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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.
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Traffic lights

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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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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
Practice exercises will help reinforce understanding of the capabilities and limitations of different memory types in GPU CUDA.
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  • 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.
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Career center

Learners who complete Introduction to Parallel Programming with CUDA will develop knowledge and skills that may be useful to these careers:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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 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.
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.
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.

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.
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.
Textbook on parallel computer architecture. It provides a good overview of the hardware and software that is used for parallel programming.
Textbook on parallel programming with OpenMP. OpenMP programming model that is used to develop parallel programs for shared memory systems.

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