Ginkgo  Generated from pipelines/2837190956 branch based on develop. Ginkgo version 2.0.0
A numerical linear algebra library targeting many-core architectures
dense.hpp
1 // SPDX-FileCopyrightText: 2017 - 2026 The Ginkgo authors
2 //
3 // SPDX-License-Identifier: BSD-3-Clause
4 
5 #ifndef GKO_PUBLIC_CORE_MATRIX_DENSE_HPP_
6 #define GKO_PUBLIC_CORE_MATRIX_DENSE_HPP_
7 
8 
9 #include <initializer_list>
10 #include <type_traits>
11 
12 #include <ginkgo/core/base/array.hpp>
13 #include <ginkgo/core/base/exception_helpers.hpp>
14 #include <ginkgo/core/base/executor.hpp>
15 #include <ginkgo/core/base/lin_op.hpp>
16 #include <ginkgo/core/base/range_accessors.hpp>
17 #include <ginkgo/core/base/types.hpp>
18 #include <ginkgo/core/base/utils.hpp>
19 #include <ginkgo/core/matrix/device_views.hpp>
20 #include <ginkgo/core/matrix/permutation.hpp>
21 #include <ginkgo/core/matrix/scaled_permutation.hpp>
22 
23 
24 namespace gko {
25 namespace experimental {
26 namespace distributed {
27 
28 
29 template <typename ValueType>
30 class Vector;
31 
32 
33 namespace detail {
34 
35 
36 template <typename ValueType>
37 class VectorCache;
38 
39 
40 } // namespace detail
41 } // namespace distributed
42 } // namespace experimental
43 
44 
45 namespace matrix {
46 
47 
48 template <typename ValueType, typename IndexType>
49 class Coo;
50 
51 template <typename ValueType, typename IndexType>
52 class Csr;
53 
54 template <typename ValueType>
55 class Diagonal;
56 
57 template <typename ValueType, typename IndexType>
58 class Ell;
59 
60 template <typename ValueType, typename IndexType>
61 class Fbcsr;
62 
63 template <typename ValueType, typename IndexType>
64 class Hybrid;
65 
66 template <typename ValueType, typename IndexType>
67 class Sellp;
68 
69 template <typename ValueType, typename IndexType>
70 class SparsityCsr;
71 
72 
87 template <typename ValueType = default_precision>
88 class Dense
89  : public LinOp,
90  public EnableCloneable<Dense<ValueType>>,
91  public ConvertibleTo<Dense<next_precision<ValueType>>>,
92 #if GINKGO_ENABLE_HALF || GINKGO_ENABLE_BFLOAT16
93  public ConvertibleTo<Dense<next_precision<ValueType, 2>>>,
94 #endif
95 #if GINKGO_ENABLE_HALF && GINKGO_ENABLE_BFLOAT16
96  public ConvertibleTo<Dense<next_precision<ValueType, 3>>>,
97 #endif
98  public ConvertibleTo<Coo<ValueType, int32>>,
99  public ConvertibleTo<Coo<ValueType, int64>>,
100  public ConvertibleTo<Csr<ValueType, int32>>,
101  public ConvertibleTo<Csr<ValueType, int64>>,
102  public ConvertibleTo<Ell<ValueType, int32>>,
103  public ConvertibleTo<Ell<ValueType, int64>>,
104  public ConvertibleTo<Fbcsr<ValueType, int32>>,
105  public ConvertibleTo<Fbcsr<ValueType, int64>>,
106  public ConvertibleTo<Hybrid<ValueType, int32>>,
107  public ConvertibleTo<Hybrid<ValueType, int64>>,
108  public ConvertibleTo<Sellp<ValueType, int32>>,
109  public ConvertibleTo<Sellp<ValueType, int64>>,
110  public ConvertibleTo<SparsityCsr<ValueType, int32>>,
111  public ConvertibleTo<SparsityCsr<ValueType, int64>>,
112  public DiagonalExtractable<ValueType>,
113  public ReadableFromMatrixData<ValueType, int32>,
114  public ReadableFromMatrixData<ValueType, int64>,
115  public WritableToMatrixData<ValueType, int32>,
116  public WritableToMatrixData<ValueType, int64>,
117  public Transposable,
118  public Permutable<int32>,
119  public Permutable<int64>,
120  public EnableAbsoluteComputation<remove_complex<Dense<ValueType>>>,
121  public ScaledIdentityAddable {
122  friend class EnableCloneable<Dense>;
123  friend class Coo<ValueType, int32>;
124  friend class Coo<ValueType, int64>;
125  friend class Csr<ValueType, int32>;
126  friend class Csr<ValueType, int64>;
127  friend class Diagonal<ValueType>;
128  friend class Ell<ValueType, int32>;
129  friend class Ell<ValueType, int64>;
130  friend class Fbcsr<ValueType, int32>;
131  friend class Fbcsr<ValueType, int64>;
132  friend class Hybrid<ValueType, int32>;
133  friend class Hybrid<ValueType, int64>;
134  friend class Sellp<ValueType, int32>;
135  friend class Sellp<ValueType, int64>;
136  friend class SparsityCsr<ValueType, int32>;
137  friend class SparsityCsr<ValueType, int64>;
138  friend class Dense<to_complex<ValueType>>;
139  friend class experimental::distributed::Vector<ValueType>;
140  friend class experimental::distributed::detail::VectorCache<ValueType>;
141  GKO_ASSERT_SUPPORTED_VALUE_TYPE;
142 
143 public:
144  using EnableCloneable<Dense>::convert_to;
145  using EnableCloneable<Dense>::move_to;
146  using ConvertibleTo<Dense<next_precision<ValueType>>>::convert_to;
147  using ConvertibleTo<Dense<next_precision<ValueType>>>::move_to;
148  using ConvertibleTo<Coo<ValueType, int32>>::convert_to;
149  using ConvertibleTo<Coo<ValueType, int32>>::move_to;
150  using ConvertibleTo<Coo<ValueType, int64>>::convert_to;
151  using ConvertibleTo<Coo<ValueType, int64>>::move_to;
152  using ConvertibleTo<Csr<ValueType, int32>>::convert_to;
153  using ConvertibleTo<Csr<ValueType, int32>>::move_to;
154  using ConvertibleTo<Csr<ValueType, int64>>::convert_to;
155  using ConvertibleTo<Csr<ValueType, int64>>::move_to;
156  using ConvertibleTo<Ell<ValueType, int32>>::convert_to;
157  using ConvertibleTo<Ell<ValueType, int32>>::move_to;
158  using ConvertibleTo<Ell<ValueType, int64>>::convert_to;
159  using ConvertibleTo<Ell<ValueType, int64>>::move_to;
160  using ConvertibleTo<Fbcsr<ValueType, int32>>::convert_to;
161  using ConvertibleTo<Fbcsr<ValueType, int32>>::move_to;
162  using ConvertibleTo<Fbcsr<ValueType, int64>>::convert_to;
163  using ConvertibleTo<Fbcsr<ValueType, int64>>::move_to;
164  using ConvertibleTo<Hybrid<ValueType, int32>>::convert_to;
165  using ConvertibleTo<Hybrid<ValueType, int32>>::move_to;
166  using ConvertibleTo<Hybrid<ValueType, int64>>::convert_to;
167  using ConvertibleTo<Hybrid<ValueType, int64>>::move_to;
