Ginkgo  Generated from pipelines/1571899447 branch based on develop. Ginkgo version 1.9.0
A numerical linear algebra library targeting many-core architectures
dense.hpp
1 // SPDX-FileCopyrightText: 2017 - 2024 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/permutation.hpp>
20 #include <ginkgo/core/matrix/scaled_permutation.hpp>
21 
22 
23 namespace gko {
24 namespace experimental {
25 namespace distributed {
26 
27 
28 template <typename ValueType>
29 class Vector;
30 
31 
32 namespace detail {
33 
34 
35 template <typename ValueType>
36 class VectorCache;
37 
38 
39 } // namespace detail
40 } // namespace distributed
41 } // namespace experimental
42 
43 
44 namespace matrix {
45 
46 
47 template <typename ValueType, typename IndexType>
48 class Coo;
49 
50 template <typename ValueType, typename IndexType>
51 class Csr;
52 
53 template <typename ValueType>
54 class Diagonal;
55 
56 template <typename ValueType, typename IndexType>
57 class Ell;
58 
59 template <typename ValueType, typename IndexType>
60 class Fbcsr;
61 
62 template <typename ValueType, typename IndexType>
63 class Hybrid;
64 
65 template <typename ValueType, typename IndexType>
66 class Sellp;
67 
68 template <typename ValueType, typename IndexType>
69 class SparsityCsr;
70 
71 
87 template <typename ValueType = default_precision>
88 class Dense
89  : public EnableLinOp<Dense<ValueType>>,
90  public ConvertibleTo<Dense<next_precision<ValueType>>>,
91 #if GINKGO_ENABLE_HALF
92  public ConvertibleTo<Dense<next_precision<next_precision<ValueType>>>>,
93 #endif
94  public ConvertibleTo<Coo<ValueType, int32>>,
95  public ConvertibleTo<Coo<ValueType, int64>>,
96  public ConvertibleTo<Csr<ValueType, int32>>,
97  public ConvertibleTo<Csr<ValueType, int64>>,
98  public ConvertibleTo<Ell<ValueType, int32>>,
99  public ConvertibleTo<Ell<ValueType, int64>>,
100  public ConvertibleTo<Fbcsr<ValueType, int32>>,
101  public ConvertibleTo<Fbcsr<ValueType, int64>>,
102  public ConvertibleTo<Hybrid<ValueType, int32>>,
103  public ConvertibleTo<Hybrid<ValueType, int64>>,
104  public ConvertibleTo<Sellp<ValueType, int32>>,
105  public ConvertibleTo<Sellp<ValueType, int64>>,
106  public ConvertibleTo<SparsityCsr<ValueType, int32>>,
107  public ConvertibleTo<SparsityCsr<ValueType, int64>>,
108  public DiagonalExtractable<ValueType>,
109  public ReadableFromMatrixData<ValueType, int32>,
110  public ReadableFromMatrixData<ValueType, int64>,
111  public WritableToMatrixData<ValueType, int32>,
112  public WritableToMatrixData<ValueType, int64>,
113  public Transposable,
114  public Permutable<int32>,
115  public Permutable<int64>,
116  public EnableAbsoluteComputation<remove_complex<Dense<ValueType>>>,
117  public ScaledIdentityAddable {
118  friend class EnablePolymorphicObject<Dense, LinOp>;
119  friend class Coo<ValueType, int32>;
120  friend class Coo<ValueType, int64>;
121  friend class Csr<ValueType, int32>;
122  friend class Csr<ValueType, int64>;
123  friend class Diagonal<ValueType>;
124  friend class Ell<ValueType, int32>;
125  friend class Ell<ValueType, int64>;
126  friend class Fbcsr<ValueType, int32>;
127  friend class Fbcsr<ValueType, int64>;
128  friend class Hybrid<ValueType, int32>;
129  friend class Hybrid<ValueType, int64>;
130  friend class Sellp<ValueType, int32>;
131  friend class Sellp<ValueType, int64>;
132  friend class SparsityCsr<ValueType, int32>;
133  friend class SparsityCsr<ValueType, int64>;
134  friend class Dense<to_complex<ValueType>>;
135  friend class experimental::distributed::Vector<ValueType>;
136  friend class experimental::distributed::detail::VectorCache<ValueType>;
137 
138 public:
139  using EnableLinOp<Dense>::convert_to;
140  using EnableLinOp<Dense>::move_to;
141  using ConvertibleTo<Dense<next_precision<ValueType>>>::convert_to;
142  using ConvertibleTo<Dense<next_precision<ValueType>>>::move_to;
143  using ConvertibleTo<Coo<ValueType, int32>>::convert_to;
144  using ConvertibleTo<Coo<ValueType, int32>>::move_to;
145  using ConvertibleTo<Coo<ValueType, int64>>::convert_to;
146  using ConvertibleTo<Coo<ValueType, int64>>::move_to;
147  using ConvertibleTo<Csr<ValueType, int32>>::convert_to;
148  using ConvertibleTo<Csr<ValueType, int32>>::move_to;
149  using ConvertibleTo<Csr<ValueType, int64>>::convert_to;
150  using ConvertibleTo<Csr<ValueType, int64>>::move_to;
151  using ConvertibleTo<Ell<ValueType, int32>>::convert_to;
