We may earn an affiliate commission when you visit our partners.
Rich Vuduc
and
Catherine Gamboa
Take Udacity's high performance computing course and gain a solid foundation in developing, analyzing and implementing parallel and locality-efficient algorithms.
Register for this course and see more details by visiting:
OpenCourser.com/course/1mmtbl/udacity
Here's a deal for you
Save money when you learn with a deal that may be relevant to this course.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
OPENCOURSER
What's inside
Syllabus
Course Information
Introduction to High Performance Computing
Intro to the Work-Span Model
Intro to OpenMP
Read more
Syllabus
Course Information
Introduction to High Performance Computing
Intro to the Work-Span Model
Intro to OpenMP
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Teaches skills and knowledge that are highly relevant to industry
Builds a strong foundation for beginners
Strengthens an existing foundation for intermediate learners
Develops professional skills or deep expertise in a particular topic or set of topics
Offers hands-on labs and interactive materials
Covers unique perspectives and ideas that may add color to other topics and subjects
Save this course
Create your own learning path.
Save this course to your list so you can find it easily later.
Save
Save
Reviews summary
Foundational high performance computing
According to learners, this course offers a solid foundation in High Performance Computing, focusing on the development and analysis of parallel and locality-efficient algorithms. Students praise its coverage of fundamental parallel programming paradigms like OpenMP and MPI. While providing significant theoretical depth and a comprehensive overview of algorithmic efficiency, some learners find the material challenging for beginners and suggest a strong prior background in programming is beneficial. Overall, it is seen as a crucial step for those aiming to master the complexities of high-performance systems.
Strong focus on theoretical principles over direct coding.
"The course deeply emphasizes the underlying theory of parallel algorithms, which I found very valuable for understanding."
"While I appreciated the foundational theory, I wished for more hands-on coding assignments or practical projects to apply the concepts."
"It provides a very strong conceptual framework, preparing you for more practical implementations later on."
Covers essential HPC topics broadly and systematically.
"The curriculum's progression from Work-Span models to Distributed BFS provided a very broad and valuable overview."
"I appreciate the inclusion of discussions on algorithmic time, energy, and power, which are often overlooked."
"The modules on cache-oblivious algorithms and I/O-avoiding techniques were particularly insightful and relevant."
Provides a robust introduction to core HPC concepts.
"I gained a solid understanding of parallel and locality-efficient algorithms that's crucial for my work."
"The course helped me build a strong base in both shared (OpenMP) and distributed (MPI) memory models."
"It's an excellent primer for anyone looking to grasp the fundamentals of high performance computing."
The material can be challenging, requiring strong prerequisites.
"I found some concepts quite demanding and benefited from having a solid background in C++ and algorithms."
"Be prepared for a steep learning curve if you are new to parallel programming; it's not entry-level."
"It's less a beginner's course and more for those with a strong technical foundation seeking specialized knowledge."
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 High Performance Computing with these
activities:
Develop a digital notebook or use a tool like Notion to organize lecture notes
Show steps
Organizing your notes will help you retain concepts better, save time searching for information, and help you find patterns that you may not have otherwise seen.
Show steps
-
Decide on the organizational structure
-
Set up a template in your digital notebook or Notion
-
Add notes to your digital notebook or Notion after each lecture
Follow online tutorials on topics like OpenMP, MPI, and parallel algorithm techniques.
Show steps
Following guided tutorials will provide you with hands-on experience and a deeper understanding of the concepts covered in this course.
Show steps
-
Find online tutorials on topics covered in the course
-
Follow the instructions provided in the tutorials
Solve practice problems on codeforces
Show steps
Solving practice problems on codeforces.com will reinforce your understanding of parallel algorithm techniques.
Show steps
-
Create a user account on codeforces.com
-
Filter problems that are labelled with tags like parallel algorithm, dp, data structures etc.
-
Attempt to solve the problems
-
Review solutions when you are stuck
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve sample problems from the course website
Show steps
Solving sample problems will reinforce your understanding of the concepts covered in each lecture.
Show steps
-
Attempt to solve the problems on your own
-
Review solutions to the problems
Develop a simple parallel algorithm to solve a problem of your choice
Show steps
Developing your own parallel algorithm will test your understanding of the concepts and principles covered in this course and strengthen your problem-solving abilities.