168  using ConvertibleTo<Sellp<ValueType, int32>>::convert_to;
169  using ConvertibleTo<Sellp<ValueType, int32>>::move_to;
170  using ConvertibleTo<Sellp<ValueType, int64>>::convert_to;
171  using ConvertibleTo<Sellp<ValueType, int64>>::move_to;
172  using ConvertibleTo<SparsityCsr<ValueType, int32>>::convert_to;
173  using ConvertibleTo<SparsityCsr<ValueType, int32>>::move_to;
174  using ConvertibleTo<SparsityCsr<ValueType, int64>>::convert_to;
175  using ConvertibleTo<SparsityCsr<ValueType, int64>>::move_to;
176  using ReadableFromMatrixData<ValueType, int32>::read;
177  using ReadableFromMatrixData<ValueType, int64>::read;
178 
179  using value_type = ValueType;
180  using index_type = int64;
181  using transposed_type = Dense<value_type>;
182  using mat_data = matrix_data<value_type, int64>;
183  using mat_data32 = matrix_data<value_type, int32>;
184  using device_mat_data = device_matrix_data<value_type, int64>;
185  using device_mat_data32 = device_matrix_data<value_type, int32>;
186  using absolute_type = remove_complex<Dense>;
187  using real_type = absolute_type;
188  using complex_type = to_complex<Dense>;
189  using device_view = matrix::view::dense<value_type>;
190  using const_device_view = matrix::view::dense<const value_type>;
191 
192  using row_major_range = gko::range<gko::accessor::row_major<ValueType, 2>>;
193 
200  static std::unique_ptr<Dense> create_with_config_of(
201  ptr_param<const Dense> other)
202  {
203  // De-referencing `other` before calling the functions (instead of
204  // using operator `->`) is currently required to be compatible with
205  // CUDA 10.1.
206  // Otherwise, it results in a compile error.
207  return (*other).create_with_same_config();
208  }
209 
221  static std::unique_ptr<Dense> create_with_type_of(
222  ptr_param<const Dense> other, std::shared_ptr<const Executor> exec,
223  const dim<2>& size = dim<2>{})
224  {
225  // See create_with_config_of()
226  return (*other).create_with_type_of_impl(exec, size, size[1]);
227  }
228 
237  static std::unique_ptr<Dense> create_with_type_of(
238  ptr_param<const Dense> other, std::shared_ptr<const Executor> exec,
239  const dim<2>& size, size_type stride)
240  {
241  // See create_with_config_of()
242  return (*other).create_with_type_of_impl(exec, size, stride);
243  }
244 
255  static std::unique_ptr<Dense> create_with_type_of(
256  ptr_param<const Dense> other, std::shared_ptr<const Executor> exec,
257  const dim<2>& size, const dim<2>& local_size, size_type stride)
258  {
259  // See create_with_config_of()
260  return (*other).create_with_type_of_impl(exec, size, stride);
261  }
262 
271  static std::unique_ptr<Dense> create_view_of(ptr_param<Dense> other)
272  {
273  return other->create_view_of_impl();
274  }
275 
283  static std::unique_ptr<const Dense> create_const_view_of(
284  ptr_param<const Dense> other)
285  {
286  return other->create_const_view_of_impl();
287  }
288 
289  friend class Dense<previous_precision<ValueType>>;
290 
291  void convert_to(Dense<next_precision<ValueType>>* result) const override;
292 
293  void move_to(Dense<next_precision<ValueType>>* result) override;
294 
295 #if GINKGO_ENABLE_HALF || GINKGO_ENABLE_BFLOAT16
296  friend class Dense<previous_precision<ValueType, 2>>;
297  using ConvertibleTo<Dense<next_precision<ValueType, 2>>>::convert_to;
298  using ConvertibleTo<Dense<next_precision<ValueType, 2>>>::move_to;
299 
300  void convert_to(Dense<next_precision<ValueType, 2>>* result) const override;
301 
302  void move_to(Dense<next_precision<ValueType, 2>>* result) override;
303 #endif
304 
305 #if GINKGO_ENABLE_HALF && GINKGO_ENABLE_BFLOAT16
306  friend class Dense<previous_precision<ValueType, 3>>;
307  using ConvertibleTo<Dense<next_precision<ValueType, 3>>>::convert_to;
308  using ConvertibleTo<Dense<next_precision<ValueType, 3>>>::move_to;
309 
310  void convert_to(Dense<next_precision<ValueType, 3>>* result) const override;
311 
312  void move_to(Dense<next_precision<ValueType, 3>>* result) override;
313 #endif
314 
315  void convert_to(Coo<ValueType, int32>* result) const override;
316 
317  void move_to(Coo<ValueType, int32>* result) override;
318 
319  void convert_to(Coo<ValueType, int64>* result) const override;
320 
321  void move_to(Coo<ValueType, int64>* result) override;
322 
323  void convert_to(Csr<ValueType, int32>* result) const override;
324 
325  void move_to(Csr<ValueType, int32>* result) override;
326 
327  void convert_to(Csr<ValueType, int64>* result) const override;
328 
329  void move_to(Csr<ValueType, int64>* result) override;
330 
331  void convert_to(Ell<ValueType, int32>* result) const override;
332 
333  void move_to(Ell<ValueType, int32>* result) override;
334 
335  void convert_to(Ell<ValueType, int64>* result) const override;
336 
337  void move_to(Ell<ValueType, int64>* result) override;
338 
339  void convert_to(Fbcsr<ValueType, int32>* result) const override;
340 
341  void move_to(Fbcsr<ValueType, int32>* result) override;
342 
343  void convert_to(Fbcsr<ValueType, int64>* result) const override;
344 
345  void move_to(Fbcsr<ValueType, int64>* result) override;
346 
347  void convert_to(Hybrid<ValueType, int32>* result) const override;
348 
349  void move_to(Hybrid<ValueType, int32>* result) override;
350 
351  void convert_to(Hybrid<ValueType, int64>* result) const override;
352 
353  void move_to(Hybrid<ValueType, int64>* result) override;
354 
355  void convert_to(Sellp<ValueType, int32>* result) const override;
356 
357  void move_to(Sellp<ValueType, int32>* result) override;
358 
359  void convert_to(Sellp<ValueType, int64>* result) const override;
360 
361  void move_to(Sellp<ValueType, int64>* result) override;
362 
363  void convert_to(SparsityCsr<ValueType, int32>* result) const override;
364 
365  void move_to(SparsityCsr<ValueType, int32>* result) override;
366 
