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Trilinos/Epetra: Linear Algebra Services Package.



Epetra implements serial and parallel linear algebra. This includes both construction and use of sparse graphs, sparse matrices, and dense vectors. Epetra also provides data distribution and redistribution facilities. This package provides the underlying foundation for a whole stack of Trilinos solvers.

Overview of Epetra.

Epetra Classes

Epetra contains a number of classes. They can be categorized as follows:

Fortran and C Support

Epetra provides a small set of user-extendable Fortran and C interfaces. These are found in Epetra_C_wrappers and Epetra_Fortran_wrappers. These wrappers are hand-generated, so adding to the list requires hand-coding. The Trilinos team is looking at automated techniques to support Fortran and C, but we have not made any signficant effort in this direction.

Trilinos and Epetra

Epetra can be used as a stand-alone package. However, it also provides the foundation for Trilinos. Trilinos is a collection of solver packages. The first available package is AztecOO, a C++ implementation of the preconditioned iterative solver package Aztec. This particular class can be used to solve a linear system as defined by a Epetra_LinearProblem object that is passed in at construction. Alternatively, one may pass in the matrix, initial guess and right-hand-side as Epetra objects and solves a linear system using preconditioner and solver options set by the user.

AztecOO supports all the functionality of Aztec except for explicit scaling options. Explicit scaling is done instead by the Epetra_LinearProblem class (which in turn uses the scaling methods in the Epetra_RowMatrix and Epetra_MultiVector classes). Documentation for Aztec can be found in your Trilinos archive in the file Trilinos/doc/aztec/manual.ps

Transition support for current Aztec users

In order to support existing Aztec users, we have developed a few modules that aid in the transition from Aztec to Trilinos/AztecOO. The first module is AZOO_iterate(). This function has an identical interface to AZ_iterate and, for most purpose, these two should be interchangeable. AZOO_iterate() differs from AZ_iterate in that it first converts the Aztec objects to Epetra objects using the module Aztec2Epetra(). It then creates an Epetra_LinearProblem and performs any requested scaling explicitly, and then calls AztecOO. The transition to AZOO_iterate() is meant to be trivial and temporary. We encourage users to customize their own version of AZOO_iterate(), and to eventually build Epetra objects directly.

AztecOO also provides the Epetra_MsrMatrix class as an Epetra_RowMatrix adapter that accepts an AZ_MATRIX struct (as defined in Aztec 2.1) and provides Epetra_RowMatrix functionality using the AZ_MATRIX object, without copying matrix data. If your application already stores matrix data in an AZ_MATRIX struct, then you can directly use AztecOO, ML and Ifpack without reimplementing the matrix construction process using native Epetra sparse matrix classes.

Extending Epetra functionality

Epetra, and therefore Trilinos, is easily extended to support new implementations of basic functionality. Although Epetra provides serial and MPI support, other parallel communication methods (such as Co-array Fortran, UPC, SHMEM, etc.) can be implemented because parallel machine functionality is accessed via the abstract Epetra_Comm interface. Similarly, solver algorithms that need access to the action of a linear operator on a vector get access to this functionality via the Epetra_Operator interface. Any class (including all Epetra matrix classes, Ifpack and ML preconditioners and AztecOO) that implements the Epetra_Operator interface can be used. The final major abstract interface in Epetra is Epetra_RowMatrix. Any solver algorithm that needs access to matrix coefficients can obtain this information via the Epetra_RowMatrix interface. All Epetra sparse matrix classes implement this interface. AztecOO, Ifpack, ML and other packages access matrix coefficients via the Epetra_RowMatrix interface.

By using abstract base classes to access external functionality and then providing a wide range of default implementations. Novel adapters to Epetra_Comm have been developed experimentally, but no production code is available beyond serial and MPI. Epetra_Operator is commonly extended by users who have complex operators that cannot be explicitly constructed but can be implicitly applied. Examples include ``matrix-free'' methods such as directional derivative approximations and implicit inverses via iterative methods. Most preconditioners are easily expressed as Epetra_Operator objects. Epetra_RowMatrix is often extended to provide support for alternative data structures, or to eliminate the need to explicitly store matrix coefficients but instead build them ``on-the-fly'' row-by-row as needed.

Although Epetra_RowMatrix is the base class that is used to access matrix coefficient information, if you want to provide your own implementation of Epetra_RowMatrix, it is probably best to inherit from the Epetra_BasicRowMatrix. Epetra_BasicRowMatrix provides a reasonable implementation of most Epetra_RowMatrix methods and reduces the number of methods you must implement from 39 to between 4 and 6. To see an example of how to make your own Epetra_RowMatrix class using Epetra_BasicRowMatrix as a starting point, see the Epetra_JadMatrix class.

Why not abstract multivector and vector classes?

Although it may seem logical to have abstract multivector and vector classes, the practival value of such classes is not as obvious to the Epetra developers, at least at the Epetra level of functionality. Serious efforts in abstract multivectors/vectors is very important to Trilinos, however our efforts in this area have been focused on the Thyra package. In Thyra the user will find a broad set of interfaces and an incredibly useful capability to provide user-defined multivector/vector operations (see the RTOp package). Furthermore, Epetra multivectors/vectors are fully compatible with Thyra.

Tutorial lessons

Epetra includes five lessons. The first shows how to initialize an application using Epetra, with or without MPI. Following lessons demonstrate how to create and modify Epetra data structures, and how to use Epetra's abstractions to move data between different parallel distributions. The lessons include both sections meant for reading, and example code that builds and runs. In fact, the code passes nightly tests on many different platforms.

Epetra adapters for Thyra and PyTrilinos

The Epetra pacakge can also optionally support adapters to various interfaces. Support for these adapters can be turned on at configure time.

Browse all of Epetra as a single doxygen collection

You can browse all of Epetra as a single doxygen collection. Warning, this is not the recommended way to learn about Epetra software. However, this is a good way to browse the directory structure of epetra, to locate files, etc.