152  using ConvertibleTo<Ell<ValueType, int32>>::move_to;
153  using ConvertibleTo<Ell<ValueType, int64>>::convert_to;
154  using ConvertibleTo<Ell<ValueType, int64>>::move_to;
155  using ConvertibleTo<Fbcsr<ValueType, int32>>::convert_to;
156  using ConvertibleTo<Fbcsr<ValueType, int32>>::move_to;
157  using ConvertibleTo<Fbcsr<ValueType, int64>>::convert_to;
158  using ConvertibleTo<Fbcsr<ValueType, int64>>::move_to;
159  using ConvertibleTo<Hybrid<ValueType, int32>>::convert_to;
160  using ConvertibleTo<Hybrid<ValueType, int32>>::move_to;
161  using ConvertibleTo<Hybrid<ValueType, int64>>::convert_to;
162  using ConvertibleTo<Hybrid<ValueType, int64>>::move_to;
163  using ConvertibleTo<Sellp<ValueType, int32>>::convert_to;
164  using ConvertibleTo<Sellp<ValueType, int32>>::move_to;
165  using ConvertibleTo<Sellp<ValueType, int64>>::convert_to;
166  using ConvertibleTo<Sellp<ValueType, int64>>::move_to;
167  using ConvertibleTo<SparsityCsr<ValueType, int32>>::convert_to;
168  using ConvertibleTo<SparsityCsr<ValueType, int32>>::move_to;
169  using ConvertibleTo<SparsityCsr<ValueType, int64>>::convert_to;
170  using ConvertibleTo<SparsityCsr<ValueType, int64>>::move_to;
171  using ReadableFromMatrixData<ValueType, int32>::read;
172  using ReadableFromMatrixData<ValueType, int64>::read;
173 
174  using value_type = ValueType;
175  using index_type = int64;
176  using transposed_type = Dense<ValueType>;
177  using mat_data = matrix_data<ValueType, int64>;
178  using mat_data32 = matrix_data<ValueType, int32>;
179  using device_mat_data = device_matrix_data<ValueType, int64>;
180  using device_mat_data32 = device_matrix_data<ValueType, int32>;
181  using absolute_type = remove_complex<Dense>;
182  using real_type = absolute_type;
183  using complex_type = to_complex<Dense>;
184 
185  using row_major_range = gko::range<gko::accessor::row_major<ValueType, 2>>;
186 
193  static std::unique_ptr<Dense> create_with_config_of(
194  ptr_param<const Dense> other)
195  {
196  // De-referencing `other` before calling the functions (instead of
197  // using operator `->`) is currently required to be compatible with
198  // CUDA 10.1.
199  // Otherwise, it results in a compile error.
200  return (*other).create_with_same_config();
201  }
202 
214  static std::unique_ptr<Dense> create_with_type_of(
215  ptr_param<const Dense> other, std::shared_ptr<const Executor> exec,
216  const dim<2>& size = dim<2>{})
217  {
218  // See create_with_config_of()
219  return (*other).create_with_type_of_impl(exec, size, size[1]);
220  }
221 
230  static std::unique_ptr<Dense> create_with_type_of(
231  ptr_param<const Dense> other, std::shared_ptr<const Executor> exec,
232  const dim<2>& size, size_type stride)
233  {
234  // See create_with_config_of()
235  return (*other).create_with_type_of_impl(exec, size, stride);
236  }
237 
248  static std::unique_ptr<Dense> create_with_type_of(
249  ptr_param<const Dense> other, std::shared_ptr<const Executor> exec,
250  const dim<2>& size, const dim<2>& local_size, size_type stride)
251  {
252  // See create_with_config_of()
253  return (*other).create_with_type_of_impl(exec, size, stride);
254  }
255 
264  static std::unique_ptr<Dense> create_view_of(ptr_param<Dense> other)
265  {
266  return other->create_view_of_impl();
267  }
268 
276  static std::unique_ptr<const Dense> create_const_view_of(
277  ptr_param<const Dense> other)
278  {
279  return other->create_const_view_of_impl();
280  }
281 
282  friend class Dense<previous_precision<ValueType>>;
283 
284  void convert_to(Dense<next_precision<ValueType>>* result) const override;
285 
286  void move_to(Dense<next_precision<ValueType>>* result) override;
287 
288 #if GINKGO_ENABLE_HALF
289  friend class Dense<previous_precision<previous_precision<ValueType>>>;
290  using ConvertibleTo<
291  Dense<next_precision<next_precision<ValueType>>>>::convert_to;
292  using ConvertibleTo<
293  Dense<next_precision<next_precision<ValueType>>>>::move_to;
294 
295  void convert_to(Dense<next_precision<next_precision<ValueType>>>* result)
296  const override;
297 
298  void move_to(
299  Dense<next_precision<next_precision<ValueType>>>* result) override;
300 #endif
301 
302  void convert_to(Coo<ValueType, int32>* result) const override;
303 
304  void move_to(Coo<ValueType, int32>* result) override;
305 
306  void convert_to(Coo<ValueType, int64>* result) const override;