367  void convert_to(SparsityCsr<ValueType, int64>* result) const override;
368 
369  void move_to(SparsityCsr<ValueType, int64>* result) override;
370 
371  void read(const mat_data& data) override;
372 
373  void read(const mat_data32& data) override;
374 
375  void read(const device_mat_data& data) override;
376 
377  void read(const device_mat_data32& data) override;
378 
379  void read(device_mat_data&& data) override;
380 
381  void read(device_mat_data32&& data) override;
382 
383  void write(mat_data& data) const override;
384 
385  void write(mat_data32& data) const override;
386 
387  std::unique_ptr<LinOp> transpose() const override;
388 
389  std::unique_ptr<LinOp> conj_transpose() const override;
390 
397  void transpose(ptr_param<Dense> output) const;
398 
405  void conj_transpose(ptr_param<Dense> output) const;
406 
412  void fill(const ValueType value);
413 
424  std::unique_ptr<Dense> permute(
425  ptr_param<const Permutation<int32>> permutation,
426  permute_mode mode = permute_mode::symmetric) const;
427 
431  std::unique_ptr<Dense> permute(
432  ptr_param<const Permutation<int64>> permutation,
433  permute_mode mode = permute_mode::symmetric) const;
434 
442  void permute(ptr_param<const Permutation<int32>> permutation,
443  ptr_param<Dense> output, permute_mode mode) const;
444 
449  void permute(ptr_param<const Permutation<int64>> permutation,
450  ptr_param<Dense> output, permute_mode mode) const;
451 
466  std::unique_ptr<Dense> permute(
467  ptr_param<const Permutation<int32>> row_permutation,
468  ptr_param<const Permutation<int32>> column_permutation,
469  bool invert = false) const;
470 
475  std::unique_ptr<Dense> permute(
476  ptr_param<const Permutation<int64>> row_permutation,
477  ptr_param<const Permutation<int64>> column_permutation,
478  bool invert = false) const;
479 
489  void permute(ptr_param<const Permutation<int32>> row_permutation,
490  ptr_param<const Permutation<int32>> column_permutation,
491  ptr_param<Dense> output, bool invert = false) const;
492 
497  void permute(ptr_param<const Permutation<int64>> row_permutation,
498  ptr_param<const Permutation<int64>> column_permutation,
499  ptr_param<Dense> output, bool invert = false) const;
500 
510  std::unique_ptr<Dense> scale_permute(
511  ptr_param<const ScaledPermutation<value_type, int32>> permutation,
512  permute_mode mode = permute_mode::symmetric) const;
513 
518  std::unique_ptr<Dense> scale_permute(
519  ptr_param<const ScaledPermutation<value_type, int64>> permutation,
520  permute_mode mode = permute_mode::symmetric) const;
521 
530  void scale_permute(
531  ptr_param<const ScaledPermutation<value_type, int32>> permutation,
532  ptr_param<Dense> output, permute_mode mode) const;
533 
538  void scale_permute(
539  ptr_param<const ScaledPermutation<value_type, int64>> permutation,
540  ptr_param<Dense> output, permute_mode mode) const;
541 
554  std::unique_ptr<Dense> scale_permute(
555  ptr_param<const ScaledPermutation<value_type, int32>> row_permutation,
556  ptr_param<const ScaledPermutation<value_type, int32>>
557  column_permutation,
558  bool invert = false) const;
559 
564  std::unique_ptr<Dense> scale_permute(
565  ptr_param<const ScaledPermutation<value_type, int64>> row_permutation,
566  ptr_param<const ScaledPermutation<value_type, int64>>
567  column_permutation,
568  bool invert = false) const;
569 
579  void scale_permute(
580  ptr_param<const ScaledPermutation<value_type, int32>> row_permutation,
581  ptr_param<const ScaledPermutation<value_type, int32>>
582  column_permutation,
583  ptr_param<Dense> output, bool invert = false) const;
584 
590  void scale_permute(
591  ptr_param<const ScaledPermutation<value_type, int64>> row_permutation,
592  ptr_param<const ScaledPermutation<value_type, int64>>
593  column_permutation,
594  ptr_param<Dense> output, bool invert = false) const;
595 
596  std::unique_ptr<LinOp> permute(
597  const array<int32>* permutation_indices) const override;
598 
599  std::unique_ptr<LinOp> permute(
600  const array<int64>* permutation_indices) const override;
601 
611  void permute(const array<int32>* permutation_indices,
612  ptr_param<Dense> output) const;
613 
617  void permute(const array<int64>* permutation_indices,
618  ptr_param<Dense> output) const;
619 
620  std::unique_ptr<LinOp> inverse_permute(
621  const array<int32>* permutation_indices) const override;
622 
623  std::unique_ptr<LinOp> inverse_permute(
624  const array<int64>* permutation_indices) const override;
625 
636  void inverse_permute(const array<int32>* permutation_indices,
637  ptr_param<Dense> output) const;
638 
642  void inverse_permute(const array<int64>* permutation_indices,
643  ptr_param<Dense> output) const;
644 
645  std::unique_ptr<LinOp> row_permute(
646  const array<int32>* permutation_indices) const override;
647 
648  std::unique_ptr<LinOp> row_permute(
649  const array<int64>* permutation_indices) const override;
650 
660  void row_permute(const array<int32>* permutation_indices,
661  ptr_param<Dense> output) const;
662 
666  void row_permute(const array<int64>* permutation_indices,
667  ptr_param<Dense> output) const;
668 
679  std::unique_ptr<Dense> row_gather(const array<int32>* gather_indices) const;
680 
684  std::unique_ptr<Dense> row_gather(const array<int64>* gather_indices) const;
685 
698  void row_gather(const array<int32>* gather_indices,
699  ptr_param<LinOp> row_collection) const;
700 
704  void row_gather(const array<int64>* gather_indices,
705  ptr_param<LinOp> row_collection) const;
706 
720  void row_gather(ptr_param<const LinOp> alpha,
721  const array<int32>* gather_indices,
722  ptr_param<const LinOp> beta,
723  ptr_param<LinOp> row_collection) const;
724 
729  void row_gather(ptr_param<const LinOp> alpha,
730  const array<int64>* gather_indices,
731  ptr_param<const LinOp> beta,
732  ptr_param<LinOp> row_collection) const;
