We outline the design of a scientific visualization system based on a data card display invariant to provide information on the creation and processing of tasks in distributed task pool algorithms. Information aggregates over the tasks and processes are incorporated via non-classical histogram representations showing the distribution of the data within individual Bins and by user-controllable tree displays.

We describe a coarse grain parallel algorithm for multivariate adaptive integration using MPI. The algorithm is asynchronous in nature and allows for load balancing. Timing results show good speedups obtained on a network of workstations for a class of integrals from Bayesian statistics

Multivariate integration problems arising in the real world often lead to computationally intensive numerical solutions. If the singularities and/or peaks in the integrand are not known a priori, the use of adaptive methods is recommended. The efficiency of adaptive methods depends heavily on focusing on the sub-regions that contain singularities or peaks in the integrands. In this paper, we present techniques based on, evolutionary strategies that can be used to identify such sub-regions. Adaptive integration algorithms and evolutionary strategies can be parallelized easily and hence combining the parallel implementations of these result in efficient parallel adaptive integration algorithms.

We present strategies for the parallel computation of the integrals typically arising in finite element problems. In order to deal efficiently with difficulties in the integrands affecting the domain in areas of which the exact locations may be unknown, a parallel adaptive strategy is applied to the entire domain. The elements comprising the domain form the initial pool of subregions in the adaptive strategy and are refined further where needed, thereby allowing load sharing or a load balanced global priority queue within each participating process group.

We explore a coarse grain parallel algorithm for the solution of large sparse linear systems with symmetric positive definite matrix, based on a preconditioned conjugate gradient method.

. We analyze a class of adaptive integration algorithms onMIMD distributed memory systems. The integration region subdividedin the course of the adaptive process is the N-dimensional cube orsimplex. At the subdivision of a subregion, the error behaves accordingto a prescribed model. The model is supported by the asymptoticbehavior of the error for integrands which are continuously differentiableof a given order over all subregions with the possible exception ofone subregion containing a...

We describe an automatic routine to integrate a function over a collection of triangles where on each triangle the function is well behaved, or has singularities of certain types at one or more vertices or edges. The underlying algorithm is globally adaptive and incorporates the d-transformation for extrapolation. Results from performance profile testing indicate that the routine is superior to other published routines when the singularities are located along edges of the triangles.

We present results on applying Lanczos methods to find some of the eigenvalues of dense matrices whose size renders reduction to fill tridiagonal or hessenberg form undesirable. The use of distributed memory supercomputers is an ideal match for many problem in physics and other applications, where a system is modeled by building a large matrix, whose entries must be computed and whose few smallest eigenvalues provide the desired information. Our motivating application starts with 2000 by 2000 systems.

article Free Access Share on An Algorithm for Automatic Integration Over a Triangle Using Nonlinear Extrapolation Author: Ian Robinson Computer Science Department, La Trobe University, Bundoora, Victoria, 3083, Australia Computer Science Department, La Trobe University, Bundoora, Victoria, 3083, AustraliaView Profile Authors Info & Claims ACM Transactions on Mathematical SoftwareVolume 10Issue 1March 1984 pp 1–16https://doi.org/10.1145/356068.356069Online:01 January 1984Publication History 9citation953DownloadsMetricsTotal Citations9Total Downloads953Last 12 Months7Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF