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High Performance Scientific Computing with C

Video Description

Use algorithm design, hardware features, and parallelism to build fast, accurate, and efficient scientific code

About This Video

  • Practical examples to understand scientific and mathematical coding in C
  • Get basic, general programming knowledge of the tools and techniques needed to write scientific codes from scratch
  • Explore key concepts of how numerical algorithms work and their optimization using parallel architectures, distributed systems, and GPGPU accelerators

In Detail

In this course, you’ll learn to develop scientific and numerical programs that solve problems. It’s ideal for scientists, engineers, and programmers who need to model mathematical or physical systems. You’ll get a core toolkit of algorithms that can be used in a wide variety of applications, using the low-level capabilities of the C programming language.

The close-to-the-metal approach means you’ll learn to optimize our programs to get the absolute best performance that our hardware can provide. You’ll see how the design of algorithms affects their performance and accuracy, the tools that can be used to optimize your code and develop your intuition about numerical problems. Finally, you’ll examine the growing array of parallel solutions that enable you to take advantage of multi-core CPUs, distributed compute clusters and GPU accelerators.

By the end of this course, you’ll know how to write fast, accurate code that can run on many different platforms and solve many different scientific problems.

The code bundle for this video course is available at-https://github.com/PacktPublishing/High-Performance-Scientific-Computing-With-C