733 
734  std::unique_ptr<LinOp> column_permute(
735  const array<int32>* permutation_indices) const override;
736 
737  std::unique_ptr<LinOp> column_permute(
738  const array<int64>* permutation_indices) const override;
739 
749  void column_permute(const array<int32>* permutation_indices,
750  ptr_param<Dense> output) const;
751 
755  void column_permute(const array<int64>* permutation_indices,
756  ptr_param<Dense> output) const;
757 
758  std::unique_ptr<LinOp> inverse_row_permute(
759  const array<int32>* permutation_indices) const override;
760 
761  std::unique_ptr<LinOp> inverse_row_permute(
762  const array<int64>* permutation_indices) const override;
763 
773  void inverse_row_permute(const array<int32>* permutation_indices,
774  ptr_param<Dense> output) const;
775 
779  void inverse_row_permute(const array<int64>* permutation_indices,
780  ptr_param<Dense> output) const;
781 
782  std::unique_ptr<LinOp> inverse_column_permute(
783  const array<int32>* permutation_indices) const override;
784 
785  std::unique_ptr<LinOp> inverse_column_permute(
786  const array<int64>* permutation_indices) const override;
787 
797  void inverse_column_permute(const array<int32>* permutation_indices,
798  ptr_param<Dense> output) const;
799 
803  void inverse_column_permute(const array<int64>* permutation_indices,
804  ptr_param<Dense> output) const;
805 
806  std::unique_ptr<Diagonal<ValueType>> extract_diagonal() const override;
807 
815  void extract_diagonal(ptr_param<Diagonal<ValueType>> output) const;
816 
817  std::unique_ptr<absolute_type> compute_absolute() const override;
818 
826  void compute_absolute(ptr_param<absolute_type> output) const;
827 
828  void compute_absolute_inplace() override;
829 
834  std::unique_ptr<complex_type> make_complex() const;
835 
841  void make_complex(ptr_param<complex_type> result) const;
842 
847  std::unique_ptr<real_type> get_real() const;
848 
852  void get_real(ptr_param<real_type> result) const;
853 
858  std::unique_ptr<real_type> get_imag() const;
859 
864  void get_imag(ptr_param<real_type> result) const;
865 
871  value_type* get_values() noexcept { return values_.get_data(); }
872 
880  const value_type* get_const_values() const noexcept
881  {
882  return values_.get_const_data();
883  }
884 
890  size_type get_stride() const noexcept { return stride_; }
891 
897  size_type get_num_stored_elements() const noexcept
898  {
899  return values_.get_size();
900  }
901 
902  device_view get_device_view();
903 
904  const_device_view get_const_device_view() const;
905 
916  value_type& at(size_type row, size_type col) noexcept
917  {
918  return values_.get_data()[linearize_index(row, col)];
919  }
920 
924  value_type at(size_type row, size_type col) const noexcept
925  {
926  return values_.get_const_data()[linearize_index(row, col)];
927  }
928 
943  ValueType& at(size_type idx) noexcept
944  {
945  return values_.get_data()[linearize_index(idx)];
946  }
947 
951  ValueType at(size_type idx) const noexcept
952  {
953  return values_.get_const_data()[linearize_index(idx)];
954  }
955 
965  void scale(ptr_param<const LinOp> alpha);
966 
976  void inv_scale(ptr_param<const LinOp> alpha);
977 
978  void validate_data() const override;
979 
990  void add_scaled(ptr_param<const LinOp> alpha, ptr_param<const LinOp> b);
991 
1002  void sub_scaled(ptr_param<const LinOp> alpha, ptr_param<const LinOp> b);
1003 
1012  void compute_dot(ptr_param<const LinOp> b, ptr_param<LinOp> result) const;
1013 
1025  void compute_dot(ptr_param<const LinOp> b, ptr_param<LinOp> result,
1026  array<char>& tmp) const;
1027 
1036  void compute_conj_dot(ptr_param<const LinOp> b,
1037  ptr_param<LinOp> result) const;
1038 
1050  void compute_conj_dot(ptr_param<const LinOp> b, ptr_param<LinOp> result,
1051  array<char>& tmp) const;
1052 
1060  void compute_norm2(ptr_param<LinOp> result) const;
1061 
1072  void compute_norm2(ptr_param<LinOp> result, array<char>& tmp) const;
1073 
1081  void compute_norm1(ptr_param<LinOp> result) const;
1082 
1093  void compute_norm1(ptr_param<LinOp> result, array<char>& tmp) const;
1094 
1103  void compute_squared_norm2(ptr_param<LinOp> result) const;
1104 
1116  void compute_squared_norm2(ptr_param<LinOp> result, array<char>& tmp) const;
1117 
1125  void compute_mean(ptr_param<LinOp> result) const;
1126 
1137  void compute_mean(ptr_param<LinOp> result, array<char>& tmp) const;
1138 
1149  std::unique_ptr<Dense> create_submatrix(const span& rows,
1150  const span& columns,
1151  const size_type stride)
1152  {
1153  return this->create_submatrix_impl(rows, columns, stride);
1154  }
1155 
1162  std::unique_ptr<Dense> create_submatrix(const span& rows,
1163  const span& columns)
1164  {
1165  return create_submatrix(rows, columns, this->get_stride());
1166  }
1167 
1168 
1177  std::unique_ptr<Dense> create_submatrix(const local_span& rows,
1178  const local_span& columns,
1179  dim<2> size)
1180  {
1181  dim<2> deduced_size{rows.length(), columns.length()};
1182  GKO_ASSERT_EQUAL_DIMENSIONS(deduced_size, size);
1183  return create_submatrix(rows, columns, this->get_stride());
1184  }
1185 
1193  std::unique_ptr<real_type> create_real_view();
1194 
1198  std::unique_ptr<const real_type> create_real_view() const;
1199 
1212  static std::unique_ptr<Dense> create(std::shared_ptr<const Executor> exec,
1213  const dim<2>& size = {},
1214  size_type stride = 0);
1215 
1232  static std::unique_ptr<Dense> create(std::shared_ptr<const Executor> exec,
1233  const dim<2>& size,
1234  array<value_type> values,
1235  size_type stride);
1236 
1241  template <typename InputValueType>
1242  GKO_DEPRECATED(
1243  "explicitly construct the gko::array argument instead of passing an"
1244  "initializer list")
1245  static std::unique_ptr<Dense> create(
1246  std::shared_ptr<const Executor> exec, const dim<2>& size,
