ExampleTridiagSpmdLinearOp.hpp

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//    Thyra: Interfaces and Support for Abstract Numerical Algorithms
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#ifndef THYRA_EXAMPLE_TRIDIAG_SPMD_LINEAR_OP_HPP
#define THYRA_EXAMPLE_TRIDIAG_SPMD_LINEAR_OP_HPP

#include "Thyra_SpmdLinearOpBase.hpp"
#include "Teuchos_DefaultComm.hpp"

template<class Scalar>
class ExampleTridiagSpmdLinearOp : public Thyra::SpmdLinearOpBase<Scalar> {
private:

  Teuchos::RCP<const Teuchos::Comm<Thyra::Index> >  comm_;
  int                  procRank_;
  int                  numProcs_;
  Thyra::Index         localDim_;
  std::vector<Scalar>  lower_;   // size = ( procRank == 0         ? localDim - 1 : localDim )    
  std::vector<Scalar>  diag_;    // size = localDim
  std::vector<Scalar>  upper_;   // size = ( procRank == numProc-1 ? localDim - 1 : localDim )

  void communicate( const bool first, const bool last, const Scalar x[], Scalar *x_km1, Scalar *x_kp1 ) const;

public:

  using Thyra::SpmdLinearOpBase<Scalar>::euclideanApply;

  ExampleTridiagSpmdLinearOp() : procRank_(0), numProcs_(0) {}

  ExampleTridiagSpmdLinearOp(
    const Teuchos::RCP<const Teuchos::Comm<Thyra::Index> > &comm
    ,const Thyra::Index localDim, const Scalar lower[], const Scalar diag[], const Scalar upper[] )
    { this->initialize(comm,localDim,lower,diag,upper); }
  
  void initialize(
    const Teuchos::RCP<const Teuchos::Comm<Thyra::Index> > &comm
    ,const Thyra::Index             localDim // >= 2
    ,const Scalar                   lower[]  // size == ( procRank == 0         ? localDim - 1 : localDim )
    ,const Scalar                   diag[]   // size == localDim
    ,const Scalar                   upper[]  // size == ( procRank == numProc-1 ? localDim - 1 : localDim )
    )
    {
      TEST_FOR_EXCEPT( localDim < 2 );
      this->setLocalDimensions(comm,localDim,localDim); // Needed to set up range() and domain()
      comm_  = Teuchos::DefaultComm<Thyra::Index>::getDefaultSerialComm(comm);
      localDim_ = localDim;
      numProcs_ = comm->getSize();
      procRank_ = comm->getRank();
      const Thyra::Index
        lowerDim = ( procRank_ == 0           ? localDim - 1 : localDim ),
        upperDim = ( procRank_ == numProcs_-1 ? localDim - 1 : localDim );
      lower_.resize(lowerDim);  for( int k = 0; k < lowerDim; ++k ) lower_[k] = lower[k];
      diag_.resize(localDim);   for( int k = 0; k < localDim; ++k ) diag_[k]  = diag[k];
      upper_.resize(upperDim);  for( int k = 0; k < upperDim; ++k ) upper_[k] = upper[k];
    }

  // Overridden form Teuchos::Describable */

  std::string description() const
    {
      return (std::string("ExampleTridiagSpmdLinearOp<") + Teuchos::ScalarTraits<Scalar>::name() + std::string(">"));
    }

protected:


  // Overridden from SingleScalarEuclideanLinearOpBase

  bool opSupported( Thyra::ETransp M_trans ) const
    {
      typedef Teuchos::ScalarTraits<Scalar> ST;
      return (M_trans == Thyra::NOTRANS || (!ST::isComplex && M_trans == Thyra::CONJ) );
    }

