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Tpetra Lesson 07: Thread-Parallel Fill

Construct a sparse matrix in paralel using Kokkos

Lesson topics

This lesson shows an example of how to go from a simple finite-element discretization, to a linear system to solve, in a thread-parallel way, using Kokkos.

Code example

This is an example code that uses Kokkos to thread-parallelize linear system construction.

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//
// Tpetra: Templated Linear Algebra Services Package
// Copyright (2008) Sandia Corporation
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// this software without specific prior written permission.
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#include <Tpetra_Core.hpp>
// This is the only header file you need to include for the "core"
// part of Kokkos. That includes Kokkos::View, Kokkos::parallel_*,
// and atomic updates.
#include <Kokkos_Core.hpp>
#include <Tpetra_CrsMatrix.hpp>
#include <Tpetra_Vector.hpp>
#include <iostream>
#ifdef KOKKOS_ENABLE_CXX11_DISPATCH_LAMBDA
// Exact solution of the partial differential equation that main()
// discretizes. We include it here to check the error.
KOKKOS_INLINE_FUNCTION double
exactSolution (const double x, const double T_left, const double T_right)
{
return -4.0 * (x - 0.5) * (x - 0.5) + 1.0 + (T_left - T_right) * x;
}
// Attempt to solve Ax=b using CG (the Method of Conjugate Gradients),
// and return the number of iterations.
template<class CrsMatrixType, class VectorType>
int
solve (VectorType& x, const CrsMatrixType& A, const VectorType& b, const double dx)
{
using std::cout;
using std::endl;
const int myRank = x.getMap ()->getComm ()->getRank ();
const bool verbose = false;
// In practice, one would call Belos using a preconditioner from
// MueLu, Ifpack2, or some other package. For now, we implement CG
// by hand.
const double convTol = std::max (dx * dx, 1.0e-8);
// Don't do more CG iterations than the problem's dimension.
const int maxNumIters = std::min (static_cast<Tpetra::global_size_t> (100),
A.getGlobalNumRows ());
VectorType r (A.getRangeMap ());
A.apply (x, r);
r.update (1.0, b, -1.0); // r := -(A*x) + b
const double origResNorm = r.norm2 ();
if (myRank == 0 && verbose) {
cout << "Original residual norm: " << origResNorm << endl;
}
if (origResNorm <= convTol) {
return 0; // the solution is already close enough
}
double r_dot_r = origResNorm * origResNorm;
VectorType p (r, Teuchos::Copy);
VectorType Ap (A.getRangeMap ());
int numIters = 1;
for (; numIters <= maxNumIters; ++numIters) {
A.apply (p, Ap);
const double p_dot_Ap = p.dot (Ap);
if (p_dot_Ap <= 0.0) {
if (myRank == 0) {
cout << "At iteration " << numIters << ", p.dot(Ap) = " << p_dot_Ap << " <= 0.";
}
// Revert to approximate solution x from previous iteration.
return numIters - 1;
}
const double alpha = r_dot_r / p_dot_Ap;
if (alpha <= 0.0) {
if (myRank == 0) {
cout << "At iteration " << numIters << ", alpha = " << alpha << " <= 0.";
}
// Revert to approximate solution x from previous iteration.
return numIters - 1;
}
x.update (alpha, p, 1.0); // x := alpha*p + x
r.update (-alpha, Ap, 1.0); // r := -alpha*Ap + r
const double newResNorm = r.norm2 ();
const double r_dot_r_next = newResNorm * newResNorm;
const double newRelResNorm = newResNorm / origResNorm;
if (myRank == 0 && verbose) {
cout << "Iteration " << numIters << ": r_dot_r = " << r_dot_r
<< ", r_dot_r_next = " << r_dot_r_next
<< ", newResNorm = " << newResNorm << endl;
}
if (newRelResNorm <= convTol) {
return numIters;
}
const double beta = r_dot_r_next / r_dot_r;
p.update (1.0, r, beta); // p := r + beta*p
r_dot_r = r_dot_r_next;
}
return numIters;
}
int main (int argc, char* argv[]) {
using std::cout;
using std::endl;
// We're filling into Tpetra data structures now, so we have to
// respect Tpetra's choices of local and global indices.
Tpetra::ScopeGuard tpetraScope (&argc, &argv);
{
auto comm = Tpetra::getDefaultComm ();
const int myRank = comm->getRank ();
const int numProcs = comm->getSize ();
LO numLclElements = 10000;
int numGblElements = numProcs * numLclElements;
// We happened to choose a discretization with the same number of
// nodes and degrees of freedom as elements. That need not always
// be the case.
