int test_crs_matrix_test_singlevec(int numRows, int numCols, int nnz, int test, const char* filename, const bool binaryfile) {
typedef Kokkos::CrsMatrix<Scalar,int,execution_space,void,int> matrix_type ;
typedef typename Kokkos::View<Scalar*,Kokkos::LayoutLeft,execution_space> mv_type;
typedef typename Kokkos::View<Scalar*,Kokkos::LayoutLeft,execution_space,Kokkos::MemoryRandomAccess > mv_random_read_type;
typedef typename mv_type::HostMirror h_mv_type;
Scalar* val = NULL;
int* row = NULL;
int* col = NULL;
srand(17312837);
if(filename==NULL)
nnz = SparseMatrix_generate<Scalar,int>(numRows,numCols,nnz,nnz/numRows*0.2,numRows*0.01,val,row,col);
else
if(!binaryfile)
nnz = SparseMatrix_MatrixMarket_read<Scalar,int>(filename,numRows,numCols,nnz,val,row,col);
else
nnz = SparseMatrix_ReadBinaryGraph<Scalar,int>(filename,numRows,numCols,nnz,val,row,col);
matrix_type A("CRS::A",numRows,numCols,nnz,val,row,col,false);
mv_type x("X",numCols);
mv_random_read_type t_x(x);
mv_type y("Y",numRows);
h_mv_type h_x = Kokkos::create_mirror_view(x);
h_mv_type h_y = Kokkos::create_mirror_view(y);
h_mv_type h_y_compare = Kokkos::create_mirror(y);
typename matrix_type::StaticCrsGraphType::HostMirror h_graph = Kokkos::create_mirror(A.graph);
typename matrix_type::values_type::HostMirror h_values = Kokkos::create_mirror_view(A.values);
//Kokkos::deep_copy(h_graph.row_map,A.graph.row_map);
//h_a(k) = (Scalar) (1.0*(rand()%40)-20.);
for(int i=0; i<numCols;i++) {
h_x(i) = (Scalar) (1.0*(rand()%40)-20.);
h_y(i) = (Scalar) (1.0*(rand()%40)-20.);
}
for(int i=0;i<numRows;i++) {
int start = h_graph.row_map(i);
int end = h_graph.row_map(i+1);
for(int j=start;j<end;j++) {
h_values(j) = h_graph.entries(j) + i;
}
h_y_compare(i) = 0;
for(int j=start;j<end;j++) {
Scalar val = h_graph.entries(j) + i;
int idx = h_graph.entries(j);
h_y_compare(i)+=val*h_x(idx);
}
}
Kokkos::deep_copy(x,h_x);
Kokkos::deep_copy(y,h_y);
Kokkos::deep_copy(A.graph.entries,h_graph.entries);
Kokkos::deep_copy(A.values,h_values);
/*for(int i=0;i<numRows;i++)
for(int k = 0; k<numVecs; k++) {
//error[k]+=(h_y_compare(i,k)-h_y(i,k))*(h_y_compare(i,k)-h_y(i,k));
printf("%i %i %lf %lf %lf\n",i,k,h_y_compare(i,k),h_y(i,k),h_x(i,k));
}*/
typename Kokkos::CrsMatrix<Scalar,int,execution_space,void,int>::values_type x1("X1",numCols);
typename Kokkos::CrsMatrix<Scalar,int,execution_space,void,int>::values_type y1("Y1",numRows);
#ifdef NEWKERNEL
KokkosSparse::spmv("N",1.0,A,x1,0.0,y1);
#else
Kokkos::MV_Multiply(y1,A,x1);
#endif
#ifdef NEWKERNEL
KokkosSparse::spmv("N",1.0,A,x,0.0,y);
#else
Kokkos::MV_Multiply(y,A,x);
#endif
execution_space::fence();
Kokkos::deep_copy(h_y,y);
Scalar error = 0;
Scalar sum = 0;
for(int i=0;i<numRows;i++) {
error+=(h_y_compare(i)-h_y(i))*(h_y_compare(i)-h_y(i));
sum += h_y_compare(i)*h_y_compare(i);
// printf("%i %i %lf %lf %lf\n",i,k,h_y_compare(i,k),h_y(i,k),h_x(i,k));
}
//for(int i=0;i<A.nnz;i++) printf("%i %lf\n",h_graph.entries(i),h_values(i));
int num_errors = 0;
double total_error = 0;
double total_sum = 0;
num_errors += (error/(sum==0?1:sum))>1e-5?1:0;
total_error += error;
total_sum += sum;
int loop = 100;
Kokkos::Impl::Timer timer;
for(int i=0;i<loop;i++)
#ifdef NEWKERNEL
KokkosSparse::spmv("N",1.0,A,x,0.0,y);
#else
Kokkos::MV_Multiply(y,A,x);
#endif
execution_space::fence();
double time = timer.seconds();
double matrix_size = 1.0*((nnz*(sizeof(Scalar)+sizeof(int)) + numRows*sizeof(int)))/1024/1024;
double vector_size = 2.0*numRows*sizeof(Scalar)/1024/1024;
double vector_readwrite = (nnz+numCols)*sizeof(Scalar)/1024/1024;
double problem_size = matrix_size+vector_size;
printf("%i %i %i %i %6.2lf MB %6.2lf GB/s %6.2lf GFlop/s %6.3lf ms %i\n",nnz, numRows,numCols,1,problem_size,(matrix_size+vector_readwrite)/time*loop/1024, 2.0*nnz*loop/time/1e9, time/loop*1000, num_errors);
return (int)total_error;
