A resource management system can effectively shorten the runtime of batch jobs in a network of workstations (NOW). This is achieved with load balancing mechanisms to distribute the load equally among the hosts. To avoid conflicts between interactive users and batch jobs, a resource management system must be able to migrate batch jobs from an interactive host to an idle host. Common resource management systems offer process migration only for sequential jobs but not for parallel jobs. Within the SEMPA project a resource management system with batch queuing functionalities including checkpointing and migration is designed and implemented. We focus on PVM applications because PVM offers dynamic task management and an interface to resource management systems 1 . 1 Introduction Parallel scientific computing applications, e.g. in computational fluid dynamics, require a large amount of CPU time and memory. Therefore, they are often run on massively parallel systems. However, networks of wo...

This paper deals with the parallel implementation of reconstruction algorithms for functional imaging on a network of workstations (NOW). Algorithms which provide the best image quality are not used in clinical routine, because they have a runtime of up to 60 hours with real clinical data sets of several hundred megabytes. After giving an overview of currently used image reconstruction algorithms, we describe a general parallel implementation of these algorithms with almost linear speedup and high efficiency which cuts down the runtime to a feasible limit. The high load which is caused by the parallel application conflicts with the predominantly interactive usage of clinical workstations, therefore we address load balancing with an application oriented, adaptive mechanism in order to preserve the ownership of workstations. Furthermore we explain how the integration of MATLAB and IDL based applications with a conventional distributed queuing system (DQS) can be achieved and why this sig...

. This paper deals with currently used algorithms for the reconstruction of functional images which run up to 60 hours or more on a single workstation and deal with hundreds of megabyte of data. A parallel implementation with high efficiency and almost linear speedup of a sophisticated iterative algorithm is given and its applicability to other reconstruction methods is shown. Whereas running this application on a high performance parallel computer is straightforward, there are more issues under production conditions as they are enforced by daily routine in a clinic. We adress the topic of fault tolerant parallelizing and batch queuing of programs which are typically written in a high level language like IDL or MATLAB and show how load balancing can preserve the ownership of workstations in a network of workstations (NOW) which is used for distributed computing during office hours. keywords: functional imaging, parallel image reconstruction, load balancing, batch queing, network of wo...

ffl ffl -- 1 ffl ffl ffl ffl ffl 2 ffl ffl ffl ffl ffl 3 ffl ffl ffl ffl ffl 4 ffl ffl ffl ffl ffl 5 ffl ffl ffl 6 ffl ffl ffl ffl ffl 7 8 ffl 9 ffl ?? August 28, 1998 4 August 28, 1998 CONTENTS 5 Contents I Project Overview 15 1 Objectives 17 1.1 Software Engineering Methods for Parallel Applications in Scientific Computing 17 1.1.1 Efficient Parallelization of large scale Software Systems in Scientific Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1.1.2 New Programming Paradigms . . . . . . . . . . . . . . . . . . . . . . . 19 1.2 Parallelization of TfC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.3 Resource Management in Networks of Workstations . . . . . . . . . . . . . . . . 20 1.4 Partners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 1.4.1 Lehrstuhl fur Rechnerarchitektur und Rechnerorganisation 1 , Technische Universitat Munchen 2 (LRR-TUM) . . . . . . . . . . . . . . . . . . . . 21 1.4.2 AEA Technology GmbH 3 . . . . . . . . . . . . . . . . . . . . . . . . . 25 1.4.3 Institut fur Computer-Anwendungen, Universitat Stuttgart (ICA III) 4 . . 25 1.4.4 GENIAS Software GmbH 5 . . . . . . . . . . . . . . . . . . . . . . . . . 26 2 State of the Art and Related Work 29 2.1 Parallelization of Scientific Computing Applications . . . . . . . . . . . . . . . 29 2.2 Software-Engineering Methods for Parallel and Distributed Systems . . . . . . . 29 2.2.1 Software Process Models . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.2.2 Programming Environments . . . . . . . . . . . . . . . . . . . . . . . . 34 2.2.3 Tools for Parallel and Distributed Programming . . . . . . . . . . . . . . 36 2.3 Resource Management in Workstation Clusters . . . . . . . . . ...

The goal of a parallel program is to reduce the execution time, compared to the fastest sequential program solving the same problem. Parallel programming is growing due to the widespread use of network of workstations (NOWs) or powerful PCs in high-performance computing. Because the hardware components are all commodity devices, NOWs are much more cost-effective than custom machines with similar technology. In this environment, the typical programming model used has been message-passing and the MPI library has become the standard in the distributed-memory computing model. On the other hand, visual programming environments try to simply the task of developing applications. They provide programmers with several standard components for creating programs. Delphi constitutes one of the most popular visual programming environments nowadays in the Windows market place. In this paper, we present MPI--Delphi, an implementation of MPI for writing parallel applications using Delphi visual programming environment. We show how MPI--Delphi has been developed, and how it makes possible to manage a cluster of homogeneous/heterogeneous PCs. Two examples of use of MPI--Delphi in a heterogeneous cluster of workstations with a mixture of Windows and Linux operating systems are also included. The MPI--Delphi interface is suitable for some specific kinds of problems, such as monitoring parallel programs of long execution time, or computationally intensive graphical simulations. In addition, MPI--Delphi has proven to be a good tool for research, as the development of new algorithms can be carried out quickly and, therefore, time spent on the debugging of such algorithms is reduced. Finally, we conclude by explaining some of the tasks we think MPI--Delphi is suitable for. 2002 Elsevier Science...