1247  std::initializer_list<InputValueType> values, size_type stride)
1248  {
1249  return create(exec, size, array<value_type>{exec, std::move(values)},
1250  stride);
1251  }
1252 
1264  static std::unique_ptr<const Dense> create_const(
1265  std::shared_ptr<const Executor> exec, const dim<2>& size,
1266  gko::detail::const_array_view<ValueType>&& values, size_type stride);
1267 
1273  Dense& operator=(const Dense&);
1274 
1280  Dense& operator=(Dense&&);
1281 
1286  Dense(const Dense&);
1287 
1292  Dense(Dense&&);
1293 
1294 protected:
1295  Dense(std::shared_ptr<const Executor> exec, const dim<2>& size = {},
1296  size_type stride = 0);
1297 
1298  Dense(std::shared_ptr<const Executor> exec, const dim<2>& size,
1299  array<value_type> values, size_type stride);
1300 
1307  virtual std::unique_ptr<Dense> create_with_same_config() const
1308  {
1309  return Dense::create(this->get_executor(), this->get_size(),
1310  this->get_stride());
1311  }
1312 
1320  virtual std::unique_ptr<Dense> create_with_type_of_impl(
1321  std::shared_ptr<const Executor> exec, const dim<2>& size,
1322  size_type stride) const
1323  {
1324  return Dense::create(exec, size, stride);
1325  }
1326 
1333  virtual std::unique_ptr<Dense> create_view_of_impl()
1334  {
1335  auto exec = this->get_executor();
1336  return Dense::create(
1337  exec, this->get_size(),
1338  gko::make_array_view(exec, this->get_num_stored_elements(),
1339  this->get_values()),
1340  this->get_stride());
1341  }
1342 
1349  virtual std::unique_ptr<const Dense> create_const_view_of_impl() const
1350  {
1351  auto exec = this->get_executor();
1352  return Dense::create_const(
1353  exec, this->get_size(),
1354  gko::make_const_array_view(exec, this->get_num_stored_elements(),
1355  this->get_const_values()),
1356  this->get_stride());
1357  }
1358 
1359  template <typename IndexType>
1360  void convert_impl(Coo<ValueType, IndexType>* result) const;
1361 
1362  template <typename IndexType>
1363  void convert_impl(Csr<ValueType, IndexType>* result) const;
1364 
1365  template <typename IndexType>
1366  void convert_impl(Ell<ValueType, IndexType>* result) const;
1367 
1368  template <typename IndexType>
1369  void convert_impl(Fbcsr<ValueType, IndexType>* result) const;
1370 
1371  template <typename IndexType>
1372  void convert_impl(Hybrid<ValueType, IndexType>* result) const;
1373 
1374  template <typename IndexType>
1375  void convert_impl(Sellp<ValueType, IndexType>* result) const;
1376 
1377  template <typename IndexType>
1378  void convert_impl(SparsityCsr<ValueType, IndexType>* result) const;
1379 
1386  virtual void scale_impl(const LinOp* alpha);
1387 
1394  virtual void inv_scale_impl(const LinOp* alpha);
1395 
1402  virtual void add_scaled_impl(const LinOp* alpha, const LinOp* b);
1403 
1410  virtual void sub_scaled_impl(const LinOp* alpha, const LinOp* b);
1411 
1418  virtual void compute_dot_impl(const LinOp* b, LinOp* result) const;
1419 
1426  virtual void compute_conj_dot_impl(const LinOp* b, LinOp* result) const;
1427 
1434  virtual void compute_norm2_impl(LinOp* result) const;
1435 
1442  virtual void compute_norm1_impl(LinOp* result) const;
1443 
1450  virtual void compute_squared_norm2_impl(LinOp* result) const;
1451 
1455  virtual void compute_mean_impl(LinOp* result) const;
1456 
1465  void resize(gko::dim<2> new_size);
1466 
1474  virtual std::unique_ptr<Dense> create_submatrix_impl(
1475  const span& rows, const span& columns, const size_type stride);
1476 
1477  void apply_impl(const LinOp* b, LinOp* x) const override;
1478 
1479  void apply_impl(const LinOp* alpha, const LinOp* b, const LinOp* beta,
1480  LinOp* x) const override;
1481 
1482  size_type linearize_index(size_type row, size_type col) const noexcept
1483  {
1484  return row * stride_ + col;
1485  }
1486 
1487  size_type linearize_index(size_type idx) const noexcept
1488  {
1489  return linearize_index(idx / this->get_size()[1],
1490  idx % this->get_size()[1]);
1491  }
1492 
1493  template <typename IndexType>
1494  void permute_impl(const Permutation<IndexType>* permutation,
1495  permute_mode mode, Dense* output) const;
1496 
1497  template <typename IndexType>
1498  void permute_impl(const Permutation<IndexType>* row_permutation,
1499  const Permutation<IndexType>* col_permutation,
1500  bool invert, Dense* output) const;
1501 
1502  template <typename IndexType>
1503  void scale_permute_impl(
1504  const ScaledPermutation<ValueType, IndexType>* permutation,
1505  permute_mode mode, Dense* output) const;
1506 
1507  template <typename IndexType>
1508  void scale_permute_impl(
1509  const ScaledPermutation<ValueType, IndexType>* row_permutation,
1510  const ScaledPermutation<ValueType, IndexType>* column_permutation,
1511  bool invert, Dense* output) const;
1512 
1513  template <typename OutputType, typename IndexType>
1514  void row_gather_impl(const array<IndexType>* row_idxs,
1515  Dense<OutputType>* row_collection) const;
1516 
1517  template <typename OutputType, typename IndexType>
1518  void row_gather_impl(const Dense<ValueType>* alpha,
1519  const array<IndexType>* row_idxs,
1520  const Dense<ValueType>* beta,
1521  Dense<OutputType>* row_collection) const;
1522 
1523 private:
1524  size_type stride_;
1525  array<value_type> values_;
1526 
1527  void add_scaled_identity_impl(const LinOp* a, const LinOp* b) override;
1528 };
1529 
1530 
1531 } // namespace matrix
1532 
1533 
1534 namespace detail {
1535 
1536 
1537 template <typename ValueType>
1538 struct temporary_clone_helper<matrix::Dense<ValueType>> {
1539  static std::unique_ptr<matrix::Dense<ValueType>> create(
1540  std::shared_ptr<const Executor> exec, matrix::Dense<ValueType>* ptr,
1541  bool copy_data)
1542  {
1543  if (copy_data) {
1544  return gko::clone(std::move(exec), ptr);
1545  } else {
1546  return matrix::Dense<ValueType>::create(exec, ptr->get_size());
