1,720,986 research outputs found
A Scalable Space-Time Domain Decomposition Approach for Solving Large Scale Nonlinear Regularized Inverse Ill Posed Problems in 4D Variational Data Assimilation
Full text linkWe address the development of innovative algorithms designed to solve the strong-constraint Four Dimensional Variational Data Assimilation (4DVar DA) problems in large scale applications. We present a space-time decomposition approach which employs the whole domain decomposition, i.e. both along the spacial and temporal direction in the overlapping case, and the partitioning of both the solution and the operator. Starting from the global functional defined on the entire domain, we get to a sort of regularized local functionals on the set of sub domains providing the order reduction of both the predictive and the Data Assimilation models. The algorithm convergence is developed. Performance in terms of reduction of time complexity and algorithmic scalability is discussed on the Shallow Water Equations on the sphere. The number of state variables in the model, the number of observations in an assimilation cycle, as well as numerical parameters as the discretization step in time and in space domain are defined on the basis of discretization grid used by data available at repository Ocean Synthesis/Reanalysis Directory of Hamburg University
About the granularity portability of block-based Krylov methods in heterogeneous computing environments
No full textLarge-scale problems in engineering and science often require the solution of sparse linear algebra problems and the Krylov subspace iteration methods (KM) have led to a major change in how users deal with them. But, for these solvers to use extreme-scale hardware efficiently a lot of work was spent to redesign both the KM algorithms and their implementations to address challenges like extreme concurrency, complex memory hierarchies, costly data movement, and heterogeneous node architectures. All the redesign approaches bases the KM algorithm on block-based strategies which lead to the Block-KM (BKM) algorithm which has high granularity (i.e., the ratio of computation time to communication time). The work proposes novel parallel revisitation of the modules used in BKM which are based on the overlapping of communication and computation. Such revisitation is evaluated by a model of their granularity and verified on the basis of a case study related to a classical problem from numerical linear algebra
Towards a parallel component for imaging in PETSc programming environment: a case study in 3-D echocardiography
No full textA key issue in designing application codes which are able to effectively take advantage of the available high performance computing resources is to deal with the software management and development complexity. This is even more true for imaging applications. This work is the first dowel in a rather wide mosaic, aimed at construction of a PSE (Problem Solving Environment) oriented to imaging applications.
We discuss computational efforts towards the development of a distributed software environment, enabling the transparent use of high performance computers and storage systems for 3-D Echocardiographic sequences denoising via nonlinear diffusion filtering.
More precisely, we describe a component-based approach for the development of an integrated software environment relying on the Portable, Extensible Toolkit for Scientific Computation (PETSc).
Our approach uses a distributed memory model where we hide, within the PETSc parallel objects, the details of internode communications while intranode communications have been handled at a higher level. We report some experiences we made on an in vivo acquired 3-D Echocardiographic sequence obtained by means of a rotational acquisition technique using Tomtec Imaging system
Un software parallelo per l'Analisi Multiscala di un'immagine tridimensionale in ambiente PETSc. Parte II: denoising di un'immagine ecocardiografica
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Parallel Software for 3D SPECT imaging based on the 2D+1 approximation of collimator blur
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