Kassel, University, Diss., 2007.

The diploma thesis with the subject ¨Parallel Genetic Algorithms with Applications¨ deals with the parallelization of Genetic Algorithms for the creation of efficient optimization methods especially for simulation based application problems. First, an introduction to Genetic Algorithms and an overview of possible parallelization approaches as well as already published results of research are given. This is followed by a detailed explanation of the conception and realization of own Parallel Genetic Algorithms. The paper is rounded off by an particularized description of the results of extensive test runs on the Chemnitzer Linux-Cluster (CLiC). / Die Diplomarbeit zum Thema ¨Parallele Genetische Algorithmen mit Anwendungen¨ befasst sich mit der Parallelisierung Genetischer Algorithmen zur Erzeugung effizienter Optimierungsverfahren für insbesondere simulationsbasierte Anwendungsprobleme. Zunächst werden eine Einführung in Genetische Algorithmen sowie ein Überblick über mögliche Parallelisierungsansätze und bereits veröffentlichte Forschungsergebnisse gegeben. Dem schließt sich eine detaillierte Erläuterung der Konzeption und Umsetzung eigener Paralleler Genetischer Algorithmen an. Abgerundet wird die Arbeit durch eine ausführliche Darstellung der Ergebnisse umfangreicher Testläufe auf dem Chemnitzer Linux-Cluster (CLiC).

The purpose of this paper is to supply a summary of library subroutines and functions for parallel MIMD computers. The subroutines have been developed and continously extended at the University of Chemnitz since the end of the eighties. In detail, they are concerned with vector operations, inter-processor communication and simple graphic output to workstations. One of the most valuable features is the machine-independence of the communication subroutines proposed in this paper for a hypercube topology of the parallel processors (excepting a kernel of only two primitive system-dependend operations). They were implemented and tested for different hardware and operating systems including PARIX for transputers and PowerPC, nCube, PVM, MPI. The vector subroutines are optimized by the use of C language and unrolled loops (BLAS1-like). Hardware-optimized BLAS1 routines may be integrated. The paper includes hints for programmers how to use the libraries with both Fortran and C programs.

This paper deals with the visualization of numerical results as a very convenient method to understand and evaluate a solution which has been calculated as a set of millions of numerical values. One of the central research fields of the Chemnitz SFB 393 is the analysis of parallel numerical algorithms for large systems of linear equations arising from differential equations (e.g. in solid and fluid mechanics). Solving large problems on massively parallel computers makes it more and more impossible to store numerical data from the distributed memory of the parallel computer to the disk for later postprocessing. However, the developer of algorithms is interested in an on-line response of his algorithms. Both visual and numerical response of the running program may be evaluated by the user for a decision how to switch or adjust interactively certain parameters that may influence the solution process. The paper gives a survey of current programmer and user interfaces that are used in our various 2D and 3D parallel finite element programs for the visualization of the solution.

Chemnitz, Techn. Univ., Diplomarb., 2002.

The characteristics of irregular algorithms make a parallel implementation difficult, especially for PC clusters or clusters of SMPs. These characteristics may include an unpredictable access behavior to dynamically changing data structures or strong irregular coupling of computations. Problems are an unknown load distribution and expensive irregular communication patterns for data accesses and redistributions. Thus the parallel implementation of irregular algorithms on distributed memory machines and clusters requires a special organizational mechanism for a dynamic load balance while keeping the communication and administration overhead low. We propose task pool teams for implementing irregular algorithms on clusters of PCs or SMPs. A task pool team combines multithreaded programming using task pools on single nodes with explicit message passing between different nodes. The dynamic load balance mechanism of task pools is generalized to a dynamic load balance scheme for all distributed nodes. We have implemented and compared several versions for task pool teams. As application example, we use the hierarchical radiosity algorithm, which is based on dynamically growing quadtree data structures annotated by varying interaction lists expressing the irregular coupling between the quadtrees. Experiments are performed on a PC cluster and a cluster of SMPs.

Zugl.: Hamburg, Univ., Diss., 2008

The purpose of this paper is to supply a summary of library subroutines and functions for parallel MIMD computers. The subroutines have been developed and continously extended at the University of Chemnitz since the end of the eighties. In detail, they are concerned with vector operations, inter-processor communication and simple graphic output to workstations. One of the most valuable features is the machine-independence of the communication subroutines proposed in this paper for a hypercube topology of the parallel processors (excepting a kernel of only two primitive system-dependend operations). They were implemented and tested for different hardware and operating systems including PARIX for transputers and PowerPC, nCube, PVM, MPI. The vector subroutines are optimized by the use of C language and unrolled loops (BLAS1-like). Hardware-optimized BLAS1 routines may be integrated. The paper includes hints for programmers how to use the libraries with both Fortran and C programs.

The diploma thesis with the subject ¨Parallel Genetic Algorithms with Applications¨ deals with the parallelization of Genetic Algorithms for the creation of efficient optimization methods especially for simulation based application problems. First, an introduction to Genetic Algorithms and an overview of possible parallelization approaches as well as already published results of research are given. This is followed by a detailed explanation of the conception and realization of own Parallel Genetic Algorithms. The paper is rounded off by an particularized description of the results of extensive test runs on the Chemnitzer Linux-Cluster (CLiC). / Die Diplomarbeit zum Thema ¨Parallele Genetische Algorithmen mit Anwendungen¨ befasst sich mit der Parallelisierung Genetischer Algorithmen zur Erzeugung effizienter Optimierungsverfahren für insbesondere simulationsbasierte Anwendungsprobleme. Zunächst werden eine Einführung in Genetische Algorithmen sowie ein Überblick über mögliche Parallelisierungsansätze und bereits veröffentlichte Forschungsergebnisse gegeben. Dem schließt sich eine detaillierte Erläuterung der Konzeption und Umsetzung eigener Paralleler Genetischer Algorithmen an. Abgerundet wird die Arbeit durch eine ausführliche Darstellung der Ergebnisse umfangreicher Testläufe auf dem Chemnitzer Linux-Cluster (CLiC).

This paper deals with the visualization of numerical results as a very convenient method to understand and evaluate a solution which has been calculated as a set of millions of numerical values. One of the central research fields of the Chemnitz SFB 393 is the analysis of parallel numerical algorithms for large systems of linear equations arising from differential equations (e.g. in solid and fluid mechanics). Solving large problems on massively parallel computers makes it more and more impossible to store numerical data from the distributed memory of the parallel computer to the disk for later postprocessing. However, the developer of algorithms is interested in an on-line response of his algorithms. Both visual and numerical response of the running program may be evaluated by the user for a decision how to switch or adjust interactively certain parameters that may influence the solution process. The paper gives a survey of current programmer and user interfaces that are used in our various 2D and 3D parallel finite element programs for the visualization of the solution.