PVM and MPI, two systems for programming clusters, are often compared. The comparisons usually start with the unspoken assumption that PVM and MPI represent different solutions to the same problem. In this paper we show that, in fact, the two systems often are solving different problems. In cases where the problems do match but the solutions chosen by PVM and MPI are different, we explain the reasons for the differences. Usually such differences can be traced to explicit differences in the goals of the two systems, their origins, or the relationship between their specifications and their implementations. For example, we show that the requirement for portability and performance across many platforms caused MPI to choose approaches different from those made by PVM, which is able to exploit the similarities of network-connected systems.

Parallel software packages which may be of use in scientific and engineering applications of the type carried out on the parallel computing facilities at EPCC and Daresbury Laboratory are surveyed. For each package, a brief description is given along with other useful information such as availability, contact addresses and systems supported. keywords: parallel computing, software packages, scientific applications. This report is available from http://www.dl.ac.uk/TCSC/HPCI/ c fl1996, Daresbury Laboratory. We do not accept any responsibility for loss or damage arising from the use of information contained in any of our reports or in any communication about our tests or investigations. ii CONTENTS iii Contents 1 Introduction 1 1.1 Criteria for inclusion : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1 1.2 Package areas : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 1 1.3 Individual entries : : : : : : : : : : : : : : : : : : : : : : : : : : : : : :...

We describe PIM (Parallel Iterative Methods), a collection of Fortran 77 routines to solve systems of linear equations on parallel computers using iterative methods. A number of iterative methods for symmetric and nonsymmetric systems are available, including Conjugate-Gradients (CG), Bi-Conjugate-Gradients (Bi-CG), ConjugateGradients squared (CGS), the stabilised version of Bi-Conjugate-Gradients (Bi-CGSTAB), the restarted stabilised version of Bi-Conjugate-Gradients (RBi-CGSTAB), generalised minimal residual (GMRES), generalised conjugate residual (GCR), normal equation solvers (CGNR and CGNE), quasi-minimal residual (QMR) with coupled two-term recurrences, transpose-free quasi-minimal residual (TFQMR) and Chebyshev acceleration. The PIM routines can be used with user-supplied preconditioners, and left-, right- or symmetric-preconditioning are supported. Several stopping criteria can be chosen by the user. In this user's guide we present a brief overview of the iterative methods and ...

This paper describes our experience in developing and maintaining multi-platform parallel / distributed scientific computing software. We gained this experience while developing the parallel computation environment for //ELLPACK

We describe PIM (Parallel Iterative Methods), a collection of Fortran 77 routines to solve systems of linear equations on parallel computers using iterative methods. A number of iterative methods for symmetric and nonsymmetric systems are available, including Conjugate-Gradients (CG), Bi-Conjugate-Gradients (Bi-CG), ConjugateGradients squared (CGS), the stabilised version of Bi-Conjugate-Gradients (Bi-CGSTAB), the restarted stabilised version of Bi-Conjugate-Gradients (RBi-CGSTAB), generalised minimal residual (GMRES), generalised conjugate residual (GCR), normal equation solvers (CGNR and CGNE), quasi-minimal residual (QMR), transpose-free quasi-minimal residual (TFQMR) and Chebyshev acceleration. The PIM routines can be used with user-supplied preconditioners, and left-, right- or symmetric-preconditioning are supported. Several stopping criteria can be chosen by the user. In this user's guide we present a brief overview of the iterative methods and algorithms available. The use of PIM is introduced via examples. We also present some results obtained with PIM concerning the selection of stopping criteria and parallel scalability. A reference manual can be found at the end of this report with specific details of the routines and parameters. Contents 1

PVM and MPI are often compared. These comparisons usually start with the unspoken assumption that PVM and MPI represent different solutions to the same problem. In this paper we show that, in fact, the two systems often are solving different problems. In cases where the problems do match but the solutions chosen by PVM and MPI are different, we explain the reasons for the differences. Usually such differences can be traced to explicit differences in the goals of the two systems, their origins, or the relationship between their specifications and their implementations. For example, we show that the requirement for portability and performance across many platforms caused MPI to chose different approaches than PVM, which is able to exploit the similarities of network-connected systems. This paper expands on earlier discussions; among the additions are parallel I/O, the safety of contexts, and a subtle performance issue in multiparty communications.

Abstract _ _ Numerous parallel programming tools have been developed so far for supporting parallel programs. This paper presents performance analysis of wide range of parallel programming simulation tools. This paper also compares the features of different tools. PVM and MPI are most widely used standards for parallel and distributed computing. MPI has better performance in high performance massively parallel processing (MMPs) computer systems to provide highly optimized and efficient implementations than PVM. In MMP, all of the processing elements are connected together to be one very large computer. This is in contrast to the distributed computing where massive numbers of separate computers, connected through a network, are used to solve a single large problem. PVM is most suitable in heterogeneous networks to gain optimal performance. One may favor the other tools depending on the need. With the help of our performance comparison one can choose which one would be the better for a particular application.