Show steps
-
Define the problem you want to solve
-
Design a parallel algorithm to solve the problem
-
Implement your algorithm in a programming language of your choice
-
Test and evaluate the performance of your algorithm
Study the book 'Parallel Programming: Techniques and Applications' by Barry Wilkinson and Michael Allen
Show steps
This book delves deeply into different parallel programming paradigms which will complement the brief coverage of these paradigms in this course.
View
并行程序设计/网络工作站与并行计算机的技术与应用/[英文本]/Parallel...
on Amazon
Show steps
Develop a digital notebook or use a tool like Notion to organize lecture notes
Show steps
Organizing your notes will help you retain concepts better, save time searching for information, and help you find patterns that you may not have otherwise seen.
Show steps
Show steps
Show steps
- Decide on the organizational structure
- Set up a template in your digital notebook or Notion
- Add notes to your digital notebook or Notion after each lecture
Follow online tutorials on topics like OpenMP, MPI, and parallel algorithm techniques.
Show steps
Following guided tutorials will provide you with hands-on experience and a deeper understanding of the concepts covered in this course.
Show steps
Show steps
Show steps
- Find online tutorials on topics covered in the course
- Follow the instructions provided in the tutorials
Solve practice problems on codeforces
Show steps
Solving practice problems on codeforces.com will reinforce your understanding of parallel algorithm techniques.
Show steps
Show steps
Show steps
- Create a user account on codeforces.com
- Filter problems that are labelled with tags like parallel algorithm, dp, data structures etc.
- Attempt to solve the problems
- Review solutions when you are stuck
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve sample problems from the course website
Show steps
Solving sample problems will reinforce your understanding of the concepts covered in each lecture.
Show steps
Show steps
Show steps
- Attempt to solve the problems on your own
- Review solutions to the problems
Develop a simple parallel algorithm to solve a problem of your choice
Show steps
Developing your own parallel algorithm will test your understanding of the concepts and principles covered in this course and strengthen your problem-solving abilities.
Show steps
Show steps
Show steps
- Define the problem you want to solve
- Design a parallel algorithm to solve the problem
- Implement your algorithm in a programming language of your choice
- Test and evaluate the performance of your algorithm
Study the book 'Parallel Programming: Techniques and Applications' by Barry Wilkinson and Michael Allen
Show steps
This book delves deeply into different parallel programming paradigms which will complement the brief coverage of these paradigms in this course.
View
并行程序设计/网络工作站与并行计算机的技术与应用/[英文本]/Parallel...
on Amazon
Show steps
Show steps
Show steps
Career center
Learners who complete High Performance Computing will develop knowledge and skills
that may be useful to these careers:
Computational Scientist
Computational Scientist
Computational Scientists use computers to solve problems in science and engineering. These problems can be very complex and require the use of high performance computing systems. Computational Scientists may use parallel and locality-efficient algorithms to improve the performance of their simulations. The Udacity high performance computing course may be useful for someone in this role because it would provide them with a foundation in parallel and locality-efficient algorithms.
High Performance Computing Analyst
High Performance Computing Analyst
A High Performance Computing Analyst is a type of Computer Systems Analyst who specializes in high-performance computing environments. High Performance Computing Analysts may design, develop, and test parallel and locality-efficient algorithms in order to improve the performance of computing systems. They must also analyze and interpret data to identify performance bottlenecks and develop solutions to improve system efficiency. The Udacity high performance computing course would be helpful for someone in this role because it would provide them with a solid foundation in parallel and locality-efficient algorithms.
Data Scientist
Data Scientist
Data Scientists gather, analyze, interpret, and present data. This data is often used to make predictions or to improve the performance of a system. Data Scientists may use high performance computing systems to process large amounts of data quickly. The Udacity high performance computing course may be useful for someone in this role because it would provide them with a foundation in parallel and locality-efficient algorithms.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and test machine learning models. These models are used to make predictions or to improve the performance of a system. Machine Learning Engineers may use high performance computing systems to train large models quickly. The Udacity high performance computing course may be useful for someone in this role because it would provide them with a foundation in parallel and locality-efficient algorithms.
Software Engineer
Software Engineer
Software Engineers design, develop, and test software systems. These systems can be used for a variety of purposes, including high performance computing. Software Engineers may use parallel and locality-efficient algorithms to improve the performance of their software systems. The Udacity high performance computing course may be useful for someone in this role because it would provide them with a foundation in parallel and locality-efficient algorithms.
