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Implement Shai Avidan and Ariel Shamir's algorithm for image resizing using OpenMP and OpenCV.

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This is the latest in a 10 year series of tutorials about OpenMP. The approach is based on the latest research in learning theory and is based on an active learning program. The goal is to talk as little as possible and present much of the material through hands-on exercises. This is actually much harder to present than a traditional “lecture style” as it requires a lecturer how stays actively engaged with his or her learners … letting them get frustrated enough to entrench what they learn into long term memory but not so frustrated that the ability to learn degrades. The extra effort is worth it, though, since this well tested set of material have proven to be an extremely effective way to teach OpenMP. Note that the data environment exercise on MD is a bit too difficult for students during a tutorial. It is better offered as homework assignment.

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This hands-on module introduces OpenMP* 3.0 directives to parallelize common functions and loops. The first section of the module introduces the most common feature of OpenMP - work sharing for loops. The second section demonstrates how to exploit non-loop parallelism, including the new task constructs in OpenMP 3.0. The final section discusses the usage of synchronization methods, library functions, and environment variables. After successful completion of this module, the participant should be able to modify C/C++ code to achieve parallelism using the new OpenMP 3.0 features, available in various compilers: gnu*, Microsoft*, Intel, and others. Note-- This module employs the Intel compiler for labs and demos.

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Objectif : mise en pratique immédiate d\\\'OpenMP grâce à une approche par l\\\'exemple. Les nombreux shémas contenus dans ce cours, appuyés par une explication orale détaillée, montreront clairement les concepts inhérents à ce mode de parallélisation relativement efficace sur des machines multi-processeurs à mémoire partagée. Public concerné : toute personne souhaitant paralléliser une application pré-existante ou dans sa genèse pour une architecture multi-processeurs à mémoire partagée. Pré-requis : Fortran et Unix de base.
Durée : 2 jours. --- Assistance maximale : 18 personnes.

Plan

1. Introduction
2. Principes
3. Partage du travail
4. Synchronisation
5. Quelques pièges
6. Performances
7. Conclusion

Travaux pratiques : environ 50 % du temps total.

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These modules are designed for an instructor led course which introduces basic concepts towards writing parallel programs that utilize the parallel execution capability of a multi-core processor.

Topics include:

Introduction to threading
Win32 Threading API
Fork/Join Threading Model

OpenMP Part 1
Parallel Block
Parallel for
Scheduling
Scopes
Contents

OpenMP Part 2
Reduction
Parallel Sections
Parallel Tasks

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