand your understanding of the underlying principles of CUDA software and hardware

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Search online for CUDA tutorials for beginners
Follow a few of these tutorials to learn about the basics of CUDA
Try implementing some of the examples provided in the tutorials

Three other activities

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Practice Algorithm Implementations

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Put your understanding of algorithms to the test by practicing their implementation

Browse courses on Algorithms

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Find a platform or website that offers coding challenges
Select a few problems to work on
Implement your solutions in the programming language of your choice

Practice Concurrent Programming Problems

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Reinforce your understanding of concurrent programming by solving practice problems

Browse courses on Concurrency

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Find a platform or website that offers coding challenges specifically related to concurrent programming
Select a few problems to work on
Implement your solutions in the programming language of your choice

Develop a CUDA-Based Application

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Put your skills to the test by creating a project that involves developing a CUDA-based parallel computing application

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Identify a problem or task that can benefit from parallelization using CUDA
Design and develop your solution using CUDA
Test and evaluate the performance of your application

Career center

Learners who complete Introduction to Concurrent Programming with GPUs will develop knowledge and skills that may be useful to these careers:

Software Developer

Software Developers who are proficient in GPU programming can develop software applications that can run more efficiently and quickly by leveraging the parallel processing capabilities of GPUs. This course may be useful for Software Developers because it develops foundational skills in GPU computing and programming, while also introducing students to CUDA-based software development.

See salaries and explore the career path for Software Developer

Machine Learning Engineer

Machine Learning Engineers who are well-versed in GPU programming can develop more efficient and effective machine learning models by leveraging the parallel processing capabilities of GPUs. This course may be useful for Machine Learning Engineers because it develops foundational skills in GPU computing and programming, while also introducing students to CUDA-based software development.

See salaries and explore the career path for Machine Learning Engineer

Computational Scientist

Computational Scientists who are familiar with GPU programming can develop and use computational models that can run more efficiently and quickly by leveraging the parallel processing capabilities of GPUs. This course may be useful for Computational Scientists because it develops foundational skills in GPU computing and programming, while also introducing students to CUDA-based software development.

See salaries and explore the career path for Computational Scientist

Data Scientist

Data Scientists with specialized knowledge in GPU-accelerated data processing can harness the power of these technologies to process large datasets more efficiently and quickly. This course may be useful for Data Scientists because it develops foundational skills in GPU computing and programming, while also introducing students to CUDA-based software development.

See salaries and explore the career path for Data Scientist

Computer Engineer

Computer Engineers who are proficient in GPU programming can design and develop computer systems that can take advantage of the parallel processing capabilities of GPUs. This course may be useful for Computer Engineers because it develops foundational skills in GPU computing and programming, while also introducing students to CUDA-based software development.

See salaries and explore the career path for Computer Engineer

Systems Programmer

Systems Programmers who are proficient in GPU programming can develop operating systems and other system software that can take advantage of the parallel processing capabilities of GPUs. This course may be useful for Systems Programmers because it develops foundational skills in GPU computing and programming, while also introducing students to CUDA-based software development.

See salaries and explore the career path for Systems Programmer

Software Architect

Software Architects who understand the principles of GPU programming can design and develop software systems that can take advantage of the parallel processing capabilities of GPUs. This course may be useful for Software Architects because it develops foundational skills in GPU computing and programming, while also introducing students to CUDA-based software development.

See salaries and explore the career path for Software Architect

Computer Architect

Computer Architects who are familiar with GPU programming can design and develop computer architectures that are optimized for GPU-accelerated computing. This course may be useful for Computer Architects because it develops foundational skills in GPU computing and programming, while also introducing students to CUDA-based software development.

See salaries and explore the career path for Computer Architect

Systems Engineer

Systems Engineers who are familiar with GPU programming can design and implement systems that can take advantage of the parallel processing capabilities of GPUs. This course may be useful for Systems Engineers because it develops foundational skills in GPU computing and programming, while also introducing students to CUDA-based software development.

See salaries and explore the career path for Systems Engineer

Hardware Engineer

Hardware Engineers who are knowledgeable about GPU programming can design and develop computer hardware that is optimized for GPU-accelerated computing. This course may be useful for Hardware Engineers because it develops foundational skills in GPU computing and programming, while also introducing students to CUDA-based software development.

See salaries and explore the career path for Hardware Engineer

Mechanical Engineer

Mechanical Engineers who are proficient in GPU programming can develop and use computational models that can run more efficiently and quickly by leveraging the parallel processing capabilities of GPUs. This course may be useful for Mechanical Engineers because it develops foundational skills in GPU computing and programming, while also introducing students to CUDA-based software development.

See salaries and explore the career path for Mechanical Engineer

Aerospace Engineer

Aerospace Engineers who are familiar with GPU programming can develop and use computational models that can run more efficiently and quickly by leveraging the parallel processing capabilities of GPUs. This course may be useful for Aerospace Engineers because it develops foundational skills in GPU computing and programming, while also introducing students to CUDA-based software development.

See salaries and explore the career path for Aerospace Engineer

Big Data Engineer

Big Data Engineers with expertise in developing software for GPUs can play a critical role in ensuring that the applications they develop can handle the large and complex datasets they often encounter. This course may be useful for Big Data Engineers because it develops foundational skills in GPU computing and programming, while also introducing students to CUDA-based software development.

See salaries and explore the career path for Big Data Engineer

Electrical Engineer

Electrical Engineers who are knowledgeable about GPU programming can design and develop electrical systems that are optimized for GPU-accelerated computing. This course may be useful for Electrical Engineers because it develops foundational skills in GPU computing and programming, while also introducing students to CUDA-based software development.

See salaries and explore the career path for Electrical Engineer

Civil Engineer

Civil Engineers who are knowledgeable about GPU programming can develop and use computational models that can run more efficiently and quickly by leveraging the parallel processing capabilities of GPUs. This course may be useful for Civil Engineers because it develops foundational skills in GPU computing and programming, while also introducing students to CUDA-based software development.

See salaries and explore the career path for Civil Engineer

Introduction to Concurrent Programming with GPUs

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