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Weronika Filinger, David Henty, Emmanouil (Manos) Farsarakis, Zheng Meyer-Zhao, Jane Kennedy, Bryan Johnston, and Ann B

Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course is designed for anyone interested in leading-edge computing technology, supercomputers or the role that computer simulation takes in modern science and engineering. All of the technical aspects will be covered at a conceptual level and there is no requirement to be able to write computer programs. However, anyone with existing programming experience will learn how programming modern supercomputers differs from programming a home PC. You can use the hashtag #FLsupercomputing to talk about this course on social media.

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Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course is designed for anyone interested in leading-edge computing technology, supercomputers or the role that computer simulation takes in modern science and engineering. All of the technical aspects will be covered at a conceptual level and there is no requirement to be able to write computer programs. However, anyone with existing programming experience will learn how programming modern supercomputers differs from programming a home PC. You can use the hashtag #FLsupercomputing to talk about this course on social media.

Topics Covered

  • Supercomputers: introducing supercomputing terminology and some of the largest machines in the world
  • Parallel computers: how they are built from hundreds of thousands of CPUs, each similar to those in a desktop PC
  • Parallel computing: using parallel processing to harness the power of all of those CPUs for a single calculation
  • Computer simulation: how we can perform virtual experiments to make real-life predictions
  • Case studies: how supercomputing is making scientific breakthroughs that were never possible before

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Reviews summary

Supercomputing: conceptual overview & applications

According to learners, "Supercomputing" is a largely positive course that offers an accessible and engaging introduction to high-performance computing. Students praise its clear explanations and the impactful case studies that effectively illustrate real-world applications of supercomputers. The course is particularly well-suited for individuals with no prior programming experience, providing a strong conceptual overview of supercomputers, parallel computing, and simulation. While many found the lectures and overall structure to be excellent, some learners with existing programming backgrounds felt the content was too high-level or lacked practical coding examples. A few older reviews also noted that some content might be outdated in its specific examples, though more recent feedback doesn't emphasize this.
Real-world examples make complex topics relatable and inspiring.
"The case studies were particularly engaging and helped me visualize the real-world impact of supercomputing."
"I learned so much! This course demystified supercomputers, and the examples were excellent in showing their role in science."
"I particularly enjoyed understanding how computer simulation works, and the compelling case studies truly inspired me."
Provides a clear, conceptual overview for all learners, especially beginners.
"Absolutely fantastic introduction to supercomputing! I had no prior programming experience, but the concepts were explained so clearly."
"This course does a great job of breaking down complex ideas into digestible parts... It caters really well to a general audience, which I appreciated."
"I found this course foundational and it made supercomputing accessible. The instructors explain complex topics simply."
Some specific examples and visuals may feel dated to some learners.
"I found some of the content a little outdated, especially the discussion around certain hardware architectures; the examples could use a refresh."
"The visuals were sometimes hard to follow on smaller screens, and I felt it relied too much on older references, making it feel less current."
"While the core concepts are timeless, I felt the specific examples in the course might not reflect the most current state of the art in the field."
May not satisfy those seeking in-depth practical skills or advanced topics.
"I found it a bit too high-level for my needs; I was hoping for more practical application or some basic labs."
"As a programmer, while I liked the parallel computing sections, I wished there were more deep dives or coding examples to challenge me more."
"I came in hoping for practical insights into parallel programming for my job, but this was far too theoretical and basic for that purpose."

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Provides a comprehensive overview of high performance computing, from its architecture to its applications. It covers the basics of HPC, as well as the latest advances in the field. The book is written by three leading experts in the field.
Provides a comprehensive introduction to parallel computing. It covers the basics of parallel programming, as well as the latest advances in the field. The book is written by a leading expert in the field.
Provides a comprehensive overview of algorithms and parallel computing. It covers the basics of algorithms and parallel computing, as well as the latest advances in the field.
Provides a comprehensive overview of high performance computing in science and engineering. It covers the basics of high performance computing, as well as the latest advances in the field.
Provides a comprehensive introduction to parallel computing, covering architectures, programming paradigms, algorithms, and standards like MPI and OpenMP. It is an excellent resource for gaining a broad understanding of the field and is often used as a textbook in academic settings.
Offers a hands-on introduction to parallel programming, focusing on key frameworks like MPI, Pthreads, and OpenMP. It is suitable for students and professionals with a background in C programming and provides numerous programming exercises.
Provides an overview of parallel algorithms for machine learning, covering topics such as linear algebra, optimization, and deep learning. It is written by a team of experts in the field and is suitable for both researchers and practitioners.
Provides a comprehensive overview of parallel computing, covering topics such as parallel architectures, algorithms, and applications. It is written by an expert in the field and is suitable for both undergraduate and graduate students.
Provides an introduction to high-performance computing for computational science, covering topics such as parallel programming, performance optimization, and scientific computing libraries. It is written by a team of experts in the field and is suitable for researchers and practitioners.
Provides a comprehensive overview of parallelism in OpenMP, covering topics such as parallel programming models, algorithms, and performance optimization. It is written by an expert in the field and is suitable for both programmers and researchers.
Provides a practical guide to parallel computing, covering topics such as parallel programming models, algorithms, and performance optimization. It is written by a team of experts in the field and is suitable for both programmers and researchers.
Provides a comprehensive overview of parallel computing for scientific and engineering applications, covering topics such as parallel programming models, algorithms, and performance optimization. It is written by a team of experts in the field and is suitable for both researchers and practitioners.
A hands-on introduction specifically focused on the Message-Passing Interface (MPI) standard, widely used in parallel systems. is valuable for those looking to program distributed-memory systems and includes many examples in C and Fortran 77.
Practical guide to using OpenMP for shared-memory parallel programming. It covers the essential concepts and directives of OpenMP, making it a useful resource for implementing parallel programs on multi-core processors. It serves as a good reference for students and practitioners.
Focuses on parallel programming for GPUs using CUDA. It provides a hands-on approach with detailed examples and case studies, making it highly relevant for those interested in accelerating applications on many-core architectures.
Introduces a pattern language for parallel programming, offering proven solutions to common challenges. It uses OpenMP, MPI, and Java to illustrate these patterns, providing a valuable perspective on structuring parallel code.
Focuses on parallel programming patterns and is an excellent resource for understanding how to design efficient parallel algorithms. It is well-regarded for its approach to making parallel programming more accessible through patterns.
Provides a comprehensive introduction to high-performance computing (HPC) for scientists and engineers. It covers the basics of HPC, including parallel programming, load balancing, and performance optimization. The book also discusses the use of HPC in a variety of scientific and engineering applications.

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