Difference between revisions of "Masterpraktikum Scientific Computing - High Performance Computing - Winter 14"

From Sccswiki
Jump to navigation Jump to search
Line 45: Line 45:
= Slides =
= Slides =
* 10/13/14: [http://www5.in.tum.de/lehre/praktika/wissprak/WS14/hpc_lab.pdf Organization, Vectorization]
= Exercise Sheets =
= Exercise Sheets =

Revision as of 13:46, 13 October 2014

WS 14
Michael Bader
Alexander Breuer
Time and Place
Kickoff: Monday 10/13/14, 4PM in room MI 02.07.023
Masterpraktikum (Modul IN2106), Wahlpflichtpraktikum im Bereich theoretische Informatik (Modul IN8904)
Studierende im Masterstudiengang Informatik, Studierende der Informatik im Hauptstudium, Studierende der Techno-, Diplom- und Finanz-Mathematik

Language English

Semesterwochenstunden / ECTS Credits
10 Credits


  • Kickoff: Monday 10/13/14 in room MI 02.07.023
  • Info session: Mon, Jun 30, 15.00 in room MI 01.07.023
  • if you have questions in advance -> Alexander Breuer


High Performance Computing has become a critical success factor in research and industry. Even the computational power of commodity parts like desktop systems increased rapidly due to vectorization, EPIC, multi-core. This lab course uncovers the principles behind these buzzwords and highlights programming concepts, software design and coding styles which are mandatory to unleash the power of modern chips.

We focus on developing datastructers and algorithms with respect to their performance, for both single and parallel executions. We deep-dive into different platforms like CPUs, coprocessors and multi-node clusters. Furthermore, we cover several programming concepts like auto-vectorization, explicit vectorization, OpenMP and MPI. The platform differences are evaluated through detailed runtime experiments.


  • Performance-Optimization of sequential applications
    • Pipelining
      • Loop unrolling
      • Data dependencies
    • Caches
      • Cache blocking
    • Performance-Measurement
  • State-of-the-Art Hardware
    • CPUs
    • Memory Subsystems
    • Supercomputer
    • Xeon Phi coprocessor
  • Parallele Programming
    • OpenMP
    • MPI

The course is held in two parts. The first one introduces basics, concepts and programming models through theoretical and practical exercises. You work on these exercise in small teams. The second part is a real-life task: each team has to optimize a given scientific application with respect to the underlying hardware architecture.

C-Programming and Linux Know-How are helpful!


Exercise Sheets