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, Multi-threading, MPI, GPU

Learning Prerequisites

Required courses

• Analysis, bachelor level
• Numerical analysis for engineers
• Matrix algebra
• C/C++ programming

Important concepts to start the course

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
• Argue about the differences in performance between theory and practice
• Investigate the performance of parallel code
• 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.
• Write a scientific or technical report.
• Access and evaluate appropriate sources of information.

Teaching methods

Lectures, exercises, project work

Expected student activities

Attendance to lectures, completing exercises, writing a project

Assessment methods

Project(s) and oral exam.

Supervision

Resources

Moodle Link

• https://go.epfl.ch/MATH-454