LO numLclNodes = numLclElements;
LO numLclRows = numLclNodes;
// Describe the physical problem (heat equation with
// nonhomogeneous Dirichlet boundary conditions) and its
// discretization.
Kokkos::View<double*> temperature ("temperature", numLclNodes);
const double diffusionCoeff = 1.0;
const double x_left = 0.0; // position of the left boundary
const double T_left = 0.0; // temperature at the left boundary
const double x_right = 1.0; // position of the right boundary
const double T_right = 1.0; // temperature at the right boundary
const double dx = (x_right - x_left) / numGblElements;
Kokkos::View<double*> forcingTerm ("forcing term", numLclNodes);
// Set the forcing term. We picked it so that we can know the exact
// solution of the heat equation, namely
//
// u(x) = -4.0 * (x - 0.5) * (x - 0.5) + 1.0 + (T_left - T_right)x.
Kokkos::parallel_for ("Set forcing term", numLclNodes,
KOKKOS_LAMBDA (const LO node) {
//const double x = x_left + node * dx;
forcingTerm(node) = -8.0;
// We multiply dx*dx into the forcing term, so the matrix's
// entries don't need to know it.
forcingTerm(node) *= dx * dx;
});
// Do a reduction over local elements to count the total number of
// (local) entries in the graph. While doing so, count the number
// of (local) entries in each row, using Kokkos' atomic updates.
// We may use LO for the number of entries in each row, since it
// may not exceed the number of columns in the local matrix.
Kokkos::View<LO*> rowCounts ("row counts", numLclRows);
size_t numLclEntries = 0;
Kokkos::parallel_reduce ("Count graph", numLclElements,
KOKKOS_LAMBDA (const LO elt, size_t& curNumLclEntries) {
const LO lclRows = elt;
// Always add a diagonal matrix entry.
Kokkos::atomic_fetch_add (&rowCounts(lclRows), LO(1));
curNumLclEntries++;
// Each neighboring MPI process contributes an entry to the
// current row. In a more realistic code, we might handle
// this either through a global assembly process (requiring
// MPI communication), or through ghosting a layer of elements
// (no MPI communication).
// MPI process to the left sends us an entry
if (myRank > 0 && lclRows == 0) {
Kokkos::atomic_fetch_add (&rowCounts(lclRows), LO(1));
curNumLclEntries++;
}
// MPI process to the right sends us an entry
if (myRank + 1 < numProcs && lclRows + 1 == numLclRows) {
Kokkos::atomic_fetch_add (&rowCounts(lclRows), LO(1));
curNumLclEntries++;
}
// Contribute a matrix entry to the previous row.
if (lclRows > 0) {
Kokkos::atomic_fetch_add (&rowCounts(lclRows-1), LO(1));
curNumLclEntries++;
}
// Contribute a matrix entry to the next row.
if (lclRows + 1 < numLclRows) {
Kokkos::atomic_fetch_add (&rowCounts(lclRows+1), LO(1));
curNumLclEntries++;
}
}, numLclEntries /* reduction result */);
// mfh 20 Aug 2017: We can't just use a Kokkos::View<size_t*> for
// the row offsets, because Tpetra::CrsMatrix reserves the right
// to use a different row offset type for different execution /
// memory spaces. Instead, we first deduce the row offset type,
// then construct a View of it. (Note that a row offset needs to
// have a type that can contain the sum of the row counts.)
using row_offset_type =
// Use a parallel scan (prefix sum) over the array of row counts, to
// compute the array of row offsets for the sparse graph.
Kokkos::View<row_offset_type*> rowOffsets ("row offsets", numLclRows+1);
Kokkos::parallel_scan ("Row offsets", numLclRows+1,
KOKKOS_LAMBDA (const LO lclRows,
row_offset_type& update,
const bool final) {
if (final) {
// Kokkos uses a multipass algorithm to implement scan. Only
// update the array on the final pass. Updating the array
// before changing 'update' means that we do an exclusive
// scan. Update the array after for an inclusive scan.
rowOffsets[lclRows] = update;
}
if (lclRows < numLclRows) {
update += rowCounts(lclRows);
}
});
// Use the array of row counts to keep track of where to put each
// new column index, when filling the graph. Updating the entries
// of rowCounts atomically lets us parallelize over elements
// (which may touch multiple rows at a time -- esp. in 2-D or 3-D,
// or with higher-order discretizations), rather than rows.
//
// We leave as an exercise to the reader how to use this array
// without resetting its entries.
