///////////////////////////////////////////////////////////////////////////////
//* performs a matrix-vector multiply with optional transposes BLAS version
void MultMv(const Matrix<double> &A, const Vector<double> &v, DenseVector<double> &c,
const bool At, double a, double b)
{
static char t[2] = {'N','T'};
char *ta=t+At;
int sA[2] = {A.nRows(), A.nCols()}; // sizes of A
int sV[2] = {v.size(), 1}; // sizes of v
GCK(A, v, sA[!At]!=sV[0], "MultAB<double>: matrix-vector multiply");
if (c.size() != sA[At])
{
c.resize(sA[At]); // set size of C to final size
c.zero();
}
// get pointers to the matrix sizes needed by BLAS
int *M = sA+At; // # of rows in op[A] (op[A] = A' if At='T' else A)
int *N = sV+1; // # of cols in op[B]
int *K = sA+!At; // # of cols in op[A] or # of rows in op[B]
double *pa=A.ptr(), *pv=v.ptr(), *pc=c.ptr();
#ifdef COL_STORAGE
dgemm_(ta, t, M, N, K, &a, pa, sA, pv, sV, &b, pc, M);
#else
dgemm_(t, ta, N, M, K, &a, pv, sV+1, pa, sA+1, &b, pc, N);
#endif
}
//============================================================
void ParSparseMatrix<double>::MultMv(const Vector<double>& v,
DenseVector<double>& c) const
{
int numProcs = MPI_Wrappers::size(_comm);
#ifdef DISABLE_PAR_HEURISTICS
// Use much more lenient heuristics to exercise parallel code
if (numProcs == 1 || _size < 300) {
#else
// These are simple heuristics to perform multiplication in serial if
// parallel will be slower. They were determined experimentally.
if ( numProcs == 1 ||
(_size < 50000 || _size > 10000000) ||
((_size < 150000 || _size > 5000000) && numProcs > 8) ||
((_size < 500000 || _size > 2500000) && numProcs > 16 ) ||
(numProcs > 32)) {
#endif
SparseMatrix<double>::MultMv(v, c);
return;
}
SparseMatrix<double>::compress(*this);
GCK(*this, v, this->nCols() != v.size(), "ParSparseMatrix * Vector")
SparseMatrix<double> A_local;
// Split the sparse matrix. partition() takes a ParSparMat, so we cast.
partition(*static_cast<ParSparseMatrix<double>*>(&A_local));
// actually do multiplication - end up with partial result vector
// on each processor
#ifdef TIMING_ON
timespec before, after;
// barrier(MPI_COMM_WORLD);
clock_gettime(CLOCK_MONOTONIC, &before);
#endif
DenseVector<double> c_local = A_local * v;
#ifdef TIMING_ON
// barrier(MPI_COMM_WORLD);
clock_gettime(CLOCK_MONOTONIC, &after);
cout << "P" << MPI_Wrappers::rank(MPI_COMM_WORLD) << " " << time_diff(before,after) << " mat.vec time\n";
//LammpsInterface::instance()->all_print((after-before),"mat.vec time");
barrier(MPI_COMM_WORLD);
#endif
// destroy A_local intelligently
static_cast<ParSparseMatrix<double>*>(&A_local)->finalize();
// Add all the result vectors together on each processor.
#ifdef TIMING_ON
barrier(MPI_COMM_WORLD);
//barrier(MPI_COMM_WORLD);
clock_gettime(CLOCK_MONOTONIC, &before);
#endif
allsum(_comm, c_local.ptr(), c.ptr(), c_local.size());
#ifdef TIMING_ON
//barrier(MPI_COMM_WORLD);
clock_gettime(CLOCK_MONOTONIC, &after);
cout << "P" << MPI_Wrappers::rank(MPI_COMM_WORLD) << " " << time_diff(before,after) << " allsum time\n";
//LammpsInterface::instance()->print_msg_once((after-before),"allsum time");
#endif
}
DenseVector<double> ParSparseMatrix<double>::transMat(
const Vector<double>& v) const {
SparseMatrix<double>::compress(*this);
GCK(*this, v, this->nRows() != v.size(), "ParSparseMatrix transpose * Vector")
DenseVector<double> c(nCols(), true);
SparseMatrix<double> A_local;
partition(*static_cast<ParSparseMatrix<double>*>(&A_local));
// actually do multiplication - end up with partial result vector
// on each processor
DenseVector<double> c_local = A_local.transMat(v);
static_cast<ParSparseMatrix<double>*>(&A_local)->finalize();
// Add all the result vectors together on each processor.
allsum(_comm, c_local.ptr(), c.ptr(), c_local.size());
return c;
}
