Chile, Jan. 2015
Prof.Dr DI Gundolf Haase, University of Graz, Institute for Mathematics and Scientific Computing
Pool computers:
Graz: 143.50.47.166 to server 143.50.47.128
.
This tutorial introduces into the field of High Performance Computing (HPC) in mathematical areas and engineering sciences with special focus on MPI, OpenMP, OpenACC and CUDA. The students will acquire specialties of recent and future hardware concepts as well as on supporting software standards. The course work will be organized such that all course topics will be implemented on the appropriate hardware ranging from a single CPU via multiple CPUs to clusters of CPU s and GPUs. The students will be able to adapt research specific code such that they can take advantage of available computer resources. The three main goals of the course consist of
I.Knowledge of the students on algorithms and data structures for HPC and active use of this knowledge.
II.The students get in touch with HPC related concepts and architectures, and the students are able to adopt new developments in this area onto the problem under consideration.
III.Standard compiler and software support for parallel computer architectures is known and used by the students for solving mathematical problems by means of HPC hardware.
IV.The students are able to write/adapt parallel programs on various parallel platforms.
1.Basic knowledge in numerical linear algebra
2.Programming skills in C and/or C++
3.English language skills.
1 
Introduction into recent processor development 

2 
Practical work with sequential HPCprogramming (ex1) 


Seminar talk on "HPC and Mathematics in Applications" 

3 
Classification of parallel programming; Shared ressources 

4 
Introduction OpenMP (with practical work) 

5 
Practical work with OpenMP (ex2) 

6 
Introduction into MPI 

7 

Practical work with MPI (ex3) 
8 
Practical work with MPI and/or OpenMP 



9 
Continuation of Practical work with MPI and/or OpenMP; Comparison of the results 

10 
Introduction into GPU programming: hardware and software 

11 
First steps with OpenACC 

12 
Practical work with OpenACC 

13 
Performance tools available in CUDA and PGIOpenACC 

14 
Practical work with GPU code and performance tools 

15 
MultipleGPU programming 

16 
Practical work in mixing MPI and GPU programming 