J.F. de Ronde

Load balancing by redundant decomposition and mapping

Abstract In this paper a new methodology for load balancing parallel processes on parallel systems is proposed. The problem of load balancing is considered to be an NP-hard optimization task. Taking static parallel finite element applications as a case study, the benefits and losses that follow from applying the methodology are studied. It is found that the proposed methodology can be especially useful for load balancing in asymmetric processor topologies, and therefore is of importance for work load balancing in workstation clusters.

Task Allocation by Parallel Evolutionary Computing

In this paper we will investigate the applicability of parallel evolutionary algorithms to the task allocation problem?a long standing problem in parallel computing. Three different evolutionary optimization strategies, genetic algorithms, simulated annealing, and steepest descent, are formulated in a parallel generic framework. In order to enhance the performance of the strategies, a number of adjustments that exploit problem specific knowledge is proposed. We adopt a parametric description of static parallel applications. As a consequence, a theoretical analysis of the task allocation solution space can be conducted with a method originating from computational biology. The prediction following from this analysis, i.e., simulated annealing performs optimally on the solution space, is supported by experimental results.

Simulation of gravitational wave detectors

A simulation program that provides insight into the vibrational properties of resonant mass gravitational radiation antennas is developed from scratch. The requirements that are set necessitate the use of an explicit finite element kernel. Since the computational complexity of this kernel requires significant computing power, it is tailored for execution on parallel computer systems. After validating the physical correctness of the program as well as the performance on distributed memory architectures, we present a number of “sample” simulation experiments to illustrate the simulation capabilities of the program. The development path of the code, consisting of problem definition, mathematical modeling, choosing an appropriate solution method, parallelization, physical validation, and performance validation, is argued to be typical for the design process of large-scale complex simulation codes.

The CAMAS workbench: computer aided migration of applications system

A simulation methodology for predicting the performance of sequential programs as well as data parallel (SPMD) programs on parallel and distributed platforms is presented. The proposed methodology comprises a parameterised description of the applications as well as the target machines. An integrated simulation procedure estimates the time complexity of the (SPMD) application given a well-defined hardware platform and predicts the execution behaviour. The methodology has been actualised in terms of a toolset currently under development at the University of Amsterdam.