307 
308  void move_to(Coo<ValueType, int64>* result) override;
309 
310  void convert_to(Csr<ValueType, int32>* result) const override;
311 
312  void move_to(Csr<ValueType, int32>* result) override;
313 
314  void convert_to(Csr<ValueType, int64>* result) const override;
315 
316  void move_to(Csr<ValueType, int64>* result) override;
317 
318  void convert_to(Ell<ValueType, int32>* result) const override;
319 
320  void move_to(Ell<ValueType, int32>* result) override;
321 
322  void convert_to(Ell<ValueType, int64>* result) const override;
323 
324  void move_to(Ell<ValueType, int64>* result) override;
325 
326  void convert_to(Fbcsr<ValueType, int32>* result) const override;
327 
328  void move_to(Fbcsr<ValueType, int32>* result) override;
329 
330  void convert_to(Fbcsr<ValueType, int64>* result) const override;
331 
332  void move_to(Fbcsr<ValueType, int64>* result) override;
333 
334  void convert_to(Hybrid<ValueType, int32>* result) const override;
335 
336  void move_to(Hybrid<ValueType, int32>* result) override;
337 
338  void convert_to(Hybrid<ValueType, int64>* result) const override;
339 
340  void move_to(Hybrid<ValueType, int64>* result) override;
341 
342  void convert_to(Sellp<ValueType, int32>* result) const override;
343 
344  void move_to(Sellp<ValueType, int32>* result) override;
345 
346  void convert_to(Sellp<ValueType, int64>* result) const override;
347 
348  void move_to(Sellp<ValueType, int64>* result) override;
349 
350  void convert_to(SparsityCsr<ValueType, int32>* result) const override;
351 
352  void move_to(SparsityCsr<ValueType, int32>* result) override;
353 
354  void convert_to(SparsityCsr<ValueType, int64>* result) const override;
355 
356  void move_to(SparsityCsr<ValueType, int64>* result) override;
357 
358  void read(const mat_data& data) override;
359 
360  void read(const mat_data32& data) override;
361 
362  void read(const device_mat_data& data) override;
363 
364  void read(const device_mat_data32& data) override;
365 
366  void read(device_mat_data&& data) override;
367 
368  void read(device_mat_data32&& data) override;
369 
370  void write(mat_data& data) const override;
371 
372  void write(mat_data32& data) const override;
373 
374  std::unique_ptr<LinOp> transpose() const override;
375 
376  std::unique_ptr<LinOp> conj_transpose() const override;
377 
384  void transpose(ptr_param<Dense> output) const;
385 
392  void conj_transpose(ptr_param<Dense> output) const;
393 
399  void fill(const ValueType value);
400 
415  std::unique_ptr<Dense> permute(
416  ptr_param<const Permutation<int32>> permutation,
417  permute_mode mode = permute_mode::symmetric) const;
418 
422  std::unique_ptr<Dense> permute(
423  ptr_param<const Permutation<int64>> permutation,
424  permute_mode mode = permute_mode::symmetric) const;
425 
431  void permute(ptr_param<const Permutation<int32>> permutation,
432  ptr_param<Dense> output, permute_mode mode) const;
433 
438  void permute(ptr_param<const Permutation<int64>> permutation,
439  ptr_param<Dense> output, permute_mode mode) const;
440 
454  std::unique_ptr<Dense> permute(
455  ptr_param<const Permutation<int32>> row_permutation,
456  ptr_param<const Permutation<int32>> column_permutation,
457  bool invert = false) const;
458 
463  std::unique_ptr<Dense> permute(
464  ptr_param<const Permutation<int64>> row_permutation,
465  ptr_param<const Permutation<int64>> column_permutation,
466  bool invert = false) const;
467 
474  void permute(ptr_param<const Permutation<int32>> row_permutation,
475  ptr_param<const Permutation<int32>> column_permutation,
476  ptr_param<Dense> output, bool invert = false) const;
477 
482  void permute(ptr_param<const Permutation<int64>> row_permutation,
483  ptr_param<const Permutation<int64>> column_permutation,
484  ptr_param<Dense> output, bool invert = false) const;
485 
495  std::unique_ptr<Dense> scale_permute(
496  ptr_param<const ScaledPermutation<value_type, int32>> permutation,
497  permute_mode mode = permute_mode::symmetric) const;
498 
503  std::unique_ptr<Dense> scale_permute(
504  ptr_param<const ScaledPermutation<value_type, int64>> permutation,
505  permute_mode mode = permute_mode::symmetric) const;
506 
513  void scale_permute(
514  ptr_param<const ScaledPermutation<value_type, int32>> permutation,
515  ptr_param<Dense> output, permute_mode mode) const;
516 
521  void scale_permute(