1547  }
1548  }
1549 };
1550 
1551 
1552 } // namespace detail
1553 
1554 
1562 template <typename VecPtr>
1563 std::unique_ptr<matrix::Dense<typename detail::pointee<VecPtr>::value_type>>
1564 make_dense_view(VecPtr&& vector)
1565 {
1566  using value_type = typename detail::pointee<VecPtr>::value_type;
1567  return matrix::Dense<value_type>::create_view_of(vector);
1568 }
1569 
1570 
1578 template <typename VecPtr>
1579 std::unique_ptr<
1580  const matrix::Dense<typename detail::pointee<VecPtr>::value_type>>
1581 make_const_dense_view(VecPtr&& vector)
1582 {
1583  using value_type = typename detail::pointee<VecPtr>::value_type;
1584  return matrix::Dense<value_type>::create_const_view_of(vector);
1585 }
1586 
1587 
1608 template <typename Matrix, typename... TArgs>
1609 std::unique_ptr<Matrix> initialize(
1610  size_type stride, std::initializer_list<typename Matrix::value_type> vals,
1611  std::shared_ptr<const Executor> exec, TArgs&&... create_args)
1612 {
1613  using dense = matrix::Dense<typename Matrix::value_type>;
1614  size_type num_rows = vals.size();
1615  auto tmp = dense::create(exec->get_master(), dim<2>{num_rows, 1}, stride);
1616  size_type idx = 0;
1617  for (const auto& elem : vals) {
1618  tmp->at(idx) = elem;
1619  ++idx;
1620  }
1621  auto mtx = Matrix::create(exec, std::forward<TArgs>(create_args)...);
1622  tmp->move_to(mtx);
1623  return mtx;
1624 }
1625 
1646 template <typename Matrix, typename... TArgs>
1647 std::unique_ptr<Matrix> initialize(
1648  std::initializer_list<typename Matrix::value_type> vals,
1649  std::shared_ptr<const Executor> exec, TArgs&&... create_args)
1650 {
1651  return initialize<Matrix>(1, vals, std::move(exec),
1652  std::forward<TArgs>(create_args)...);
1653 }
1654 
1655 
1676 template <typename Matrix, typename... TArgs>
1677 std::unique_ptr<Matrix> initialize(
1678  size_type stride,
1679  std::initializer_list<std::initializer_list<typename Matrix::value_type>>
1680  vals,
1681  std::shared_ptr<const Executor> exec, TArgs&&... create_args)
1682 {
1683  using dense = matrix::Dense<typename Matrix::value_type>;
1684  size_type num_rows = vals.size();
1685  size_type num_cols = num_rows > 0 ? begin(vals)->size() : 1;
1686  auto tmp =
1687  dense::create(exec->get_master(), dim<2>{num_rows, num_cols}, stride);
1688  size_type ridx = 0;
1689  for (const auto& row : vals) {
1690  size_type cidx = 0;
1691  for (const auto& elem : row) {
1692  tmp->at(ridx, cidx) = elem;
1693  ++cidx;
1694  }
1695  ++ridx;
1696  }
1697  auto mtx = Matrix::create(exec, std::forward<TArgs>(create_args)...);
1698  tmp->move_to(mtx);
1699  return mtx;
1700 }
1701 
1702 
1724 template <typename Matrix, typename... TArgs>
1725 std::unique_ptr<Matrix> initialize(
1726  std::initializer_list<std::initializer_list<typename Matrix::value_type>>
1727  vals,
1728  std::shared_ptr<const Executor> exec, TArgs&&... create_args)
1729 {
1730  return initialize<Matrix>(vals.size() > 0 ? begin(vals)->size() : 0, vals,
1731  std::move(exec),
1732  std::forward<TArgs>(create_args)...);
1733 }
1734 
1735 
1736 } // namespace gko
1737 
1738 
1739 #endif // GKO_PUBLIC_CORE_MATRIX_DENSE_HPP_
gko::matrix::Dense::permute
std::unique_ptr< Dense > permute(ptr_param< const Permutation< int32 >> permutation, permute_mode mode=permute_mode::symmetric) const
Creates a permuted copy of this matrix with the given permutation .
gko::matrix::Dense::inverse_column_permute
std::unique_ptr< LinOp > inverse_column_permute(const array< int32 > *permutation_indices) const override
Returns a LinOp representing the row permutation of the inverse permuted object.
gko::matrix::Dense::add_scaled
void add_scaled(ptr_param< const LinOp > alpha, ptr_param< const LinOp > b)
Adds b scaled by alpha to the matrix (aka: BLAS axpy).
gko::matrix::Fbcsr
Fixed-block compressed sparse row storage matrix format.
Definition: csr.hpp:47
gko::matrix::Csr
CSR is a matrix format which stores only the nonzero coefficients by compressing each row of the matr...
Definition: matrix.hpp:30
gko::matrix::permute_mode::rows
The rows will be permuted.
gko::matrix::Dense::transpose
std::unique_ptr< LinOp > transpose() const override
Returns a LinOp representing the transpose of the Transposable object.
gko::matrix::permute_mode::columns
The columns will be permuted.
gko::ReadableFromMatrixData< ValueType, int32 >::read
virtual void read(const matrix_data< ValueType, int32 > &data)=0
Reads a matrix from a matrix_data structure.
gko::matrix::Dense
Dense is a matrix format which explicitly stores all values of the matrix.
Definition: dense_cache.hpp:28
gko::matrix::SparsityCsr
SparsityCsr is a matrix format which stores only the sparsity pattern of a sparse matrix by compressi...
Definition: csr.hpp:41
gko::matrix::Dense::at
ValueType at(size_type idx) const noexcept
Returns a single element of the matrix.
Definition: dense.hpp:951
gko::matrix::Dense::row_gather
std::unique_ptr< Dense > row_gather(const array< int32 > *gather_indices) const
Create a Dense matrix consisting of the given rows from this matrix.
gko::matrix::Dense::make_complex
std::unique_ptr< complex_type > make_complex() const
Creates a complex copy of the original matrix.
gko::matrix::Dense::create
static std::unique_ptr< Dense > create(std::shared_ptr< const Executor > exec, const dim< 2 > &size={}, size_type stride=0)
Creates an uninitialized Dense matrix of the specified size.
gko::matrix::Dense::create_with_type_of
static std::unique_ptr< Dense > create_with_type_of(ptr_param< const Dense > other, std::shared_ptr< const Executor > exec, const dim< 2 > &size, size_type stride)
Definition: dense.hpp:237
gko::matrix::Dense::scale
void scale(ptr_param< const LinOp > alpha)
Scales the matrix with a scalar (aka: BLAS scal).
gko::matrix::ScaledPermutation
ScaledPermutation is a matrix combining a permutation with scaling factors.
