Pasqua D'Ambra

Integrating MPI-based numerical software into an advanced parallel computing environment

In this paper we present first experiences concerning the integration of MPI-based numerical software into an advanced programming environment for building parallel and distributed high-performance applications, which is under development in the context of Italian national research projects. Such a programming environment, named ASSIST, is based on a combination of the concepts of structured parallel programming and component-based programming. Some activities within the projects are devoted to the definition, implementation and testing of a methodology for the integration of a parallel numerical library into ASSIST. The goal is providing a set of efficient, accurate and reliable tools that can be easily used as building blocks for high-performance scientific applications. We focus on the integration of existing and widely used MPI-based numerical library modules. To this aim, we propose a general approach to embed MPI computations into the ASSIST basic programming unit. This approach has been tested using the MPICH implementation of MPI for networks of workstations. Some modifications have been applied to the MPICH process startup procedure, in order to make it compliant with the ASSIST environment. Results of experiments concerning the integration of routines from a well-known FFT package are discussed.

Adaptive AMG with coarsening based on compatible weighted matching

We introduce a new composite adaptive Algebraic Multigrid (composite

Extending PSBLAS to build parallel schwarz preconditioners

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SParC-LES

αAMG) method to solve systems of linear equations without a-priori knowledge or assumption on characteristics of near-null components of the AMG preconditioned problem referred to as algebraic smoothness. Our version of

Dynamic Load Balancing for High-Performance Simulations of Combustion in Engine Applications

\alpha

On the use of aggregation-based parallel multilevel preconditioners in the LES of wall-bounded turbulent flows

αAMG is a composite solver built through a bootstrap strategy aimed to obtain a desired convergence rate. The coarsening process employed to build each new solver component relies on a pairwise aggregation scheme based on weighted matching in a graph, successfully exploited for reordering algorithms in sparse direct methods to enhance diagonal dominance, and compatible relaxation. The proposed compatible matching process replaces the commonly used characterization of strength of connection in both the coarse space selection and in the interpolation scheme. The goal is to design a method leading to scalable AMG for a wide class of problems that go beyond the standard elliptic Partial Differential Equations (PDEs). In the present work, we introduce the method and demonstrate its potential when applied to symmetric positive definite linear systems arising from finite element discretization of highly anisotropic elliptic PDEs on structured and unstructured meshes. We also report on some preliminary tests for 2D and 3D elasticity problems as well as on problems from the University of Florida Sparse Matrix Collection.