522  ptr_param<const ScaledPermutation<value_type, int64>> permutation,
523  ptr_param<Dense> output, permute_mode mode) const;
524 
537  std::unique_ptr<Dense> scale_permute(
538  ptr_param<const ScaledPermutation<value_type, int32>> row_permutation,
539  ptr_param<const ScaledPermutation<value_type, int32>>
540  column_permutation,
541  bool invert = false) const;
542 
547  std::unique_ptr<Dense> scale_permute(
548  ptr_param<const ScaledPermutation<value_type, int64>> row_permutation,
549  ptr_param<const ScaledPermutation<value_type, int64>>
550  column_permutation,
551  bool invert = false) const;
552 
559  void scale_permute(
560  ptr_param<const ScaledPermutation<value_type, int32>> row_permutation,
561  ptr_param<const ScaledPermutation<value_type, int32>>
562  column_permutation,
563  ptr_param<Dense> output, bool invert = false) const;
564 
570  void scale_permute(
571  ptr_param<const ScaledPermutation<value_type, int64>> row_permutation,
572  ptr_param<const ScaledPermutation<value_type, int64>>
573  column_permutation,
574  ptr_param<Dense> output, bool invert = false) const;
575 
576  std::unique_ptr<LinOp> permute(
577  const array<int32>* permutation_indices) const override;
578 
579  std::unique_ptr<LinOp> permute(
580  const array<int64>* permutation_indices) const override;
581 
591  void permute(const array<int32>* permutation_indices,
592  ptr_param<Dense> output) const;
593 
597  void permute(const array<int64>* permutation_indices,
598  ptr_param<Dense> output) const;
599 
600  std::unique_ptr<LinOp> inverse_permute(
601  const array<int32>* permutation_indices) const override;
602 
603  std::unique_ptr<LinOp> inverse_permute(
604  const array<int64>* permutation_indices) const override;
605 
616  void inverse_permute(const array<int32>* permutation_indices,
617  ptr_param<Dense> output) const;
618 
622  void inverse_permute(const array<int64>* permutation_indices,
623  ptr_param<Dense> output) const;
624 
625  std::unique_ptr<LinOp> row_permute(
626  const array<int32>* permutation_indices) const override;
627 
628  std::unique_ptr<LinOp> row_permute(
629  const array<int64>* permutation_indices) const override;
630 
640  void row_permute(const array<int32>* permutation_indices,
641  ptr_param<Dense> output) const;
642 
646  void row_permute(const array<int64>* permutation_indices,
647  ptr_param<Dense> output) const;
648 
659  std::unique_ptr<Dense> row_gather(const array<int32>* gather_indices) const;
660 
664  std::unique_ptr<Dense> row_gather(const array<int64>* gather_indices) const;
665 
678  void row_gather(const array<int32>* gather_indices,
679  ptr_param<LinOp> row_collection) const;
680 
684  void row_gather(const array<int64>* gather_indices,
685  ptr_param<LinOp> row_collection) const;
686 
700  void row_gather(ptr_param<const LinOp> alpha,
701  const array<int32>* gather_indices,
702  ptr_param<const LinOp> beta,
703  ptr_param<LinOp> row_collection) const;
704 
709  void row_gather(ptr_param<const LinOp> alpha,
710  const array<int64>* gather_indices,
711  ptr_param<const LinOp> beta,
712  ptr_param<LinOp> row_collection) const;
713 
714  std::unique_ptr<LinOp> column_permute(
715  const array<int32>* permutation_indices) const override;
716 
717  std::unique_ptr<LinOp> column_permute(
718  const array<int64>* permutation_indices) const override;
719 
729  void column_permute(const array<int32>* permutation_indices,
730  ptr_param<Dense> output) const;
731 
735  void column_permute(const array<int64>* permutation_indices,
736  ptr_param<Dense> output) const;
737 
738  std::unique_ptr<LinOp> inverse_row_permute(
739  const array<int32>* permutation_indices) const override;
740 
741  std::unique_ptr<LinOp> inverse_row_permute(
742  const array<int64>* permutation_indices) const override;
743 
753  void inverse_row_permute(const array<int32>* permutation_indices,
754  ptr_param<Dense> output) const;
755 
759  void inverse_row_permute(const array<int64>* permutation_indices,
760  ptr_param<Dense> output) const;
761 
762  std::unique_ptr<LinOp> inverse_column_permute(
763  const array<int32>* permutation_indices) const override;
764 
765  std::unique_ptr<LinOp> inverse_column_permute(
766  const array<int64>* permutation_indices) const override;
767 
777  void inverse_column_permute(const array<int32>* permutation_indices,
778  ptr_param<Dense> output) const;
779 
