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EpetraLesson03
This lesson demonstrates the following:
- How to construct a Epetra_CrsMatrix (a distributed sparse matrix)
- How to modify the entries of a previously constructed CrsMatrix
- How to use CrsMatrix and Vector to implement a simple iterative eigensolver (the power method)
Before starting this lesson, it helps to have finished [Epetra Lesson 01] (../EpetraLesson01) (how to initialize Epetra) and [Epetra Lesson 02] (../EpetraLesson02) (how to create distributions and distributed vectors). After completing this lesson, you might want to learn about more efficient ways to add or modify entries in a Epetra sparse matrix.
This is the first lesson in the usual sequence which covers adding entries to ("filling") a Epetra sparse matrix, and modifying the values of those entries after creating the matrix. Creating and filling a Epetra sparse matrix involves the following steps:
- Create the CrsMatrix (by calling one of its constructors)
- Call methods to add entries to the sparse matrix
- Call the matrix's FillComplete() method
Epetra's sparse matrices are distributed over one or more parallel processes, just like vectors or other distributed objects. Also just like vectors, you have to tell the sparse matrix its distribution on construction. Unlike vectors, though, sparse matrices have two dimensions over which to be distributed: rows and columns.
Many users are perfectly happy ignoring the column distribution and just distributing the matrix in "one-dimensional" fashion over rows. In that case, you need only supply the "row Map" to the constructor. This implies that for any row which a process owns, that process may insert entries in any column in that row.
Some users want to use the full flexibility of distributing both the rows and columns of the matrix over processes. This "two-dimensional" distribution, if chosen optimally, can significantly reduce the amount of communication needed for distributed-memory parallel sparse matrix-vector multiply. Trilinos packages like Zoltan can help you compute this distribution. In that case, you may give both the "row Map" and the "column Map" to the constructor. This implies that for any row which a process owns, that process may insert entries in any column in that row which that process owns in its column Map.
Finally, other users already know the structure of the sparse matrix, and just want to fill in values. These users should first create the graph (as an Epetra_CrsGraph), call FillComplete() on the graph, and then give the graph to the constructor of CrsMatrix. The graph may have either a "1-D" or "2-D" distribution, as mentioned above.
Methods of CrsMatrix that start with "Insert" actually change the structure of the sparse matrix. Methods that start with "Replace" or "SumInto" only modify existing values.
== Calling FillComplete()
Calling FillComplete() signals that you are done changing the structure (if allowed) or values of the sparse matrix. This is an expensive operation, because it both rearranges local data, and communicates in order to build reusable communication patterns for sparse matrix-vector multiply. You should try to amortize the cost of this operation whenever possible over many sparse matrix-vector multiplies.
FillComplete() takes two arguments:
- the domain Map (the distribution of the input vector x in a sparse matrix-vector multiply y = A*x)
- the range Map (the distribution of the output vector y in a sparse matrix-vector multiply y = A*x)
Both the domain and range Maps must be one-to-one: that is, each global index in the Map must be uniquely owned by one and only one process. You will need to supply these two arguments to FillComplete() under any of the following conditions:
- When the row Map is not the same as the range Map (it can't be if the row Map is not one to one)
- When the domain and range Maps are not equal (e.g., if the matrix is not square)
- When the domain or range Map as not the same as the row Map
If the domain and range Maps equal the row Map and the row Map is one-to-one, then you may call FillComplete() with no arguments.
Once you have called FillComplete(), you may modify the values in the sparse matrix, but you may not modify its graph structure.
The following code example shows how to fill and compute with a Epetra sparse matrix, using the procedure discussed in the text above.
/*!
\example lesson03_power_method.cpp
\brief Example showing how to fill and compute with a Epetra sparse matrix
\ref Epetra_Lesson03 explains this example in detail.
*/
// This defines useful macros like HAVE_MPI, which is defined if and
// only if Epetra was built with MPI enabled.
#include <Epetra_config.h>
#ifdef HAVE_MPI
# include <mpi.h>
// Epetra's wrapper for MPI_Comm. This header file only exists if
// Epetra was built with MPI enabled.
# include <Epetra_MpiComm.h>
#else
# include <Epetra_SerialComm.h>
#endif // HAVE_MPI
#include <Epetra_CrsMatrix.h>
#include <Epetra_Map.h>
#include <Epetra_Vector.h>
#include <Epetra_Version.h>
#include <sstream>
#include <stdexcept>
// Implementation of the power method for estimating the eigenvalue of
// maximum magnitude of a matrix. This function returns the
// eigenvalue estimate.