Kokkos::deep_copy (rowCounts, 0);
Kokkos::View<LO*> colIndices ("column indices", numLclEntries);
Kokkos::View<double*> matrixValues ("matrix values", numLclEntries);
// Iterate over elements in parallel to fill the graph, matrix,
// and right-hand side (forcing term). The latter gets the
// boundary conditions (a trick for nonzero Dirichlet boundary
// conditions).
Kokkos::parallel_for ("Assemble", numLclElements,
KOKKOS_LAMBDA (const LO elt) {
// Push dx*dx into the forcing term.
const double offCoeff = -diffusionCoeff / 2.0;
const double midCoeff = diffusionCoeff;
// In this discretization, every element corresponds to a
// degree of freedom, and to a row of the matrix. (Boundary
// conditions are Dirichlet, so they don't count as degrees of
// freedom.)
const int lclRows = elt;
// Always add a diagonal matrix entry.
{
const LO count =
Kokkos::atomic_fetch_add (&rowCounts(lclRows), LO(1));
colIndices(rowOffsets(lclRows) + count) = lclRows;
Kokkos::atomic_fetch_add (&matrixValues(rowOffsets(lclRows) + count), midCoeff);
}
// Each neighboring MPI process contributes an entry to the
// current row. In a more realistic code, we might handle
// this either through a global assembly process (requiring
// MPI communication), or through ghosting a layer of elements
// (no MPI communication).
// MPI process to the left sends us an entry
if (myRank > 0 && lclRows == 0) {
const LO count = Kokkos::atomic_fetch_add (&rowCounts(lclRows), LO(1));
colIndices(rowOffsets(lclRows) + count) = numLclRows;
Kokkos::atomic_fetch_add (&matrixValues(rowOffsets(lclRows) + count), offCoeff);
}
// MPI process to the right sends us an entry
if (myRank + 1 < numProcs && lclRows + 1 == numLclRows) {
const LO count =
Kokkos::atomic_fetch_add (&rowCounts(lclRows), LO(1));
// Give this entry the right local column index, depending
// on whether the MPI process to the left has already sent
// us an entry.
const int colInd = (myRank > 0) ? numLclRows + 1 : numLclRows;
colIndices(rowOffsets(lclRows) + count) = colInd;
Kokkos::atomic_fetch_add (&matrixValues(rowOffsets(lclRows) + count), offCoeff);
}
// Contribute a matrix entry to the previous row.
if (lclRows > 0) {
const LO count = Kokkos::atomic_fetch_add (&rowCounts(lclRows-1), LO(1));
colIndices(rowOffsets(lclRows-1) + count) = lclRows;
Kokkos::atomic_fetch_add (&matrixValues(rowOffsets(lclRows-1) + count), offCoeff);
}
// Contribute a matrix entry to the next row.
if (lclRows + 1 < numLclRows) {
const LO count = Kokkos::atomic_fetch_add (&rowCounts(lclRows+1), LO(1));
colIndices(rowOffsets(lclRows+1) + count) = lclRows;
Kokkos::atomic_fetch_add (&matrixValues(rowOffsets(lclRows+1) + count), offCoeff);
}
});
//
// Construct Tpetra objects
//
using Teuchos::RCP;
using Teuchos::rcp;
const GO indexBase = 0;
RCP<const Tpetra::Map<> > rowMap =
rcp (new Tpetra::Map<> (numGblElements, numLclElements, indexBase, comm));
LO num_col_inds = numLclElements;
if (myRank > 0) {
num_col_inds++;
}
if (myRank + 1 < numProcs) {
num_col_inds++;
}
// Soon, it will be acceptable to use a device View here. The
// issues are that Teuchos::RCP isn't thread safe, and the Kokkos
// version of Tpetra::Map isn't quite ready yet. We will change
// the latter soon.
Kokkos::View<GO*>::HostMirror colInds ("Column Map", num_col_inds);
for (LO k = 0; k < numLclElements; ++k) {
colInds(k) = rowMap->getGlobalElement (k);
}
LO k = numLclElements;
if (myRank > 0) {
// Contribution from left process.
colInds(k++) = rowMap->getGlobalElement (0) - 1;
}
if (myRank + 1 < numProcs) {
// Contribution from right process.
colInds(k++) = rowMap->getGlobalElement (numLclElements - 1) + 1;
}
// Flag to tell Tpetra::Map to compute the global number of
// indices in the Map.