783  void inverse_column_permute(const array<int64>* permutation_indices,
784  ptr_param<Dense> output) const;
785 
786  std::unique_ptr<Diagonal<ValueType>> extract_diagonal() const override;
787 
795  void extract_diagonal(ptr_param<Diagonal<ValueType>> output) const;
796 
797  std::unique_ptr<absolute_type> compute_absolute() const override;
798 
806  void compute_absolute(ptr_param<absolute_type> output) const;
807 
808  void compute_absolute_inplace() override;
809 
814  std::unique_ptr<complex_type> make_complex() const;
815 
821  void make_complex(ptr_param<complex_type> result) const;
822 
827  std::unique_ptr<real_type> get_real() const;
828 
832  void get_real(ptr_param<real_type> result) const;
833 
838  std::unique_ptr<real_type> get_imag() const;
839 
844  void get_imag(ptr_param<real_type> result) const;
845 
851  value_type* get_values() noexcept { return values_.get_data(); }
852 
860  const value_type* get_const_values() const noexcept
861  {
862  return values_.get_const_data();
863  }
864 
870  size_type get_stride() const noexcept { return stride_; }
871 
877  size_type get_num_stored_elements() const noexcept
878  {
879  return values_.get_size();
880  }
881 
892  value_type& at(size_type row, size_type col) noexcept
893  {
894  return values_.get_data()[linearize_index(row, col)];
895  }
896 
900  value_type at(size_type row, size_type col) const noexcept
901  {
902  return values_.get_const_data()[linearize_index(row, col)];
903  }
904 
919  ValueType& at(size_type idx) noexcept
920  {
921  return values_.get_data()[linearize_index(idx)];
922  }
923 
927  ValueType at(size_type idx) const noexcept
928  {
929  return values_.get_const_data()[linearize_index(idx)];
930  }
931 
941  void scale(ptr_param<const LinOp> alpha);
942 
952  void inv_scale(ptr_param<const LinOp> alpha);
953 
964  void add_scaled(ptr_param<const LinOp> alpha, ptr_param<const LinOp> b);
965 
976  void sub_scaled(ptr_param<const LinOp> alpha, ptr_param<const LinOp> b);
977 
986  void compute_dot(ptr_param<const LinOp> b, ptr_param<LinOp> result) const;
987 
999  void compute_dot(ptr_param<const LinOp> b, ptr_param<LinOp> result,
1000  array<char>& tmp) const;
1001 
1010  void compute_conj_dot(ptr_param<const LinOp> b,
1011  ptr_param<LinOp> result) const;
1012 
1024  void compute_conj_dot(ptr_param<const LinOp> b, ptr_param<LinOp> result,
1025  array<char>& tmp) const;
1026 
1034  void compute_norm2(ptr_param<LinOp> result) const;
1035 
1046  void compute_norm2(ptr_param<LinOp> result, array<char>& tmp) const;
1047 
1055  void compute_norm1(ptr_param<LinOp> result) const;
1056 
1067  void compute_norm1(ptr_param<LinOp> result, array<char>& tmp) const;
1068 
1077  void compute_squared_norm2(ptr_param<LinOp> result) const;
1078 
1090  void compute_squared_norm2(ptr_param<LinOp> result, array<char>& tmp) const;
1091 
1099  void compute_mean(ptr_param<LinOp> result) const;
1100 
1111  void compute_mean(ptr_param<LinOp> result, array<char>& tmp) const;
1112 
1123  std::unique_ptr<Dense> create_submatrix(const span& rows,
1124  const span& columns,
1125  const size_type stride)
1126  {
1127  return this->create_submatrix_impl(rows, columns, stride);
1128  }
1129 
1136  std::unique_ptr<Dense> create_submatrix(const span& rows,
1137  const span& columns)
1138  {
1139  return create_submatrix(rows, columns, this->get_stride());
1140  }
1141 
1149  std::unique_ptr<real_type> create_real_view();
1150 
1154  std::unique_ptr<const real_type> create_real_view() const;
1155 
1168  static std::unique_ptr<Dense> create(std::shared_ptr<const Executor> exec,
1169  const dim<2>& size = {},
1170  size_type stride = 0);
1171 
1188  static std::unique_ptr<Dense> create(std::shared_ptr<const Executor> exec,
1189  const dim<2>& size,
1190  array<value_type> values,
1191  size_type stride);
1192 
1197  template <typename InputValueType>
1198  GKO_DEPRECATED(
1199  "explicitly construct the gko::array argument instead of passing an"
1200  "initializer list")
1201  static std::unique_ptr<Dense> create(
1202  std::shared_ptr<const Executor> exec, const dim<2>& size,
1203  std::initializer_list<InputValueType> values, size_type stride)
1204  {
1205  return create(exec, size, array<value_type>{exec, std::move(values)},
1206  stride);
1207  }
1208 
1220  static std::unique_ptr<const Dense> create_const(
1221  std::shared_ptr<const Executor> exec, const dim<2>& size,
