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kronmult4_pbatched.hpp
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kronmult4_pbatched.hpp
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#ifndef KRONMULT4_PBATCHED_HPP
#define KRONMULT4_PBATCHED_HPP 1
#include "kroncommon.hpp"
#include "kronmult4.hpp"
// --------------------------------------------------------------------
// Performs Y(:,k) += kron(A1(k),...,A4(k)) * X(:,k), k=1:batchCount
// Note result in Y but X and W may be modified as temporary work space
// --------------------------------------------------------------------
template<typename T>
GLOBAL_FUNCTION
void kronmult4_pbatched(
int const n,
T const Aarray_[],
T* pX_[],
T* pY_[],
T* pW_[],
int const batchCount)
//
// conceptual shape of Aarray is (n,n,6,batchCount)
//
// pX_[] is array of pointers to X[], each of size n^4
// pY_[] is array of pointers to Y[], each of size n^4
// pW_[] is array of pointers to Z[], each of size n^4
//
// Y is the output
// X is the input (but may be modified)
// W is workspace
//
//
{
#ifdef USE_GPU
// -------------------------------------------
// note 1-based matlab convention for indexing
// -------------------------------------------
int const iz_start = blockIdx.x + 1;
int const iz_size = gridDim.x;
expect( gridDim.y == 1);
expect( gridDim.z == 1);
#else
int const iz_start = 1;
int const iz_size = 1;
#endif
auto Aarray = [&] (int const i1,
int const i2,
int const i3,
int const i4) -> T const & {
return( Aarray_[ indx4f(i1,i2,i3,i4, n,n,4 ) ] );
};
#ifndef USE_GPU
#pragma omp parallel for
#endif
for(int ibatch=iz_start; ibatch <= batchCount; ibatch += iz_size) {
T* const Xp = pX_[ (ibatch-1) ];
T* const Yp = pY_[ (ibatch-1) ];
T* const Wp = pW_[ (ibatch-1) ];
T const * const A1 = &(Aarray(1,1,1,ibatch));
T const * const A2 = &(Aarray(1,1,2,ibatch));
T const * const A3 = &(Aarray(1,1,3,ibatch));
T const * const A4 = &(Aarray(1,1,4,ibatch));
int const nvec = 1;
kronmult4( n, nvec, A1,A2,A3,A4, Xp, Yp, Wp );
};
}
#endif