701-1270-00L  High Performance Computing for Weather and Climate

SemesterSpring Semester 2020
LecturersO. Fuhrer
Periodicityyearly recurring course
Language of instructionEnglish


701-1270-00 GHigh Performance Computing for Weather and Climate
Block course of one full week in Zurich
Starting in 2020, either June 1 – 5 or June 8 – 12 2020.
Hands-on exercises and work-project on supercomputer at CSCS
40s hrsO. Fuhrer

Catalogue data

AbstractState-of-the-art weather and climate simulations rely on large and complex software running on supercomputers. This course focuses on programming methods and tools for understanding, developing and optimizing the computational aspects of weather and climate models. Emphasis will be placed on the foundations of parallel computing, practical exercises and emerging trends such as heterogeneous comput
ObjectiveAfter attending this course, students will be able to:
- understand a broad variety of high performance computing concepts relevant for weather and climate simulations
- work with weather and climate simulation codes that run on large supercomputers
ContentHPC Overview:
- Why does weather and climate require HPC?
- Today's HPC: Beowulf-style clusters, massively parallel architectures, hybrid computing, accelerators
- Scaling / Parallel efficiency
- Algorithmic motifs in weather and climate

Writing HPC code:
- Data locality and single node efficiency
- Shared memory parallelism with OpenMP
- Distributed memory parallelism with MPI
- GPU computing
- High-level programming and domain-specific languages
Literature- Introduction to High Performance Computing for Scientists and Engineers, G. Hager and G. Wellein, CRC Press, 2011
- Computer Organization and Design, D.H. Patterson and J.L. Hennessy
- Parallel Computing, A. Grama, A. Gupta, G. Karypis, V. Kumar (https://www-users.cs.umn.edu/~karypis/parbook/)
- Parallel Programming in MPI and OpenMP, V. Eijkhout (http://pages.tacc.utexas.edu/~eijkhout/pcse/html/index.html)
Prerequisites / Notice- fundamentals of numerical analysis and atmospheric modeling
- basic experience in a programming language (C/C++, Fortran, Python, …)
- experience using command line interfaces in *nix environments (e.g., Unix, Linux)

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits3 credits
ExaminersO. Fuhrer
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.
Additional information on mode of examinationWork project to hand-in up to 1-2 months after block course.

Learning materials

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Offered in

Data Science MasterInterdisciplinary ElectivesWInformation
Computational Science and Engineering BachelorAdditional Electives from the Fields of Specialization (CSE Master)WInformation
Computational Science and Engineering MasterPhysics of the AtmosphereWInformation
Environmental Sciences MasterAdditional Elective CoursesWInformation