May 1, 2024
3 minute read
High-Performance Computing (HPC) is the use of supercomputers and parallel processing to solve complex computational problems. HPC is used in a wide variety of fields, including scientific research, engineering, and financial modeling.
What is HPC Used For?
HPC is used to solve problems that are too complex or time-consuming to be solved on a single computer. Some of the most common applications of HPC include:
- Scientific research: HPC is used to simulate complex physical systems, such as the weather or the human body.
- Engineering: HPC is used to design and test new products, such as airplanes and cars.
- Financial modeling: HPC is used to develop and test financial models, such as those used to predict stock prices.
Benefits of Learning HPC
There are many benefits to learning HPC, including:
- Increased job opportunities: HPC is a growing field, and there is a high demand for qualified HPC professionals.
- Higher salaries: HPC professionals earn higher salaries than the average computer scientist.
- Greater job satisfaction: HPC professionals often work on challenging and rewarding projects.
How to Learn HPC
There are many ways to learn HPC, including:
ut2bke|
Find a path to becoming a HPC. Learn more at:
OpenCourser.com/topic/ut2bke/hp
Reading list
We've selected ten 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
HPC.
Provides a comprehensive overview of supercomputing, from the basics to advanced topics such as exascale computing and quantum computing. It is suitable for both beginners and experienced HPC users.
Covers the fundamental principles of parallel programming for high-performance computing, including message passing, shared memory, and threading. It is suitable for both beginners and experienced HPC users.
Provides a comprehensive overview of high-performance computing, including topics such as parallel programming, performance optimization, and applications in science and engineering.
Covers the fundamental principles of high-performance programming, including vectorization, parallelization, and caching. It is suitable for both beginners and experienced HPC users.
Provides a comprehensive overview of high-performance scientific computing, including topics such as parallel programming, performance optimization, and numerical methods. It is suitable for both beginners and experienced HPC users.
Provides a broad overview of the architectures and systems used in high-performance computing, including supercomputers, clusters, and grids. It is suitable for both beginners and experienced HPC users.
Provides a gentle introduction to high-performance computing for scientists and engineers. It covers the basics of parallel programming, performance optimization, and scientific computing.
Covers the fundamental principles of parallel computing, including message passing, shared memory, and threading. It includes a wide range of applications in science and engineering.
Covers the techniques for optimizing the performance of numerical software written in C++. It includes topics such as data structures, algorithms, and parallel programming.
Provides a comprehensive overview of computer architecture, from the basics to advanced topics such as superscalar processors, multi-core processors, and GPUs. It is suitable for both beginners and experienced HPC users.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/ut2bke/hp
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