1222  gko::detail::const_array_view<ValueType>&& values, size_type stride);
1223 
1229  Dense& operator=(const Dense&);
1230 
1236  Dense& operator=(Dense&&);
1237 
1242  Dense(const Dense&);
1243 
1248  Dense(Dense&&);
1249 
1250 protected:
1251  Dense(std::shared_ptr<const Executor> exec, const dim<2>& size = {},
1252  size_type stride = 0);
1253 
1254  Dense(std::shared_ptr<const Executor> exec, const dim<2>& size,
1255  array<value_type> values, size_type stride);
1256 
1263  virtual std::unique_ptr<Dense> create_with_same_config() const
1264  {
1265  return Dense::create(this->get_executor(), this->get_size(),
1266  this->get_stride());
1267  }
1268 
1276  virtual std::unique_ptr<Dense> create_with_type_of_impl(
1277  std::shared_ptr<const Executor> exec, const dim<2>& size,
1278  size_type stride) const
1279  {
1280  return Dense::create(exec, size, stride);
1281  }
1282 
1289  virtual std::unique_ptr<Dense> create_view_of_impl()
1290  {
1291  auto exec = this->get_executor();
1292  return Dense::create(
1293  exec, this->get_size(),
1294  gko::make_array_view(exec, this->get_num_stored_elements(),
1295  this->get_values()),
1296  this->get_stride());
1297  }
1298 
1305  virtual std::unique_ptr<const Dense> create_const_view_of_impl() const
1306  {
1307  auto exec = this->get_executor();
1308  return Dense::create_const(
1309  exec, this->get_size(),
1310  gko::make_const_array_view(exec, this->get_num_stored_elements(),
1311  this->get_const_values()),
1312  this->get_stride());
1313  }
1314 
1315  template <typename IndexType>
1316  void convert_impl(Coo<ValueType, IndexType>* result) const;
1317 
1318  template <typename IndexType>
1319  void convert_impl(Csr<ValueType, IndexType>* result) const;
1320 
1321  template <typename IndexType>
1322  void convert_impl(Ell<ValueType, IndexType>* result) const;
1323 
1324  template <typename IndexType>
1325  void convert_impl(Fbcsr<ValueType, IndexType>* result) const;
1326 
1327  template <typename IndexType>
1328  void convert_impl(Hybrid<ValueType, IndexType>* result) const;
1329 
1330  template <typename IndexType>
1331  void convert_impl(Sellp<ValueType, IndexType>* result) const;
1332 
1333  template <typename IndexType>
1334  void convert_impl(SparsityCsr<ValueType, IndexType>* result) const;
1335 
1342  virtual void scale_impl(const LinOp* alpha);
1343 
1350  virtual void inv_scale_impl(const LinOp* alpha);
1351 
1358  virtual void add_scaled_impl(const LinOp* alpha, const LinOp* b);
1359 
1366  virtual void sub_scaled_impl(const LinOp* alpha, const LinOp* b);
1367 
1374  virtual void compute_dot_impl(const LinOp* b, LinOp* result) const;
1375 
1382  virtual void compute_conj_dot_impl(const LinOp* b, LinOp* result) const;
1383 
1390  virtual void compute_norm2_impl(LinOp* result) const;
1391 
1398  virtual void compute_norm1_impl(LinOp* result) const;
1399 
1406  virtual void compute_squared_norm2_impl(LinOp* result) const;
1407 
1411  virtual void compute_mean_impl(LinOp* result) const;
1412 
1421  void resize(gko::dim<2> new_size);
1422 
1430  virtual std::unique_ptr<Dense> create_submatrix_impl(
1431  const span& rows, const span& columns, const size_type stride);
1432 
1433  void apply_impl(const LinOp* b, LinOp* x) const override;
1434 
1435  void apply_impl(const LinOp* alpha, const LinOp* b, const LinOp* beta,
1436  LinOp* x) const override;
1437 
1438  size_type linearize_index(size_type row, size_type col) const noexcept
1439  {
1440  return row * stride_ + col;
1441  }
1442 
1443  size_type linearize_index(size_type idx) const noexcept
1444  {
1445  return linearize_index(idx / this->get_size()[1],
1446  idx % this->get_size()[1]);
1447  }
1448 
1449  template <typename IndexType>
1450  void permute_impl(const Permutation<IndexType>* permutation,
1451  permute_mode mode, Dense* output) const;
1452 
1453  template <typename IndexType>
1454  void permute_impl(const Permutation<IndexType>* row_permutation,
1455  const Permutation<IndexType>* col_permutation,
1456  bool invert, Dense* output) const;
1457 
1458  template <typename IndexType>
1459  void scale_permute_impl(
1460  const ScaledPermutation<ValueType, IndexType>* permutation,
1461  permute_mode mode, Dense* output) const;
1462 
1463  template <typename IndexType>
1464  void scale_permute_impl(
1465  const ScaledPermutation<ValueType, IndexType>* row_permutation,
