An Algorithm for Dynamic Load Balancing of Synchronous Monte Carlo Simulations on Multiprocessor Systems

We describe an algorithm for dynamic load balancing of geometrically parallelized synchronous Monte Carlo simulations of physical models. This algorithm is designed for a (heterogeneous) multiprocessor system of the MIMD type with distributed memory. The algorithm is based on a dynamic partitioning of the domain of the algorithm, taking into account the actual processor resources of the various processors of the multiprocessor system.Comment: 12 pages, uuencoded figures included, 75.93.0

An efficient parallelization of a real scientific application

Bibliography: leaves 137-145.In the past decade the cost of computing has come down considerably making high-powered computing more easily affordable. As a result many institutions and organisations now have networks of high-powered workstations. Such networks provide a large, generally untapped, source of computing power which can be used for running large scientific applications which previously could only be run on supercomputers. This dissertation shows that a substantial improvement in performance can be achieved by the parallelization of a real scientific application for a heterogeneous network of Sun and Silicon Graphics workstations connected by an Ethernet network, but that this is affected by a number of factors. These factors include communication delays, load balancing, and the number of slaves used. This dissertation shows that performance can be improved by sending more, shorter messages, and by overlapping communication with computation. Part of this thesis concerns the difficulties involved in the evaluation of parallel performance on a heterogeneous network. This dissertation shows that conventional methods such as speedup and efficiency are not appropriate for evaluating the performance of a heterogeneous system, and that linear speed gives a much more representative indication of the actual performance achieved. We also proposed new concepts of perfect linear speed and linear efficiency, which help to evaluate the improvement in parallel performance on a heterogeneous system

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

Parallelization of the two-dimensional Ising Model on a Cluster of IBM RISC System/6000 Workstations

Using the PVM programming environment for parallel applications, we have parallelized a simulation of the two--dimensional Ising Model on a cluster of IBM RISC System/6000 1 workstations connected by a Token Ring (16Mb/sec) and by Serial Optical Channels (220 Mb/sec) via a NSC 2 DX Router. The parallelization is done by dividing the lattice into sublattices, each sublattice being associated with one workstation. On each sublattice, a Metropolis algorithm using Multispin Coding techniques is used to generate new configurations. We provide numerical results concerning the number of spin updates per second, speedups, and efficiencies for various numbers of processors and lattice sizes. Keywords. Statistical Physics; Ising Model; Workstation Cluster; Geometric Parallelization. 1 Introduction The goal of Theoretical Statistical Physics is a mathematical description of thermodynamic properties (e.g. of magnetism or phase transitions) of macroscopic bodies, commercing with a description ..

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

Proceedings of the International Workshop "Innovation Information Technologies: Theory and Practice": Dresden, Germany, September 06-10.2010

This International Workshop is a high quality seminar providing a forum for the exchange of scientific achievements between research communities of different universities and research institutes in the area of innovation information technologies. It is a continuation of the Russian-German Workshops that have been organized by the universities in Dresden, Karlsruhe and Ufa before. The workshop was arranged in 9 sessions covering the major topics: Modern Trends in Information Technology, Knowledge Based Systems and Semantic Modelling, Software Technology and High Performance Computing, Geo-Information Systems and Virtual Reality, System and Process Engineering, Process Control and Management and Corporate Information Systems