Parallel and high-performance computing

Summary

This course provides insight into a broad variety of High Performance Computing (HPC) concepts and the majority of modern HPC architectures. Moreover, the student will learn to have a feeling about what architectures are suited for several types of algorithms and learn to program for them.

Content

Keywords

HPC, Parallelization, MPI, GPU

Learning Prerequisites

Required courses

• Analysis, bachelor level
• Numerical analysis for engineers
• Matrix algebra

Recommended courses

• Programming concepts in scientific computing

Learning Outcomes

By the end of the course, the student must be able to:

• Classify the types of HPC architecture
• Identify codes suited for parallelization
• Apply the most common parallelization techniques
• Implement algorithms in parallel
• Investigate the performance of parallel code
• Argue about the differences in performance between theory and practice
• Optimize the usage of hardware and software resources depending on the type of algorithm to parallelize

Transversal skills

• Set objectives and design an action plan to reach those objectives.
• Communicate effectively with professionals from other disciplines.
• Access and evaluate appropriate sources of information.
• Write a scientific or technical report.

Teaching methods

Lectures, exercises, project work

Expected student activities

Attendance to lectures, completing exercises, writing a project

Assessment methods

Graded exercises, final project, and oral defense of project

Supervision

Resources

Moodle Link

• https://go.epfl.ch/MATH-454