1466  const ScaledPermutation<ValueType, IndexType>* column_permutation,
1467  bool invert, Dense* output) const;
1468 
1469  template <typename OutputType, typename IndexType>
1470  void row_gather_impl(const array<IndexType>* row_idxs,
1471  Dense<OutputType>* row_collection) const;
1472 
1473  template <typename OutputType, typename IndexType>
1474  void row_gather_impl(const Dense<ValueType>* alpha,
1475  const array<IndexType>* row_idxs,
1476  const Dense<ValueType>* beta,
1477  Dense<OutputType>* row_collection) const;
1478 
1479 private:
1480  size_type stride_;
1481  array<value_type> values_;
1482 
1483  void add_scaled_identity_impl(const LinOp* a, const LinOp* b) override;
1484 };
1485 
1486 
1487 } // namespace matrix
1488 
1489 
1490 namespace detail {
1491 
1492 
1493 template <typename ValueType>
1494 struct temporary_clone_helper<matrix::Dense<ValueType>> {
1495  static std::unique_ptr<matrix::Dense<ValueType>> create(
1496  std::shared_ptr<const Executor> exec, matrix::Dense<ValueType>* ptr,
1497  bool copy_data)
1498  {
1499  if (copy_data) {
1500  return gko::clone(std::move(exec), ptr);
1501  } else {
1502  return matrix::Dense<ValueType>::create(exec, ptr->get_size());
1503  }
1504  }
1505 };
1506 
1507 
1508 } // namespace detail
1509 
1510 
1518 template <typename VecPtr>
1519 std::unique_ptr<matrix::Dense<typename detail::pointee<VecPtr>::value_type>>
1520 make_dense_view(VecPtr&& vector)
1521 {
1522  using value_type = typename detail::pointee<VecPtr>::value_type;
1523  return matrix::Dense<value_type>::create_view_of(vector);
1524 }
1525 
1526 
1534 template <typename VecPtr>
1535 std::unique_ptr<
1536  const matrix::Dense<typename detail::pointee<VecPtr>::value_type>>
1537 make_const_dense_view(VecPtr&& vector)
1538 {
1539  using value_type = typename detail::pointee<VecPtr>::value_type;
1540  return matrix::Dense<value_type>::create_const_view_of(vector);
1541 }
1542 
1543 
1564 template <typename Matrix, typename... TArgs>
1565 std::unique_ptr<Matrix> initialize(
1566  size_type stride, std::initializer_list<typename Matrix::value_type> vals,
1567  std::shared_ptr<const Executor> exec, TArgs&&... create_args)
1568 {
1569  using dense = matrix::Dense<typename Matrix::value_type>;
1570  size_type num_rows = vals.size();
1571  auto tmp = dense::create(exec->get_master(), dim<2>{num_rows, 1}, stride);
1572  size_type idx = 0;
1573  for (const auto& elem : vals) {
1574  tmp->at(idx) = elem;
1575  ++idx;
1576  }
1577  auto mtx = Matrix::create(exec, std::forward<TArgs>(create_args)...);
1578  tmp->move_to(mtx);
1579  return mtx;
1580 }
1581 
1602 template <typename Matrix, typename... TArgs>
1603 std::unique_ptr<Matrix> initialize(
1604  std::initializer_list<typename Matrix::value_type> vals,
1605  std::shared_ptr<const Executor> exec, TArgs&&... create_args)
1606 {
1607  return initialize<Matrix>(1, vals, std::move(exec),
1608  std::forward<TArgs>(create_args)...);
1609 }
1610 
1611 
1632 template <typename Matrix, typename... TArgs>
1633 std::unique_ptr<Matrix> initialize(
1634  size_type stride,
1635  std::initializer_list<std::initializer_list<typename Matrix::value_type>>
1636  vals,
1637  std::shared_ptr<const Executor> exec, TArgs&&... create_args)
1638 {
1639  using dense = matrix::Dense<typename Matrix::value_type>;
1640  size_type num_rows = vals.size();
1641  size_type num_cols = num_rows > 0 ? begin(vals)->size() : 1;
1642  auto tmp =
1643  dense::create(exec->get_master(), dim<2>{num_rows, num_cols}, stride);
1644  size_type ridx = 0;
1645  for (const auto& row : vals) {
1646  size_type cidx = 0;
1647  for (const auto& elem : row) {
1648  tmp->at(ridx, cidx) = elem;
1649  ++cidx;
1650  }
1651  ++ridx;
1652  }
1653  auto mtx = Matrix::create(exec, std::forward<TArgs>(create_args)...);
1654  tmp->move_to(mtx);
1655  return mtx;
1656 }
1657 
1658 
1680 template <typename Matrix, typename... TArgs>
1681 std::unique_ptr<Matrix> initialize(
1682  std::initializer_list<std::initializer_list<typename Matrix::value_type>>
1683  vals,
1684  std::shared_ptr<const Executor> exec, TArgs&&... create_args)
1685 {
1686  return initialize<Matrix>(vals.size() > 0 ? begin(vals)->size() : 0, vals,
1687  std::move(exec),
1688  std::forward<TArgs>(create_args)...);
1689 }
1690 
1691 
1692 } // namespace gko
1693 
1694 
1695 #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:46
gko::EnablePolymorphicAssignment< ConcreteLinOp >::move_to