Systems Engineer
Systems Engineer
Systems Engineers design, develop, and test systems that include hardware, software, and networks. These systems can be used for a variety of purposes, including high performance computing. Systems Engineers may use parallel and locality-efficient algorithms to improve the performance of their systems. The Udacity high performance computing course may be useful for someone in this role because it would provide them with a foundation in parallel and locality-efficient algorithms.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical models to solve problems in business and industry. These problems can be very complex and require the use of high performance computing systems. Operations Research Analysts may use parallel and locality-efficient algorithms to improve the performance of their models. The Udacity high performance computing course may be useful for someone in this role because it would provide them with a foundation in parallel and locality-efficient algorithms.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make predictions about the future performance of companies or markets. These predictions can be used to make investment decisions or to develop financial plans. Financial Analysts may use high performance computing systems to process large amounts of data quickly. The Udacity high performance computing course may be useful for someone in this role because it would provide them with a foundation in parallel and locality-efficient algorithms.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk. This risk can come from a variety of sources, such as natural disasters, accidents, or financial losses. Actuaries may use high performance computing systems to process large amounts of data quickly. The Udacity high performance computing course may be useful for someone in this role because it would provide them with a foundation in parallel and locality-efficient algorithms.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. This data can be used to make predictions or to improve the performance of a system. Statisticians may use high performance computing systems to process large amounts of data quickly. The Udacity high performance computing course may be useful for someone in this role because it would provide them with a foundation in parallel and locality-efficient algorithms.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data. This data can be used to make predictions or to improve the performance of a system. Data Analysts may use high performance computing systems to process large amounts of data quickly. The Udacity high performance computing course may be useful for someone in this role because it would provide them with a foundation in parallel and locality-efficient algorithms.
Business Analyst
Business Analyst
Business Analysts analyze business processes and identify ways to improve efficiency. They may use high performance computing systems to process large amounts of data quickly. The Udacity high performance computing course may be useful for someone in this role because it would provide them with a foundation in parallel and locality-efficient algorithms.
Management Consultant
Management Consultant
Management Consultants help organizations improve their performance. They may use high performance computing systems to process large amounts of data quickly. The Udacity high performance computing course may be useful for someone in this role because it would provide them with a foundation in parallel and locality-efficient algorithms.
Product Manager
Product Manager
Product Managers develop and manage products. They may use high performance computing systems to process large amounts of data quickly. The Udacity high performance computing course may be useful for someone in this role because it would provide them with a foundation in parallel and locality-efficient algorithms.
Computer Scientist
Computer Scientist
Computer Scientists perform research on new computing technologies. This can include high performance computing systems. They may also design, develop, implement, test, or maintain computer applications or software systems. The Udacity high performance computing course may be useful for someone in this role because it would provide them with a foundation in parallel and locality-efficient algorithms.
For more career information including salaries, visit:
OpenCourser.com/course/1mmtbl/udacity
Reading list
We've selected 14 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
High Performance Computing.
Provides an introduction to the field of high-performance computing, particularly focusing on scientific and engineering applications. This book would be useful as a textbook for the course.
Introduces concepts of high performance computing by presenting fundamentals and important recent advances through a balanced treatment of computer architectures and algorithm design.
Provides a comprehensive overview of MPI, a popular message passing interface. It valuable resource for anyone who wants to learn more about this topic.
Presents the fundamentals of parallel programming and high-performance computing, with a focus on modern architectures and programming models.
Provides coverage of the field of parallel computing and is commonly used as a textbook or reference at academic institutions.
Provides a comprehensive overview of performance optimization for high-performance computing. It valuable resource for anyone who wants to learn more about this topic.
Provides a comprehensive overview of high-performance computing, focusing on algorithms and applications. This book would be a good reference for the course.
Provides a comprehensive overview of advanced programming techniques in the UNIX environment. It valuable resource for anyone who wants to learn more about this topic.
Provides a comprehensive overview of numerical recipes in C++. It valuable resource for anyone who wants to learn more about this topic.
Provides a comprehensive overview of parallel programming with MPI. It valuable resource for anyone who wants to learn more about this topic.
Save
Provides a comprehensive overview of OpenMP. It valuable resource for anyone who wants to learn more about this topic.
Save
Introduces the fundamentals and implementation of OpenMP. OpenMP popular programming model for shared memory systems. This book would be useful as supplemental reading for the course.
Provides a comprehensive overview of computer architecture. It valuable resource for anyone who wants to learn more about this topic.
Provides a comprehensive overview of operating system concepts. It valuable resource for anyone who wants to learn more about this topic.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1mmtbl/udacity
Share
Similar courses
Similar courses are unavailable at this time. Please try again later.
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2025 OpenCourser