International Conference on Computational Science 

About: International Conference on Computational Science is an academic conference. The conference publishes majorly in the area(s): Grid & Artificial neural network. Over the lifetime, 6713 publications have been published by the conference receiving 43581 citations.

hypre: A Library of High Performance Preconditioners

Abstract: hypre is a software library for the solution of large, sparse linear systems on massively parallel computers. Its emphasis is on modern powerful and scalable preconditioners. hypre provides various conceptual interfaces to enable application users to access the library in the way they naturally think about their problems. This paper presents the conceptual interfaces in hypre. An overview of the preconditioners that are available in hypre is given, including some numerical results that show the efficiency of the library.

OSIRIS: A Three-Dimensional, Fully Relativistic Particle in Cell Code for Modeling Plasma Based Accelerators

Abstract: We describe OSIRIS, a three-dimensional, relativistic, massively parallel, object oriented particle-in-cell code for modeling plasma based accelerators. Developed in Fortran 90, the code runs on multiple platforms (Cray T3E, IBM SP, Mac clusters) and can be easily ported to new ones. Details on the code's capabilities are given. We discuss the object-oriented design of the code, the encapsulation of system dependent code and the parallelization of the algorithms involved. We also discuss the implementation of communications as a boundary condition problem and other key characteristics of the code, such as the moving window, open-space and thermal bath boundaries, arbitrary domain decomposition, 2D (cartesian and cylindric) and 3D simulation modes, electron sub-cycling, energy conservation and particle and field diagnostics. Finally results from three-dimensional simulations of particle and laser wakefield accelerators are presented, in connection with the data analysis and visualization infrastructure developed to post-process the scalar and vector results from PIC simulations.

Dynamic Data Driven Applications Systems: A New Paradigm for Application Simulations and Measurements

Abstract: Dynamic Data Driven Application Systems (DDDAS) entails the ability to incorporate additional data into an executing application – these data can be archival or collected on-line; and in reverse, the ability of applications to dynamically steer the measurement process. The paradigm offers the promise of improving modeling methods, and augmenting the analysis and prediction capabilities of application simulations and the effectiveness of measurement systems. This presents the potential to transform the way science and engineering are done, and induce a major impact in the way many functions in our society are conducted, such as manufacturing, commerce, hazard management, medicine. Enabling this synergistic feedback and control-loop between application simulations and measurements requires novel application modeling approaches and frameworks, algorithms tolerant to perturbations from dynamic data injection and steering, and systems software to support the dynamic environments of concern here. Recent advances in complex applications, the advent of grid computing and of sensor systems, are some of the technologies that make it timely to embark in developing DDDAS capabilities. Research and development of such technologies requires synergistic multidisciplinary collaboration in the applications, algorithms, software systems, and measurements systems areas, and involving researchers in basic sciences, engineering, and computer sciences. The rest of the papers in the proceedings of this workshop provide examples of ongoing research developing DDDAS technologies within the context of specific and important application areas.

Evaluating the Performance of Skeleton-Based High Level Parallel Programs

Abstract: We show in this paper how to evaluate the performance of skeleton-based high level parallel programs. Since many applications fol- low some commonly used algorithmic skeletons, we identify such skele- tons and model them with process algebra in order to get relevant in- formation about the performance of the application, and be able to take some "good" scheduling decisions. This concept is illustrated through the case study of the Pipeline skeleton, and a tool which generates auto- matically a set of models and solves them is presented. Some numerical results are provided, proving the efficiency of this approach.

Solving Unsymmetric Sparse Systems of Linear Equations with PARDISO

Abstract: Supernode pivoting for unsymmetric matrices coupled with supernode partitioning and asynchronous computation can achieve high gigaflop rates for parallel sparse LU factorization on shared memory parallel computers. The progress in weighted graph matching algorithms helps to extend these concepts further and prepermutation of rows is used to place large matrix entries on the diagonal. Supernode pivoting allows dynamical interchanges of columns and rows during the factorization process. The BLAS-3 level efficiency is retained. An enhanced left-right looking scheduling scheme is uneffected and results in good speedup on SMP machines without increasing the operation count. These algorithms have been integrated into the recent unsymmetric version of the PARDISO solver. Experiments demonstrate that a wide set of unsymmetric linear systems can be solved and high performance is consistently achieved for large sparse unsymmetric matrices from real world applications.