void move_to(result_type *result) override
Definition: polymorphic_object.hpp:731
gko::EnablePolymorphicAssignment< ConcreteLinOp >::convert_to
void convert_to(result_type *result) const override
Definition: polymorphic_object.hpp:729
gko::matrix::Csr
CSR is a matrix format which stores only the nonzero coefficients by compressing each row of the matr...
Definition: matrix.hpp:28
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:19
gko::matrix::SparsityCsr
SparsityCsr is a matrix format which stores only the sparsity pattern of a sparse matrix by compressi...
Definition: csr.hpp:40
gko::matrix::Dense::at
ValueType at(size_type idx) const noexcept
Returns a single element of the matrix.
Definition: dense.hpp:927
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:230
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:36
gko::size_type
std::size_t size_type
Integral type used for allocation quantities.
Definition: types.hpp:89
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:111
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:806
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:1123
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:193
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:1565
gko::matrix::Dense::at
value_type & at(size_type row, size_type col) noexcept
Returns a single element of the matrix.
Definition: dense.hpp:892
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:173
gko::matrix::Dense::get_stride
size_type get_stride() const noexcept
Returns the stride of the matrix.
Definition: dense.hpp:870
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:264
gko::range
A range is a multidimensional view of the memory.
Definition: range.hpp:297
gko
The Ginkgo namespace.
Definition: abstract_factory.hpp:20
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:877
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:851
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:214
gko::span
A span is a lightweight structure used to create sub-ranges from other ranges.
Definition: range.hpp:46
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:41
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:900
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:673
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:248
gko::stop::mode
mode
The mode for the residual norm criterion.
Definition: residual_norm.hpp:38
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:860
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:787
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::matrix::Dense::compute_absolute_inplace
void compute_absolute_inplace() override
Compute absolute inplace on each element.
gko::next_precision
next_precision_base< T > next_precision
Obtains the next type in the singly-linked precision list with half.
Definition: math.hpp:445
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::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:276
gko::matrix::Dense::at
ValueType & at(size_type idx) noexcept
Returns a single element of the matrix.
Definition: dense.hpp:919
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:112
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:31
gko::ConvertibleTo
ConvertibleTo interface is used to mark that the implementer can be converted to the object of Result...
Definition: polymorphic_object.hpp:470
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:1136
gko::int32
std::int32_t int32
32-bit signed integral type.
Definition: types.hpp:106
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:1520
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:682
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:37
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:1537
gko::PolymorphicObject::get_executor
std::shared_ptr< const Executor > get_executor() const noexcept
Returns the Executor of the object.
Definition: polymorphic_object.hpp:234
gko::array::get_size
size_type get_size() const noexcept
Returns the number of elements in the array.
Definition: array.hpp:656
gko::LinOp::get_size
const dim< 2 > & get_size() const noexcept
Returns the size of the operator.
Definition: lin_op.hpp:210
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:279
gko::matrix::Coo
COO stores a matrix in the coordinate matrix format.
Definition: coo.hpp:50
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