Definition: scaled_permutation.hpp:35
gko::size_type
std::size_t size_type
Integral type used for allocation quantities.
Definition: types.hpp:101
gko::matrix::Dense::get_real
std::unique_ptr< real_type > get_real() const
Creates a new real matrix and extracts the real part of the original matrix into that.
gko::matrix::Dense::inv_scale
void inv_scale(ptr_param< const LinOp > alpha)
Scales the matrix with the inverse of a scalar.
gko::matrix::Permutation
Permutation is a matrix format that represents a permutation matrix, i.e.
Definition: permutation.hpp:110
gko::matrix::Dense::fill
void fill(const ValueType value)
Fill the dense matrix with a given value.
gko::make_const_array_view
detail::const_array_view< ValueType > make_const_array_view(std::shared_ptr< const Executor > exec, size_type size, const ValueType *data)
Helper function to create a const array view deducing the value type.
Definition: array.hpp:819
gko::matrix::Dense::compute_dot
void compute_dot(ptr_param< const LinOp > b, ptr_param< LinOp > result) const
Computes the column-wise dot product of this matrix and b.
gko::matrix::Dense::create_submatrix
std::unique_ptr< Dense > create_submatrix(const span &rows, const span &columns, const size_type stride)
Create a submatrix from the original matrix.
Definition: dense.hpp:1149
gko::matrix::Dense::create_with_config_of
static std::unique_ptr< Dense > create_with_config_of(ptr_param< const Dense > other)
Creates a Dense matrix with the same size and stride as another Dense matrix.
Definition: dense.hpp:200
gko::initialize
std::unique_ptr< Matrix > initialize(size_type stride, std::initializer_list< typename Matrix::value_type > vals, std::shared_ptr< const Executor > exec, TArgs &&... create_args)
Creates and initializes a column-vector.
Definition: dense.hpp:1609
gko::clone
detail::cloned_type< Pointer > clone(const Pointer &p)
Creates a unique clone of the object pointed to by p.
Definition: utils_helper.hpp:425
gko::matrix::Dense::at
value_type & at(size_type row, size_type col) noexcept
Returns a single element of the matrix.
Definition: dense.hpp:916
gko::matrix::Dense::get_stride
size_type get_stride() const noexcept
Returns the stride of the matrix.
Definition: dense.hpp:890
gko::matrix::Dense::create_view_of
static std::unique_ptr< Dense > create_view_of(ptr_param< Dense > other)
Creates a Dense matrix, where the underlying array is a view of another Dense matrix' array.
Definition: dense.hpp:271
gko::range
A range is a multidimensional view of the memory.
Definition: range.hpp:302
gko
The Ginkgo namespace.
Definition: abstract_factory.hpp:19
gko::matrix::Dense::get_num_stored_elements
size_type get_num_stored_elements() const noexcept
Returns the number of elements explicitly stored in the matrix.
Definition: dense.hpp:897
gko::matrix::Dense::sub_scaled
void sub_scaled(ptr_param< const LinOp > alpha, ptr_param< const LinOp > b)
Subtracts b scaled by alpha from the matrix (aka: BLAS axpy).
gko::matrix::Dense::get_values
value_type * get_values() noexcept
Returns a pointer to the array of values of the matrix.
Definition: dense.hpp:871
gko::array
An array is a container which encapsulates fixed-sized arrays, stored on the Executor tied to the arr...
Definition: array.hpp:26
gko::matrix::Dense::compute_norm2
void compute_norm2(ptr_param< LinOp > result) const
Computes the column-wise Euclidean (L^2) norm of this matrix.
gko::matrix::Dense::row_permute
std::unique_ptr< LinOp > row_permute(const array< int32 > *permutation_indices) const override
Returns a LinOp representing the row permutation of the Permutable object.
gko::matrix::Dense::create_with_type_of
static std::unique_ptr< Dense > create_with_type_of(ptr_param< const Dense > other, std::shared_ptr< const Executor > exec, const dim< 2 > &size=dim< 2 >{})
Creates a Dense matrix with the same type as another Dense matrix but on a different executor and wit...
Definition: dense.hpp:221
gko::span
A span is a lightweight structure used to create sub-ranges from other ranges.
Definition: range.hpp:45
gko::dim< 2 >
gko::matrix::Diagonal
This class is a utility which efficiently implements the diagonal matrix (a linear operator which sca...
Definition: lin_op.hpp:31
gko::matrix::Dense::create_const
static std::unique_ptr< const Dense > create_const(std::shared_ptr< const Executor > exec, const dim< 2 > &size, gko::detail::const_array_view< ValueType > &&values, size_type stride)
Creates a constant (immutable) Dense matrix from a constant array.
gko::ptr_param
This class is used for function parameters in the place of raw pointers.
Definition: utils_helper.hpp:43
gko::matrix::Dense::at
value_type at(size_type row, size_type col) const noexcept
Returns a single element of the matrix.
Definition: dense.hpp:924
gko::array::get_data
value_type * get_data() noexcept
Returns a pointer to the block of memory used to store the elements of the array.
Definition: array.hpp:686
gko::matrix::Dense::validate_data
void validate_data() const override
Throws gko::InvalidData exception if we found the data inside the object does not fulfill certain pro...
gko::matrix::Dense::compute_mean
void compute_mean(ptr_param< LinOp > result) const
Computes the column-wise arithmetic mean of this matrix.
gko::matrix::Dense::inverse_row_permute
std::unique_ptr< LinOp > inverse_row_permute(const array< int32 > *permutation_indices) const override
Returns a LinOp representing the row permutation of the inverse permuted object.
gko::matrix::permute_mode::symmetric
The rows and columns will be permuted.
gko::matrix::Dense::get_imag
std::unique_ptr< real_type > get_imag() const
Creates a new real matrix and extracts the imaginary part of the original matrix into that.
gko::matrix::Dense::compute_squared_norm2
void compute_squared_norm2(ptr_param< LinOp > result) const
Computes the square of the column-wise Euclidean (L^2) norm of this matrix.
gko::matrix::Dense::operator=
Dense & operator=(const Dense &)
Copy-assigns a Dense matrix.
gko::matrix::Dense::inverse_permute
std::unique_ptr< LinOp > inverse_permute(const array< int32 > *permutation_indices) const override
Returns a LinOp representing the symmetric inverse row and column permutation of the Permutable objec...
gko::matrix::Dense::create_with_type_of
static std::unique_ptr< Dense > create_with_type_of(ptr_param< const Dense > other, std::shared_ptr< const Executor > exec, const dim< 2 > &size, const dim< 2 > &local_size, size_type stride)
Definition: dense.hpp:255
gko::stop::mode
mode
The mode for the residual norm criterion.
