High Performance Scientific Computing with C 1.1: The Course Overview

Benjamin Keller, an astrophysicist and Humboldt Fellow, introduces a course on using computers to solve mathematical problems in science and engineering. The course covers numerical methods, code optimization, and leveraging modern hardware for performance. Students will learn to write fast code, optimize algorithms, and use parallel computing. Prerequisites include basic math, familiarity with C or similar languages, and command line experience.

What is the primary focus of the course introduced by Benjamin Keller?

• Astrophysics and galaxy formation

• Numerical methods and scientific computing

• Theoretical physics and cosmology

• Basic programming in Python

Which of the following is NOT mentioned as a numerical method covered in the course?

• Matrix factorization

• Differential equations

• Monte Carlo methods

• Numerical integration

What is emphasized when optimizing code for performance in the course?

• Focusing solely on algorithm complexity

• Writing code in multiple languages

• Understanding modern hardware design

• Using only high-level programming languages

Which hardware architecture is NOT discussed in the course?

• Distributed supercomputer clusters

• Parallel multicore machines

• GPU acceleration

• Quantum computers

What programming language familiarity is required for the course?

• C or C-like languages

• JavaScript

• Swift

• Ruby