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Chancellor Thomas Pascale
This course will help prepare students for developing code that can process large amounts of data in parallel on Graphics Processing Units (GPUs). It will learn on how to implement software that can solve complex problems with the leading consumer to enterprise-grade GPUs available using Nvidia CUDA. They will focus on the hardware and software capabilities, including the use of 100s to 1000s of threads and various forms of memory.
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What's inside
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.
Read more
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.
Read more
Threads, Blocks and Grids
The single most important concept for using GPUs to solve complex and large-scale problems, is management of threads. CUDA provides two- and three-dimensional logical abstractions of threads, blocks and grids. Students will develop programs that utilize threads, blocks, and grids to process large 2 to 3-dimensional data sets.
Host and Global Memory
To manage the access and modification of data in physical memory effectively, students will need to load data into CPU (host) and GPU (global) general-purpose memory. Students will create software that allocates host memory and transfers it into global memory for use by threads. Students will also learn the capabilities and speeds of these types of memories.
Shared and Constant Memory
To improve performance in GPU software, students will need to utilized mutable (shared) and static (constant) memory. They will use them to apply masks to all items of a data set, to manage the communication between threads, and use for caching in complex programs.
Register Memory
In this module, students will learn the benefits and constraints of GPUs most hyper-localized memory, registers. While using this type of memory will be natural for students, gaining the largest performance boost from it, like all forms of memory, will require thoughtful design of software. Students will develop implementations of algorithms using each type of memory and generate performance analysis.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Delves into the use of Nvidia CUDA, a leading technology in the industry
Provides hands-on experience in developing code for parallel processing on GPUs
Covers various forms of memory, including shared and constant memory, to optimize performance
Introduces CUDA's thread, block, and grid concepts for effective thread management
Assumes prior knowledge in programming and requires students to have their own computer with a GPU
Taught by Chancellor Thomas Pascale, an expert in the field with extensive experience
Save this course
Save Introduction to Parallel Programming with CUDA to your list so you can find it easily later:
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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 Parallel Programming with CUDA with these
activities:
Solve exercises implementing various kinds of memory in GPU CUDA
Show steps
Practice exercises will help reinforce understanding of the capabilities and limitations of different memory types in GPU CUDA.
Show steps
-
Review the course materials on memory management in GPU CUDA.
-
Choose a set of practice exercises that cover different types of memory and their applications.
-
Solve the exercises and test your code to ensure it works correctly.
-
Analyze the results of your exercises to identify areas where you need further practice.
-
Repeat steps 2-4 until you are confident in your understanding of memory management in GPU CUDA.
Show all one activities
Solve exercises implementing various kinds of memory in GPU CUDA
Show steps
Practice exercises will help reinforce understanding of the capabilities and limitations of different memory types in GPU CUDA.
Show steps
Show steps
Show steps
- Review the course materials on memory management in GPU CUDA.
- Choose a set of practice exercises that cover different types of memory and their applications.
- Solve the exercises and test your code to ensure it works correctly.
- Analyze the results of your exercises to identify areas where you need further practice.
- Repeat steps 2-4 until you are confident in your understanding of memory management in GPU CUDA.
Show all one activities
Career center
Learners who complete Introduction to Parallel Programming with CUDA will develop knowledge and skills
that may be useful to these careers:
Computer Engineer
Computer Engineer
Computer Engineers design and build computer systems. The course Introduction to Parallel Programming with CUDA can be useful in this role because it teaches students how to develop software that can solve complex problems using GPUs. This skill can be valuable for Computer Engineers who need to design and build computer systems that can handle large amounts of data.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop and maintain machine learning models. The course Introduction to Parallel Programming with CUDA can be useful in this role because it teaches students how to develop software that can solve complex problems using GPUs. This skill can be valuable for Machine Learning Engineers who need to develop machine learning models that can handle large amounts of data.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve problems in business and industry. The course Introduction to Parallel Programming with CUDA can be useful in this role because it teaches students how to develop software that can solve complex problems using GPUs. This skill can be valuable for Operations Research Analysts who need to develop software that can handle large amounts of data.
Cloud Architect
Cloud Architect
Cloud Architects design and implement cloud computing solutions. The course Introduction to Parallel Programming with CUDA can be useful in this role because it teaches students how to develop software that can solve complex problems using GPUs. This skill can be valuable for Cloud Architects who need to design and implement cloud computing solutions that can handle large amounts of data.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. The course Introduction to Parallel Programming with CUDA can be useful in this role because it teaches students how to develop software that can solve complex problems using GPUs. This skill can be valuable for Actuaries who need to develop software that can handle large amounts of data.
Software Developer
Software Developer
Software Developers implement and maintain software systems. The course Introduction to Parallel Programming with CUDA can be useful in this role because it teaches students how to develop software that can solve complex problems using GPUs. This skill can be valuable for Software Developers who need to develop software that can handle large amounts of data.