}
int test_crs_matrix_test_singlevec(int numRows, int numCols, int nnz, int test, const char* filename, const bool binaryfile) {
typedef KokkosArray::CrsMatrix<Scalar,int,device_type> matrix_type ;
typedef typename KokkosArray::View<Scalar*,KokkosArray::LayoutLeft,device_type> mv_type;
typedef typename KokkosArray::View<Scalar*,KokkosArray::LayoutLeft,device_type,KokkosArray::MemoryRandomRead> mv_random_read_type;
typedef typename mv_type::HostMirror h_mv_type;
Scalar* val = NULL;
int* row = NULL;
int* col = NULL;
srand(17312837);
if(filename==NULL)
nnz = SparseMatrix_generate<Scalar,int>(numRows,numCols,nnz,nnz/numRows*0.2,numRows*0.01,val,row,col);
else
if(!binaryfile)
nnz = SparseMatrix_MatrixMarket_read<Scalar,int>(filename,numRows,numCols,nnz,val,row,col);
else
nnz = SparseMatrix_ReadBinaryGraph<Scalar,int>(filename,numRows,numCols,nnz,val,row,col);
matrix_type A("CRS::A",numRows,numCols,nnz,val,row,col,false);
mv_type x("X",numCols);
mv_random_read_type t_x(x);
mv_type y("Y",numRows);
h_mv_type h_x = KokkosArray::create_mirror_view(x);
h_mv_type h_y = KokkosArray::create_mirror_view(y);
h_mv_type h_y_compare = KokkosArray::create_mirror(y);
typename matrix_type::CrsArrayType::HostMirror h_graph = KokkosArray::create_mirror(A.graph);
typename matrix_type::values_type::HostMirror h_values = KokkosArray::create_mirror_view(A.values);
//KokkosArray::deep_copy(h_graph.row_map,A.graph.row_map);
//h_a(k) = (Scalar) (1.0*(rand()%40)-20.);
for(int i=0; i<numCols;i++) {
h_x(i) = (Scalar) (1.0*(rand()%40)-20.);
h_y(i) = (Scalar) (1.0*(rand()%40)-20.);
}
for(int i=0;i<numRows;i++) {
int start = h_graph.row_map(i);
int end = h_graph.row_map(i+1);
for(int j=start;j<end;j++) {
h_values(j) = h_graph.entries(j) + i;
}
h_y_compare(i) = 0;
for(int j=start;j<end;j++) {
Scalar val = h_graph.entries(j) + i;
int idx = h_graph.entries(j);
h_y_compare(i)+=val*h_x(idx);
}
}
KokkosArray::deep_copy(x,h_x);
KokkosArray::deep_copy(y,h_y);
KokkosArray::deep_copy(A.graph.entries,h_graph.entries);
KokkosArray::deep_copy(A.values,h_values);
/*for(int i=0;i<numRows;i++)
for(int k = 0; k<numVecs; k++) {
//error[k]+=(h_y_compare(i,k)-h_y(i,k))*(h_y_compare(i,k)-h_y(i,k));
printf("%i %i %lf %lf %lf\n",i,k,h_y_compare(i,k),h_y(i,k),h_x(i,k));
}*/
typename KokkosArray::CrsMatrix<Scalar,int,device_type>::values_type x1("X1",numCols);
typename KokkosArray::CrsMatrix<Scalar,int,device_type>::values_type y1("Y1",numRows);
KokkosArray::MV_Multiply(0.0,y1,1.0,A,x1);
KokkosArray::MV_Multiply(0.0,y,1.0,A,x);
device_type::fence();
KokkosArray::deep_copy(h_y,y);
Scalar error = 0;
Scalar sum = 0;
for(int i=0;i<numRows;i++) {
error+=(h_y_compare(i)-h_y(i))*(h_y_compare(i)-h_y(i));
sum += h_y_compare(i)*h_y_compare(i);
// printf("%i %i %lf %lf %lf\n",i,k,h_y_compare(i,k),h_y(i,k),h_x(i,k));
}
//for(int i=0;i<A.nnz;i++) printf("%i %lf\n",h_graph.entries(i),h_values(i));
int num_errors = 0;
double total_error = 0;
double total_sum = 0;
num_errors += (error/(sum==0?1:sum))>1e-5?1:0;
total_error += error;
total_sum += sum;
int loop = 10;
timespec starttime,endtime;
clock_gettime(CLOCK_REALTIME,&starttime);
for(int i=0;i<loop;i++)
KokkosArray::MV_Multiply(0.0,y,1.0,A,t_x);
device_type::fence();
clock_gettime(CLOCK_REALTIME,&endtime);
double time = endtime.tv_sec - starttime.tv_sec + 1.0 * (endtime.tv_nsec - starttime.tv_nsec) / 1000000000;
double matrix_size = 1.0*((nnz*(sizeof(Scalar)+sizeof(int)) + numRows*sizeof(int)))/1024/1024;
double vector_size = 2.0*numRows*sizeof(Scalar)/1024/1024;
double vector_readwrite = 2.0*nnz*sizeof(Scalar)/1024/1024;
double problem_size = matrix_size+vector_size;
printf("%i %i %i %i %6.2lf MB %6.2lf GB/s %6.2lf ms %i\n",nnz, numRows,numCols,1,problem_size,(matrix_size+vector_readwrite)/time*loop/1024, time/loop*1000, num_errors);
return (int)total_error;
}
