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151-0107-20L 4 Credits BSC , DR , MSC D-ITET , D-MAVT , D-INFK , D-PHYS , D-MATH

High Performance Computing for Science and Engineering (HPCSE) I

Lecturers: Dr. Jens Honoré Walther, Dr. Sergio Miguel Martin
Examiners: Dr. Sergio Miguel Martin
VVZ CR n/a

Last Updated: 2026-02-05 16:02:03

Abstract

This course gives an introduction into algorithms and numerical methods for parallel computing on shared and distributed memory architectures. The algorithms and methods are supported with problems that appear frequently in science and engineering.

Objective

With manufacturing processes reaching its limits in terms of transistor density on today’s computing architectures, efficient utilization of computing resources must include parallel execution to maintain scaling. The use of computers in academia, industry and society is a fundamental tool for problem solving today while the “think parallel” mind-set of developers is still lagging behind. The aim of the course is to introduce the student to the fundamentals of parallel programming using shared and distributed memory programming models. The goal is on learning to apply these techniques with the help of examples frequently found in science and engineering and to deploy them on large scale high performance computing (HPC) architectures.

Content

1. Hardware and Architecture: Moore’s Law, Instruction set architectures (MIPS, RISC, CISC), Instruction pipelines, Caches, Flynn’s taxonomy, Vector instructions (for Intel x86) 2. Shared memory parallelism: Threads, Memory models, Cache coherency, Mutual exclusion, Uniform and Non-Uniform memory access, Open Multi-Processing (OpenMP) 3. Distributed memory parallelism: Message Passing Interface (MPI), Point-to-Point and collective communication, Blocking and non-blocking methods, Parallel file I/O, Hybrid programming models 4. Performance and parallel efficiency analysis: Performance analysis of algorithms, Roofline model, Amdahl’s Law, Strong and weak scaling analysis 5. Applications: HPC Math libraries, Linear Algebra and matrix/vector operations, Singular value decomposition, Neural Networks and linear autoencoders, Solving partial differential equations (PDEs) using grid-based and particle methods

Resources

Lecture Notes

https://www.cse-lab.ethz.ch/teaching/hpcse-i_hs22/Class notes, handouts

Literature

• An Introduction to Parallel Programming, P. Pacheco, Morgan Kaufmann • Introduction to High Performance Computing for Scientists and Engineers, G. Hager and G. Wellein, CRC Press • Computer Organization and Design, D.H. Patterson and J.L. Hennessy, Morgan Kaufmann • Vortex Methods, G.H. Cottet and P. Koumoutsakos, Cambridge University Press • Lecture notes

Learning Materials (Links)

General Information

Language
English
Levels
BSC , DR , MSC
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 180 minutes
Aids
You are allowed to bring a HANDWRITTEN summary of 4 A4 sheets, written on the front and back pages (8 pages total). Photocopies are not allowed.
Digital
The exam takes place on devices provided by ETH Zurich.
Computer based examination involving theoretical questions and coding problems. Parts of the lecture documents and other materials will be made available online during the examination.

Course Components

Type Title Time & Place Hours
lecture with exercise High Performance Computing for Science and Engineering (HPCSE) I
  • Wed 08:15-10:00 (HG E 3)
  • Wed 12:15-14:00 (HG E 3)
4 h weekly

Offered In