//
// A: The sparse matrix or operator, as an Epetra_Operator.
// niters: Maximum number of iterations of the power method.
// tolerance: If the 2-norm of the residual A*x-lambda*x (for the
// current eigenvalue estimate lambda) is less than this, stop
// iterating.
// out: output stream to which to print the current status of the
// power method.
double
powerMethod (const Epetra_Operator& A,
const int niters,
const double tolerance);
int
main (int argc, char *argv[])
{
using std::cout;
using std::endl;
#ifdef HAVE_MPI
MPI_Init (&argc, &argv);
Epetra_MpiComm comm (MPI_COMM_WORLD);
#else
Epetra_SerialComm comm;
#endif // HAVE_MPI
const int myRank = comm.MyPID ();
const int numProcs = comm.NumProc ();
if (myRank == 0) {
// Print out the Epetra software version.
cout << Epetra_Version () << endl << endl
<< "Total number of processes: " << numProcs << endl;
}
// The type of global indices. You could just set this to int,
// but we want the example to work for Epetra64 as well.
#ifdef EPETRA_NO_32BIT_GLOBAL_INDICES
// Epetra was compiled only with 64-bit global index support, so use
// 64-bit global indices.
typedef long long global_ordinal_type;
#else
// Epetra was compiled with 32-bit global index support. If
// EPETRA_NO_64BIT_GLOBAL_INDICES is defined, it does not also
// support 64-bit indices.
typedef int global_ordinal_type;
#endif // EPETRA_NO_32BIT_GLOBAL_INDICES
// The number of rows and columns in the matrix.
const global_ordinal_type numGlobalElements = 50;
// Construct a Map that puts approximately the same number of
// equations on each processor.
const global_ordinal_type indexBase = 0;
Epetra_Map map (numGlobalElements, indexBase, comm);
// Get the list of global indices that this process owns. In this
// example, this is unnecessary, because we know that we created a
// contiguous Map (see above). (Thus, we really only need the min
// and max global index on this process.) However, in general, we
// don't know what global indices the Map owns, so if we plan to add
// entries into the sparse matrix using global indices, we have to
// get the list of global indices this process owns.
const int numMyElements = map.NumMyElements ();
global_ordinal_type* myGlobalElements = NULL;
#ifdef EPETRA_NO_32BIT_GLOBAL_INDICES
myGlobalElements = map.MyGlobalElements64 ();
#else
myGlobalElements = map.MyGlobalElements ();
#endif // EPETRA_NO_32BIT_GLOBAL_INDICES
// In general, tests like this really should synchronize across all
// processes. However, the likely cause for this case is a
// misconfiguration of Epetra, so we expect it to happen on all
// processes, if it happens at all.
if (numMyElements > 0 && myGlobalElements == NULL) {
throw std::logic_error ("Failed to get the list of global indices");
}
if (myRank == 0) {
cout << endl << "Creating the sparse matrix" << endl;
}
// Create a Epetra sparse matrix whose rows have distribution given
// by the Map. The max number of entries per row is 3.
Epetra_CrsMatrix A (Copy, map, 3);
// Local error code for use below.
int lclerr = 0;
// Fill the sparse matrix, one row at a time. InsertGlobalValues
// adds entries to the sparse matrix, using global column indices.
// It changes both the graph structure and the values.
double tempVals[3];
global_ordinal_type tempGblInds[3];
for (int i = 0; i < numMyElements; ++i) {
// A(0, 0:1) = [2, -1]
if (myGlobalElements[i] == 0) {
tempVals[0] = 2.0;
tempVals[1] = -1.0;
tempGblInds[0] = myGlobalElements[i];
tempGblInds[1] = myGlobalElements[i] + 1;
if (lclerr == 0) {
lclerr = A.InsertGlobalValues (myGlobalElements[i], 2, tempVals, tempGblInds);
}
if (lclerr != 0) {
break;
}
}
// A(N-1, N-2:N-1) = [-1, 2]
else if (myGlobalElements[i] == numGlobalElements - 1) {
tempVals[0] = -1.0;
tempVals[1] = 2.0;
tempGblInds[0] = myGlobalElements[i] - 1;
tempGblInds[1] = myGlobalElements[i];
if (lclerr == 0) {
lclerr = A.InsertGlobalValues (myGlobalElements[i], 2, tempVals, tempGblInds);
}
if (lclerr != 0) {
break;
}
}
// A(i, i-1:i+1) = [-1, 2, -1]
else {
tempVals[0] = -1.0;
tempVals[1] = 2.0;
tempVals[2] = -1.0;
tempGblInds[0] = myGlobalElements[i] - 1;
tempGblInds[1] = myGlobalElements[i];
tempGblInds[2] = myGlobalElements[i] + 1;
if (lclerr == 0) {
lclerr = A.InsertGlobalValues (myGlobalElements[i], 3, tempVals, tempGblInds);
}
if (lclerr != 0) {
break;
}
}
}
// If any process failed to insert at least one entry, throw.