Definition: residual_norm.hpp:37
gko::matrix::Dense::scale_permute
std::unique_ptr< Dense > scale_permute(ptr_param< const ScaledPermutation< value_type, int32 >> permutation, permute_mode mode=permute_mode::symmetric) const
Creates a scaled and permuted copy of this matrix.
gko::matrix::Dense::compute_norm1
void compute_norm1(ptr_param< LinOp > result) const
Computes the column-wise (L^1) norm of this matrix.
gko::matrix::Dense::get_const_values
const value_type * get_const_values() const noexcept
Returns a pointer to the array of values of the matrix.
Definition: dense.hpp:880
gko::make_array_view
array< ValueType > make_array_view(std::shared_ptr< const Executor > exec, size_type size, ValueType *data)
Helper function to create an array view deducing the value type.
Definition: array.hpp:800
gko::matrix::Dense::extract_diagonal
std::unique_ptr< Diagonal< ValueType > > extract_diagonal() const override
Extracts the diagonal entries of the matrix into a vector.
gko::next_precision
typename detail::find_precision_impl< T, step >::type next_precision
Obtains the next move type of T in the singly-linked precision corresponding bfloat16/half.
Definition: math.hpp:465
gko::matrix::Dense::compute_absolute_inplace
void compute_absolute_inplace() override
Compute absolute inplace on each element.
gko::matrix::Dense::column_permute
std::unique_ptr< LinOp > column_permute(const array< int32 > *permutation_indices) const override
Returns a LinOp representing the column permutation of the Permutable object.
gko::EnableCloneable::convert_to
void convert_to(result_type *result) const override
Converts the implementer to an object of type result_type.
Definition: polymorphic_object.hpp:411
gko::previous_precision
typename detail::find_precision_impl< T, -step >::type previous_precision
Obtains the previous move type of T in the singly-linked precision corresponding bfloat16/half.
Definition: math.hpp:472
gko::matrix::Dense::create_const_view_of
static std::unique_ptr< const Dense > create_const_view_of(ptr_param< const Dense > other)
Creates a immutable Dense matrix, where the underlying array is a view of another Dense matrix' array...
Definition: dense.hpp:283
gko::matrix::Dense::at
ValueType & at(size_type idx) noexcept
Returns a single element of the matrix.
Definition: dense.hpp:943
gko::matrix::Dense::conj_transpose
std::unique_ptr< LinOp > conj_transpose() const override
Returns a LinOp representing the conjugate transpose of the Transposable object.
gko::int64
std::int64_t int64
64-bit signed integral type.
Definition: types.hpp:124
gko::matrix::Dense::compute_conj_dot
void compute_conj_dot(ptr_param< const LinOp > b, ptr_param< LinOp > result) const
Computes the column-wise dot product of conj(this matrix) and b.
gko::matrix::Ell
ELL is a matrix format where stride with explicit zeros is used such that all rows have the same numb...
Definition: csr.hpp:32
gko::ConvertibleTo
ConvertibleTo interface is used to mark that the implementer can be converted to the object of Result...
Definition: polymorphic_object.hpp:147
gko::matrix::Dense::create_submatrix
std::unique_ptr< Dense > create_submatrix(const span &rows, const span &columns)
Create a submatrix from the original matrix.
Definition: dense.hpp:1162
gko::int32
std::int32_t int32
32-bit signed integral type.
Definition: types.hpp:118
gko::matrix::Dense::compute_absolute
std::unique_ptr< absolute_type > compute_absolute() const override
Gets the AbsoluteLinOp.
gko::make_dense_view
std::unique_ptr< matrix::Dense< typename detail::pointee< VecPtr >::value_type > > make_dense_view(VecPtr &&vector)
Creates a view of a given Dense vector.
Definition: dense.hpp:1564
gko::Executor
The first step in using the Ginkgo library consists of creating an executor.
Definition: executor.hpp:615
gko::matrix::Hybrid
HYBRID is a matrix format which splits the matrix into ELLPACK and COO format.
Definition: coo.hpp:32
gko::array::get_const_data
const value_type * get_const_data() const noexcept
Returns a constant pointer to the block of memory used to store the elements of the array.
Definition: array.hpp:695
gko::matrix::permute_mode
permute_mode
Specifies how a permutation will be applied to a matrix.
Definition: permutation.hpp:42
gko::matrix::Dense::Dense
Dense(const Dense &)
Copy-constructs a Dense matrix.
gko::matrix::Sellp
SELL-P is a matrix format similar to ELL format.
Definition: csr.hpp:38
gko::make_const_dense_view
std::unique_ptr< const matrix::Dense< typename detail::pointee< VecPtr >::value_type > > make_const_dense_view(VecPtr &&vector)
Creates a view of a given Dense vector.
Definition: dense.hpp:1581
gko::EnableCloneable::move_to
void move_to(result_type *result) override
Converts the implementer to an object of type result_type by moving data from this object.
Definition: polymorphic_object.hpp:413
gko::PolymorphicObject::get_executor
std::shared_ptr< const Executor > get_executor() const noexcept
Returns the Executor of the object.
Definition: polymorphic_object.hpp:69
gko::array::get_size
size_type get_size() const noexcept
Returns the number of elements in the array.
Definition: array.hpp:669
gko::LinOp::get_size
const dim< 2 > & get_size() const noexcept
Returns the size of the operator.
Definition: lin_op.hpp:169
gko::local_span
A span that is used exclusively for local numbering.
Definition: range.hpp:137
gko::matrix::Dense::create_submatrix
std::unique_ptr< Dense > create_submatrix(const local_span &rows, const local_span &columns, dim< 2 > size)
Create a submatrix from the original matrix.
Definition: dense.hpp:1177
gko::matrix::Dense::create_real_view
std::unique_ptr< real_type > create_real_view()
Create a real view of the (potentially) complex original matrix.
gko::LinOp::LinOp
LinOp(const LinOp &)=default
Copy-constructs a LinOp.
gko::to_complex
typename detail::to_complex_s< T >::type to_complex
Obtain the type which adds the complex of complex/scalar type or the template parameter of class by a...
Definition: math.hpp:282
gko::matrix::Coo
COO stores a matrix in the coordinate matrix format.
Definition: coo.hpp:49
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