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Previous COMPUTE courses

Autumn 2016

Interdisciplinary studies in computational research (study period I-II, 2-4 ECTS)

Course description and schedule.

Course responsible: and

Spring 2016

Interdisciplinary studies in computational research (study period I-II, 2-4 ECTS)

Course description and schedule.

Course responsible: and

Autumn 2015

Introduction to R (study period II, 1.5 ECTS)
This course provides an introduction to the programming language R. The first part of the course, giving 1.5 ECTS credits, will take place in week 3 of 2016 (18-22 January). It will be taught by Eric Feigelson, professor at Penn State and author of the book "Modern Statistical Methods for Astronomy with R Applications". The course is intended for PhD students from any area of physics, although some specific examples will be taken from astronomy research.

An optional second part of the course offers individual consultation on using R in specific research problems, potentially for additional credit.

The course consists of the following modules:

No prior knowledge of R is required to take the course.

Spring 2015

Domain decomposition methods (study period II, 7.5 ECTS)
Domain Decomposition is an important concept in the parallelization of numerical methods for partial differential equations (PDEs). Thereby, the spatial domain is decomposed into several parts, which are assigned to different processes, which then communicate over the boundaries of these domains. Naturally the question arises what happens if we modify the data or reduce the amount of communication. For large classes of PDEs, practical answers can be found by mathematical analysis, which turns out to be relevant to other multiphysics problems such as fluid structure interaction as well.

The course will cover the basic theory of domain decomposition methods for model problems like the Poisson and the Heat Equation with finite difference discretizations and then look at the practical issues of implementing these in a Message Passing Interface (MPI) framework.

Course responsible:

Autumn 2014

Monte Carlo and molecular dynamics tools (study period II, 7.5 ECTS)
The course gives a basic introduction to techniques for simulating complex systems and processes, typically with many coupled degrees of freedom. The main aim of the course is to introduce the students to applications of these techniques. To this end, the course contains five one-week projects in different areas (astronomy, biophysics, elementary particle physics, medical radiation physics and physical/theoretical chemistry).

Teachers: Melvyn B. Davies, Anders Irbäck, Per Linse, Michael Ljungberg and Leif Lönnblad/Torbjörn Sjöstrand

Course coordinator:

Spring 2014

Modelling and computer simulation of particles and nuclides passage through matter and magnetic field, with Geant4 as example (7-11 April 2014, 3 ECTS)
Geant4 is a toolkit for simulating the passage of particles through matter. It is the reference simulation engine in many areas. Geant4 covers all relevant physics processes, electromagnetic, hadronic, decay, optical, for long and short lived particles, for energy range spanning from tens of eV to TeV scale. The transport of low energy neutrons down to thermal energies is also be handled. The software can also simulate remnants of hadronic interactions, including atomic de-excitation and provides extension to low energies down to the DNA scale for biological modelling.
The course concerns the following topics: Introduction to simulation of elementary particles and nuclides passing through and interacting with matter; structure of a simulation program based on object-orientation; definition of realistic geometry including magnetic field; primary particles and interfaces to generators; electromagnetic and strong interaction physics processes; user interfaces; visualization; event biasing; simulation examples from subatomic physics, space science and medical applications.

More information can be found in the information sheet and here.

Course responsible: Torsten Åkesson

Teachers: Alberto Ribon (CERN), Sebastien Incerti (Bordeaux), Makoto Asai (Stanford), Marc Verderi (École Polytechnique)

Grid computing concepts and tools (study period II, 4.5 ECTS)
The course gives introduction into concepts of Grid computing, discussing aspects of authentication and authorisation, interfaces to computing services, distributed storage and data handling, resource characterization and discovery, information representation and monitoring, workload management and scheduling in a distributed environment. It will introduce concepts of Grid certificates and trust, Virtual Organisations, Grid jobs, meta-protocols, information systems, brokering and runtime environments. Existing tools and services will be introduced. The students will obtain Grid certificates and a temporary access to a Grid testbed for exercises. The course will be a combination of lectures and hands-on tutorials.

Teachers: Oxana Smirnova, Balazs Konya

Efficient programming of modern HPC architectures (study period I, 7.5 ECTS)
The course discusses programming techniques required to efficiently utilise high performance computing in a PhD-project in computational science and engineering. The course content includes developing modularised software in Fortran 95 and the scripting language Python. You will be taught how to conduct an object-oriented analysis of common problems in science and engineering. Parallel programming will be another focus point of the course. We will discuss shared memory and distributed memory programming. The course will introduce the application interfaces of OpenMP and MPI as well as the concepts behind these.

Teachers: Joachim Hein, Jonas Lindemann

Autumn 2013

Image analysis (study period II, 7.5 ECTS)
The main aim of the course is to give a basic introduction to theory and mathematical methods used in image analysis, to an extent that will allow image processing problems to be developed and evaluated. In addition the aim is to help the PhD student develop his or her ability in problem solving, both with and without a computer. Furthermore, the aim is to prepare the student for further studies in computer vision, multispectral image analysis, machine learning and statistical image analysis.

It is assumed that the students following the course selects a project before the course starts.

The course is divided into logical modules:

During the course, homework assignments are made on data specific to the students' individual projects with methods taught in the different modules. The course ends with project presentations.

Suggested reading: l Szeliski, R.: Computer Vision: Algorithms and Applications. Springer, 2010, ISBN: 9781848829343. It is possible to pass the course without owning the book, using material available through the course home page.

Teachers: Karl Åström, Katarina Sjögreen Gleisner, Michael Ljungberg, Mattias Ohlsson.

Course coordinator:

Spring 2013

Computational tools and recipes (study period I, 7.5 ECTS)
The aim of this course is to equip the participating PhD students with the tools needed to solve advanced numerical problems efficiently in their research. Topics will be taken from the book, "Numerical Recipes" by Press et al., and include sorting, root finding, interpolation and extrapolation, minimization and maximization of functions, fitting procedures and modeling of data, and Fast Fourier transforms. The course will be a mixture of lectures and hands-on exercise sessions given by experts from a number of departments.

Teachers: Ross Church, Eskil Hansen, David Hobbs, Per-Åke Malmqvist, Valera Veryazov

Course coordinator:

Autumn 2012

Monte Carlo and molecular dynamics tools (study period II)
The course will teach techniques for simulating complex systems and processes, typically with many coupled degrees of freedom. A wide spectrum of problems will be discussed, including examples from astronomy, biophysics, elementary particle physics, medical radiation physics and physical/theoretical chemistry.

Schedule (pdf-format).

Teachers: Melvyn B. Davies, Anders Irbäck, Michael Ljungberg, Torbjörn Sjöstrand, Erik Wernersson

Course coordinator:

Efficient programming of modern HPC architectures (study period I, 7.5 ECTS)
The course discusses programming techniques required to efficiently utilise high performance computing in a PhD-project in computational science and engineering. The course content includes developing modularised software in Fortran 95 and the scripting language Python. You will be taught how to conduct an object-oriented analysis of common problems in science and engineering. Parallel programming will be another focus point of the course. We will discuss shared memory and distributed memory programming. The course will introduce the application interfaces of OpenMP and MPI as well as the concepts behind these.

Please visit the course web-page for a schedule and further information.

Teachers: Joachim Hein, Jonas Lindemann

Course coordinator:

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This page was last modified on 2 December 2016.