int gblerr = 0;
(void) comm.MaxAll (&lclerr, &gblerr, 1);
if (gblerr != 0) {
throw std::runtime_error ("Some process failed to insert an entry.");
}
// Tell the sparse matrix that we are done adding entries to it.
gblerr = A.FillComplete ();
if (gblerr != 0) {
std::ostringstream os;
os << "A.FillComplete() failed with error code " << gblerr << ".";
throw std::runtime_error (os.str ());
}
// Number of iterations
const int niters = 500;
// Desired (absolute) residual tolerance
const double tolerance = 1.0e-2;
// Run the power method and report the result.
double lambda = powerMethod (A, niters, tolerance);
if (myRank == 0) {
cout << endl << "Estimated max eigenvalue: " << lambda << endl;
}
//
// Now we're going to change values in the sparse matrix and run the
// power method again.
//
//
// Increase diagonal dominance
//
if (myRank == 0) {
cout << endl << "Increasing magnitude of A(0,0), solving again" << endl;
}
if (A.RowMap ().MyGID (0)) {
// Get a copy of the row with with global index 0. Modify the
// diagonal entry of that row. Submit the modified values to the
// matrix.
const global_ordinal_type gidOfFirstRow = 0;
// Since 0 is a GID in the row Map on the calling process,
// lidOfFirstRow is a valid LID of that GID in the row Map.
const int lidOfFirstRow = A.RowMap ().LID (gidOfFirstRow);
int numEntriesInRow = A.NumMyEntries (lidOfFirstRow);
double* rowvals = new double [numEntriesInRow];
global_ordinal_type* rowinds = new global_ordinal_type [numEntriesInRow];
// Get a copy of the entries and column indices of global row 0.
// Get global column indices, so that we can figure out which
// entry corresponds to the diagonal entry in this row. (The row
// Map and column Map of the matrix may differ, so their local
// indices may not be the same.)
//
// Note that it's legal (though we don't exercise it in this
// example) for the row Map of the sparse matrix not to be one to
// one. This means that more than one process might own entries
// in the first row. In general, multiple processes might own the
// (0,0) entry, so that the global A(0,0) value is really the sum
// of all processes' values for that entry. However, scaling the
// entry by a constant factor distributes across that sum, so it's
// OK to do so.
if (lclerr == 0) {
lclerr = A.ExtractGlobalRowCopy (gidOfFirstRow,
numEntriesInRow, numEntriesInRow,
rowvals, rowinds);
}
if (lclerr == 0) { // no error
for (int i = 0; i < numEntriesInRow; ++i) {
if (rowinds[i] == gidOfFirstRow) {
// We have found the diagonal entry; modify it.
rowvals[i] *= 10.0;
}
}
// "Replace global values" means modify the values, but not the
// structure of the sparse matrix. If the specified columns
// aren't already populated in this row on this process, then this
// method fails and returns nonzero. Since we have already called
// FillComplete() on this matrix, we may not modify its graph
// structure any more.
if (lclerr == 0) {
lclerr = A.ReplaceGlobalValues (gidOfFirstRow, numEntriesInRow,
rowvals, rowinds);
}
}
if (rowvals != NULL) {
delete [] rowvals;
}
if (rowinds != NULL) {
delete [] rowinds;
}
}
// If the owning process(es) of global row 0 failed to replace the
// one entry, throw.
gblerr = 0;
(void) comm.MaxAll (&lclerr, &gblerr, 1);
if (gblerr != 0) {
throw std::runtime_error ("One of the owning process(es) of global "
"row 0 failed to replace an entry.");
}
// Run the power method again.
