1,064 research outputs found
Supporting 64-bit global indices in Epetra and other Trilinos packages -- Techniques used and lessons learned
The Trilinos Project is an effort to facilitate the design, development,
integration and ongoing support of mathematical software libraries within an
object-oriented framework. It is intended for large-scale, complex multiphysics
engineering and scientific applications. Epetra is one of its basic packages.
It provides serial and parallel linear algebra capabilities. Before Trilinos
version 11.0, released in 2012, Epetra used the C++ int data-type for storing
global and local indices for degrees of freedom (DOFs). Since int is typically
32-bit, this limited the largest problem size to be smaller than approximately
two billion DOFs. This was true even if a distributed memory machine could
handle larger problems. We have added optional support for C++ long long
data-type, which is at least 64-bit wide, for global indices. To save memory,
maintain the speed of memory-bound operations, and reduce further changes to
the code, the local indices are still 32-bit. We document the changes required
to achieve this feature and how the new functionality can be used. We also
report on the lessons learned in modifying a mature and popular package from
various perspectives -- design goals, backward compatibility, engineering
decisions, C++ language features, effects on existing users and other packages,
and build integration
The LifeV library: engineering mathematics beyond the proof of concept
LifeV is a library for the finite element (FE) solution of partial
differential equations in one, two, and three dimensions. It is written in C++
and designed to run on diverse parallel architectures, including cloud and high
performance computing facilities. In spite of its academic research nature,
meaning a library for the development and testing of new methods, one
distinguishing feature of LifeV is its use on real world problems and it is
intended to provide a tool for many engineering applications. It has been
actually used in computational hemodynamics, including cardiac mechanics and
fluid-structure interaction problems, in porous media, ice sheets dynamics for
both forward and inverse problems. In this paper we give a short overview of
the features of LifeV and its coding paradigms on simple problems. The main
focus is on the parallel environment which is mainly driven by domain
decomposition methods and based on external libraries such as MPI, the Trilinos
project, HDF5 and ParMetis.
Dedicated to the memory of Fausto Saleri.Comment: Review of the LifeV Finite Element librar
Recommended from our members
Preparing sparse solvers for exascale computing.
Sparse solvers provide essential functionality for a wide variety of scientific applications. Highly parallel sparse solvers are essential for continuing advances in high-fidelity, multi-physics and multi-scale simulations, especially as we target exascale platforms. This paper describes the challenges, strategies and progress of the US Department of Energy Exascale Computing project towards providing sparse solvers for exascale computing platforms. We address the demands of systems with thousands of high-performance node devices where exposing concurrency, hiding latency and creating alternative algorithms become essential. The efforts described here are works in progress, highlighting current success and upcoming challenges. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'
Automating embedded analysis capabilities and managing software complexity in multiphysics simulation part II: application to partial differential equations
A template-based generic programming approach was presented in a previous
paper that separates the development effort of programming a physical model
from that of computing additional quantities, such as derivatives, needed for
embedded analysis algorithms. In this paper, we describe the implementation
details for using the template-based generic programming approach for
simulation and analysis of partial differential equations (PDEs). We detail
several of the hurdles that we have encountered, and some of the software
infrastructure developed to overcome them. We end with a demonstration where we
present shape optimization and uncertainty quantification results for a 3D PDE
application
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