Systems Analyst
Systems Analyst
Systems Analysts design and implement IT systems. The course Introduction to Parallel Programming with CUDA can be useful in this role because it teaches students how to develop software that can solve complex problems using GPUs. This skill can be valuable for Systems Analysts who need to design and implement IT systems that can handle large amounts of data.
Software Architect
Software Architect
Software Architects design and develop software systems. The course Introduction to Parallel Programming with CUDA can be useful in this role because it teaches students how to develop software that can solve complex problems using GPUs. This skill can be valuable for Software Architects who need to design and develop software systems that can handle large amounts of data.
Database Administrator
Database Administrator
Database Administrators maintain and optimize databases. The course Introduction to Parallel Programming with CUDA can be useful in this role because it teaches students how to develop software that can solve complex problems using GPUs. This skill can be valuable for Database Administrators who need to develop software that can handle large amounts of data.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines. The course Introduction to Parallel Programming with CUDA can be useful in this role because it teaches students how to develop software that can solve complex problems using GPUs. This skill can be valuable for Data Engineers who need to design and build data pipelines that can handle large amounts of data.
Statistician
Statistician
Statisticians collect and analyze data in order to draw conclusions about the world. The course Introduction to Parallel Programming with CUDA can be useful in this role because it teaches students how to process large amounts of data in parallel on Graphics Processing Units (GPUs). This skill can be valuable for Statisticians who need to process large data sets quickly and efficiently.
Data Analyst
Data Analyst
Data Analysts collect and analyze data in order to extract meaningful insights. The course Introduction to Parallel Programming with CUDA can be useful in this role because it teaches students how to process large amounts of data in parallel on Graphics Processing Units (GPUs). This skill can be valuable for Data Analysts who need to process large data sets quickly and efficiently.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts collect and analyze data in order to extract meaningful insights. The course Introduction to Parallel Programming with CUDA can be useful in this role because it teaches students how to process large amounts of data in parallel on Graphics Processing Units (GPUs). This skill can be valuable for Business Intelligence Analysts who need to process large data sets quickly and efficiently.
Financial Analyst
Financial Analyst
Financial Analysts collect and analyze financial data in order to make investment recommendations. The course Introduction to Parallel Programming with CUDA can be useful in this role because it teaches students how to process large amounts of data in parallel on Graphics Processing Units (GPUs). This skill can be valuable for Financial Analysts who need to process large data sets quickly and efficiently.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting and analyzing data in order to extract meaningful insights. The course Introduction to Parallel Programming with CUDA can be useful in this role because it teaches students how to process large amounts of data in parallel on Graphics Processing Units (GPUs). This skill can be valuable for Data Scientists who need to process large data sets quickly and efficiently.
For more career information including salaries, visit:
OpenCourser.com/course/snxfeq/introduction
Reading list
We've selected eight books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Introduction to Parallel Programming with CUDA.
Save
Provides a comprehensive introduction to GPU programming with CUDA. It covers the basics of CUDA programming, including threads, blocks, grids, and memory management. It also provides a number of examples of how to use CUDA to solve real-world problems.
Provides a comprehensive overview of parallel programming. It covers the different types of parallel programming models, including CUDA. It also provides a number of examples of how to use CUDA to solve real-world problems.
Classic textbook on computer architecture. It provides a good background in the underlying hardware that is used for parallel programming.
Classic textbook on operating systems. It provides a good background in the concepts that are related to parallel programming, such as memory management and process scheduling.
Provides a comprehensive overview of parallel programming for many-core chips. It covers the different types of parallel programming models, including CUDA. It also provides a number of examples of how to use CUDA to solve real-world problems.
Provides a comprehensive overview of parallel programming techniques and applications. It good resource for students who want to learn more about the practical aspects of parallel programming.
Textbook on parallel computer architecture. It provides a good overview of the hardware and software that is used for parallel programming.
Save
Textbook on parallel programming with OpenMP. OpenMP programming model that is used to develop parallel programs for shared memory systems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/snxfeq/introduction
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Intro to Parallel Programming
Effort
4-5 weeks of study, 4-8 hours/week
Level
Intermediate
Via
Coursera
Institution
Johns Hopkins University
Instructor
Chancellor Thomas Pascale
Language
English
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This course belongs to a
collection
called
GPU Programming. It's comprised of four courses:
- CUDA at Scale for the Enterprise
- CUDA Advanced Libraries
- Introduction to Concurrent Programming with GPUs
- Introduction to Parallel Programming with CUDA (viewing)
Good to know
Delves into the use of Nvidia CUDA, a leading technology in the industry
Provides hands-on experience in developing code for parallel processing on GPUs
Covers various forms of memory, including shared and constant memory, to optimize performance
Introduces CUDA's thread, block, and grid concepts for effective thread management
Assumes prior knowledge in programming and requires students to have their own computer with a GPU
Taught by Chancellor Thomas Pascale, an expert in the field with extensive experience
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