lambda = powerMethod (A, niters, tolerance);
if (myRank == 0) {
cout << endl << "Estimated max eigenvalue: " << lambda << endl;
}
// This tells the Trilinos test framework that the test passed.
if (myRank == 0) {
cout << "End Result: TEST PASSED" << endl;
}
#ifdef HAVE_MPI
(void) MPI_Finalize ();
#endif // HAVE_MPI
return 0;
}
double
powerMethod (const Epetra_Operator& A,
const int niters,
const double tolerance)
{
using std::cout;
using std::endl;
// An Operator doesn't have a Comm, but its domain Map does.
const Epetra_Comm& comm = A.OperatorDomainMap ().Comm ();
const int myRank = comm.MyPID ();
// Create three vectors for iterating the power method. Since the
// power method computes z = A*q, q should be in the domain of A and
// z should be in the range. (Obviously the power method requires
// that the domain and the range are equal, but it's a good idea to
// get into the habit of thinking whether a particular vector
// "belongs" in the domain or range of the matrix.) The residual
// vector "resid" is of course in the range of A.
Epetra_Vector q (A.OperatorDomainMap ());
Epetra_Vector z (A.OperatorRangeMap ());
Epetra_Vector resid (A.OperatorRangeMap ());
// Local error code for use below.
int lclerr = 0;
// Fill the iteration vector z with random numbers to start. Don't
// have grand expectations about the quality of our pseudorandom
// number generator; this is usually good enough for eigensolvers.
//
// If this call fails, just let the code below finish before trying
// to catch the error globally. Ditto for other calls below.
lclerr = z.Random ();
// lambda: the current approximation of the eigenvalue of maximum magnitude.
// normz: the 2-norm of the current iteration vector z.
// residual: the 2-norm of the current residual vector "resid"
double lambda = 0.0;
double normz = 0.0;
double residual = 0.0;
const double zero = 0.0;
const double one = 1.0;
// How often to report progress in the power method. Reporting
// progress requires computing a residual which can be expensive.
// However, if you don't compute the residual often enough, you
// might keep iterating even after you've converged.
const int reportFrequency = 10;
// Do the power method, until the method has converged or the
// maximum iteration count has been reached.
for (int iter = 0; iter < niters; ++iter) {
// If you feel confident that your code is correct, you may omit
// everything having to do with lclerr, and just write the following:
//
// z.Norm2 (&normz); // Compute the 2-norm of z
// q.Scale (one / normz, z); // q := z / normz
// A.Apply (q, z); // z := A * q
// q.Dot (z, &lambda); // Approx. max eigenvalue
lclerr = (lclerr == 0) ? z.Norm2 (&normz) : lclerr;
lclerr = (lclerr == 0) ? q.Scale (one / normz, z) : lclerr;
lclerr = (lclerr == 0) ? A.Apply (q, z) : lclerr;
lclerr = (lclerr == 0) ? q.Dot (z, &lambda) : lclerr;
// Compute and report the residual norm every reportFrequency
// iterations, or if we've reached the maximum iteration count.
if (iter % reportFrequency == 0 || iter + 1 == niters) {
// If you feel confident that your code is correct, you may omit
// everything having to do with lclerr, and just write the
// following:
//
// resid.Update (one, z, -lambda, q, zero); // z := A*q - lambda*q
// resid.Norm2 (&residual); // 2-norm of the residual vector
lclerr = (lclerr == 0) ? resid.Update (one, z, -lambda, q, zero) : lclerr;
lclerr = (lclerr == 0) ? resid.Norm2 (&residual) : lclerr;
if (myRank == 0) {
cout << "Iteration " << iter << ":" << endl
<< "- lambda = " << lambda << endl
<< "- ||A*q - lambda*q||_2 = " << residual << endl;
}
}
if (residual < tolerance) {
if (myRank == 0) {
cout << "Converged after " << iter << " iterations" << endl;
}
break;
} else if (iter + 1 == niters) {
if (myRank == 0) {
cout << "Failed to converge after " << niters << " iterations" << endl;
}
break;
}
}
// If any process failed to insert at least one entry, throw.
int gblerr = 0;
(void) comm.MaxAll (&lclerr, &gblerr, 1);
if (gblerr != 0) {
throw std::runtime_error ("The power method failed in some way.");
}
return lambda;
}
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