Teuchos::OrdinalTraits<Tpetra::global_size_t>::invalid ();
RCP<const Tpetra::Map<> > colMap =
rcp (new Tpetra::Map<> (INV, colInds.data (), colInds.extent (0), indexBase, comm));
Tpetra::CrsMatrix<double> A (rowMap, colMap, rowOffsets,
colIndices, matrixValues);
A.fillComplete ();
// Hack to deal with the fact that Tpetra::Vector needs a
// DualView<double**> for now, rather than a View<double*>.
Kokkos::DualView<double**, Kokkos::LayoutLeft> b_lcl ("b", numLclRows, 1);
b_lcl.modify<Kokkos::DualView<double**, Kokkos::LayoutLeft>::t_dev::execution_space> ();
Kokkos::deep_copy (Kokkos::subview (b_lcl.d_view, Kokkos::ALL (), 0), forcingTerm);
Tpetra::Vector<> b (A.getRangeMap (), b_lcl);
Kokkos::DualView<double**, Kokkos::LayoutLeft> x_lcl ("b", numLclRows, 1);
x_lcl.modify<Kokkos::DualView<double**, Kokkos::LayoutLeft>::t_dev::execution_space> ();
Kokkos::deep_copy (Kokkos::subview (x_lcl.d_view, Kokkos::ALL (), 0), temperature);
Tpetra::Vector<> x (A.getDomainMap (), x_lcl);
const int numIters = solve (x, A, b, dx); // solve the linear system
if (myRank == 0) {
cout << "Linear system Ax=b took " << numIters << " iteration(s) to solve" << endl;
}
// Hack to deal with the fact that Tpetra::Vector needs a
// DualView<double**> for now, rather than a View<double*>. This
// means that we have to make a deep copy back into the
// 'temperature' output array.
x_lcl.sync<Kokkos::DualView<double**, Kokkos::LayoutLeft>::t_dev::execution_space> ();
Kokkos::deep_copy (temperature, Kokkos::subview (b_lcl.d_view, Kokkos::ALL (), 0));
// Correct the solution for the nonhomogenous Dirichlet boundary
// conditions.
Kokkos::parallel_for ("Boundary conditions", numLclNodes,
KOKKOS_LAMBDA (const LO node) {
const double x_cur = x_left + node * dx;
temperature(node) += (T_left - T_right) * x_cur;
});
// Compare the computed solution against the known exact solution:
//
// u(x) = -4.0 * (x - 0.5) * (x - 0.5) + 1.0 + (T_left - T_right)x.
Tpetra::Vector<> x_exact (x, Teuchos::Copy);
typedef Tpetra::Vector<>::dual_view_type dual_view_type;
typedef dual_view_type::t_dev::execution_space execution_space;
typedef dual_view_type::t_dev::memory_space memory_space;
typedef Kokkos::RangePolicy<execution_space, LO> policy_type;
x_exact.sync<memory_space> ();
x_exact.modify<memory_space> ();
// Slight breakage with respect to GCC < 4.8.
// mfh 20 Aug 2017: See also GitHub issue #1629.
#if defined(__GNUC__)
# if __GNUC__ == 4 && __GNUC_MINOR__ <= 7
auto x_exact_lcl = x_exact.getLocalView<memory_space> ();
# elif __GNUC__ == 4 && __GNUC_MINOR__ > 7 // GCC >= 4.8
auto x_exact_lcl = x_exact.template getLocalView<memory_space> ();
# else // GCC >= 5
auto x_exact_lcl = x_exact.getLocalView<memory_space> ();
# endif // __GNUC__ == 4 && __GNUC_MINOR__ <= 7
#else // ! defined(__GNUC__)
auto x_exact_lcl = x_exact.template getLocalView<memory_space> ();
#endif // defined(__GNUC__)
Kokkos::parallel_for ("Compare solutions",
policy_type (0, numLclNodes),
KOKKOS_LAMBDA (const LO& node) {
const double x_cur = x_left + node * dx;
x_exact_lcl(node,0) -= exactSolution (x_cur, T_left, T_right);
});
const double absErrNorm = x_exact.norm2 ();
const double relErrNorm = b.norm2 () / absErrNorm;
if (myRank == 0) {
cout << "Relative error norm: " << relErrNorm << endl;
}
if (myRank == 0) {
cout << "End Result: TEST PASSED" << endl;
}
}
return EXIT_SUCCESS;
}
#else
int main (int argc, char* argv[]) {
using std::cout;
using std::endl;
cout << "This lesson was not compiled because Kokkos" << endl
"was not configured with lambda support for all backends." << endl
"Tricking CTest into perceiving success anyways:" << endl
"End Result: TEST PASSED" << endl;
return 0;
}
#endif