  // Overridden from SerialLinearOpBase

  void euclideanApply(
    const Thyra::ETransp                         M_trans
    ,const RTOpPack::ConstSubVectorView<Scalar>  &local_x_in
    ,const RTOpPack::SubVectorView<Scalar>       *local_y_out
    ,const Scalar                                alpha
    ,const Scalar                                beta
    ) const
    {
      typedef Teuchos::ScalarTraits<Scalar> ST;
      TEST_FOR_EXCEPTION( M_trans != Thyra::NOTRANS, std::logic_error, "Error, can not handle transpose!" );
      // Get constants
      const Scalar zero = ST::zero();
      // Get raw pointers to vector data to make me feel better!
      const Scalar *x = local_x_in.values();
      Scalar       *y = local_y_out->values();
      // Determine what process we are
      const bool first = ( procRank_ == 0 ), last = ( procRank_ == numProcs_-1 );
      // Communicate ghost elements
      Scalar x_km1, x_kp1;
      communicate( first, last, x, &x_km1, &x_kp1 );
      // Perform operation (if beta==0 then we must be careful since y could be uninitialized on input!)
      Thyra::Index k = 0, lk = 0;
      if( beta == zero ) {
        y[k] = alpha * ( (first?zero:lower_[lk]*x_km1) + diag_[k]*x[k] + upper_[k]*x[k+1] ); if(!first) ++lk;             // First local row
        for( k = 1; k < localDim_ - 1; ++lk, ++k )
          y[k] = alpha * ( lower_[lk]*x[k-1] + diag_[k]*x[k] + upper_[k]*x[k+1] );                                        // Middle local rows
        y[k] = alpha * ( lower_[lk]*x[k-1] + diag_[k]*x[k] + (last?zero:upper_[k]*x_kp1) );                               // Last local row
      }
      else {
        y[k] = alpha * ( (first?zero:lower_[lk]*x_km1) + diag_[k]*x[k] + upper_[k]*x[k+1] ) + beta*y[k]; if(!first) ++lk; // First local row
        for( k = 1; k < localDim_ - 1; ++lk, ++k )
          y[k] = alpha * ( lower_[lk]*x[k-1] + diag_[k]*x[k] + upper_[k]*x[k+1] ) + beta*y[k];                            // Middle local rows
        y[k] = alpha * ( lower_[lk]*x[k-1] + diag_[k]*x[k] + (last?zero:upper_[k]*x_kp1) ) + beta*y[k];                   // Last local row
      }
      //std::cout << "\ny = ["; for(k=0;k<localDim_;++k) { std::cout << y[k]; if(k<localDim_-1) std::cout << ","; } std::cout << "]\n";
    }

};  // end class ExampleTridiagSpmdLinearOp

// private

template<class Scalar>
void ExampleTridiagSpmdLinearOp<Scalar>::communicate(
  const bool first, const bool last, const Scalar x[], Scalar *x_km1, Scalar *x_kp1
  ) const
{
  const bool isEven = (procRank_ % 2 == 0);
  const bool isOdd = !isEven;
  // 1) Send x[localDim_-1] from each even process forward to the next odd
  // process where it is received in x_km1.
  if( isEven && procRank_+1 < numProcs_ ) send(*comm_,x[localDim_-1],procRank_+1);
  if( isOdd && procRank_-1 >= 0 )         receive(*comm_,procRank_-1,x_km1);
  // 2) Send x[0] from each odd process backward to the previous even
  // process where it is received in x_kp1.
  if( isOdd && procRank_-1 >= 0 )         send(*comm_,x[0],procRank_-1);
  if( isEven && procRank_+1 < numProcs_ ) receive(*comm_,procRank_+1,x_kp1);
  // 3) Send x[localDim_-1] from each odd process forward to the next even
  // process where it is received in x_km1.
  if( isOdd && procRank_+1 < numProcs_ )  send(*comm_,x[localDim_-1],procRank_+1);
  if( isEven && procRank_-1 >= 0 )        receive(*comm_,procRank_-1,x_km1);
  // 4) Send x[0] from each even process backward to the previous odd
  // process where it is received in x_kp1.
  if(isEven && procRank_-1 >= 0 )         send(*comm_,x[0],procRank_-1);
  if(isOdd && procRank_+1 < numProcs_ )   receive(*comm_,procRank_+1,x_kp1);
}

#endif  // THYRA_EXAMPLE_TRIDIAG_SPMD_LINEAR_OP_HPP

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