GCC Code Coverage Report

Directory: ./
File: tasks/samoylenko_i_conj_grad_method/mpi/src/ops_mpi.cpp
Date: 2026-01-27 01:59:34
Exec Total Coverage
Lines: 120 120 100.0%
Functions: 8 8 100.0%
Branches: 81 130 62.3%
Line Branch Exec Source
1 #include "samoylenko_i_conj_grad_method/mpi/include/ops_mpi.hpp"
2
3 #include <mpi.h>
4
5 #include <cmath>
6 #include <cstddef>
7 #include <vector>
8
9 #include "samoylenko_i_conj_grad_method/common/include/common.hpp"
10
11 namespace samoylenko_i_conj_grad_method {
12
13 24 SamoylenkoIConjGradMethodMPI::SamoylenkoIConjGradMethodMPI(const InType &in) {
14 SetTypeOfTask(GetStaticTypeOfTask());
15 GetInput() = in;
16 GetOutput().clear();
17 24 }
18
19 24 bool SamoylenkoIConjGradMethodMPI::ValidationImpl() {
20 24 int world_rank = 0;
21 24 MPI_Comm_rank(MPI_COMM_WORLD, &world_rank);
22
23 24 int valid = 0;
24
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 24 if (world_rank == 0) {
25
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 12 valid = (GetInput().first > 0 && (GetInput().second >= 0 && GetInput().second <= 2) && GetOutput().empty()) ? 1 : 0;
26 }
27
28 24 MPI_Bcast(&valid, 1, MPI_INT, 0, MPI_COMM_WORLD);
29 24 return valid == 1;
30 }
31
32 24 bool SamoylenkoIConjGradMethodMPI::PreProcessingImpl() {
33 24 int world_rank = 0;
34 24 MPI_Comm_rank(MPI_COMM_WORLD, &world_rank);
35
36
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 24 if (world_rank == 0) {
37 GetOutput().clear();
38 }
39
40 24 return true;
41 }
42
43 namespace {
44
45 void CalculateDistribution(size_t size, int world_size, std::vector<int> &row_counts, std::vector<int> &row_displs) {
46 24 int proc_rows = static_cast<int>(size) / world_size;
47 24 int extra_rows = static_cast<int>(size) % world_size;
48 int disp = 0;
49
50
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 72 for (int proc = 0; proc < world_size; ++proc) {
51
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 84 row_counts[proc] = proc_rows + (proc < extra_rows ? 1 : 0);
52 48 row_displs[proc] = disp;
53 48 disp += row_counts[proc];
54 }
55 }
56
57 24 std::vector<double> BuildLocalMatrix(size_t size, int local_rows, int local_start, int variant) {
58 24 std::vector<double> local_matrix(local_rows * size, 0.0);
59
60
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 216 for (int i = 0; i < local_rows; ++i) {
61 192 size_t global_row = local_start + i;
62
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 192 switch (variant) {
63 64 case 0: {
64
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 64 local_matrix[(static_cast<size_t>(i) * size) + global_row] = 4.0;
65
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 64 if (global_row > 0) {
66 60 local_matrix[(static_cast<size_t>(i) * size) + (global_row - 1)] = 1.0;
67 }
68
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 64 if (global_row + 1 < size) {
69 60 local_matrix[(static_cast<size_t>(i) * size) + (global_row + 1)] = 1.0;
70 }
71 break;
72 }
73
74 64 case 1: {
75 64 local_matrix[(static_cast<size_t>(i) * size) + global_row] = 5.0;
76 64 break;
77 }
78
79 64 case 2: {
80
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 64 local_matrix[(static_cast<size_t>(i) * size) + global_row] = 3.0;
81 64 size_t anti_col = size - 1 - global_row;
82
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 64 if (anti_col != global_row) {
83 62 local_matrix[(static_cast<size_t>(i) * size) + anti_col] = -1.0;
84 }
85 break;
86 }
87
88 default:
89 break;
90 }
91 }
92 24 return local_matrix;
93 }
94
95 84 void LocalMatrixVectorMult(size_t size, int local_rows, const std::vector<double> &local_matrix,
96 const std::vector<double> &vector, std::vector<double> &local_result) {
97
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 1114 for (int i = 0; i < local_rows; ++i) {
98 double sum = 0.0;
99
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 47108 for (size_t j = 0; j < size; ++j) {
100 46078 sum += local_matrix[(static_cast<size_t>(i) * size) + j] * vector[j];
101 }
102 1030 local_result[i] = sum;
103 }
104 84 }
105
106 double LocalDotProduct(int local_rows, const std::vector<double> &local_first,
107 const std::vector<double> &local_second) {
108 double sum = 0.0;
109
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 2012 for (int i = 0; i < local_rows; ++i) {
110 1868 sum += local_first[i] * local_second[i];
111 }
112 return sum;
113 }
114
115 24 void ConjugateGradient(size_t size, int local_rows, const std::vector<double> &local_matrix,
116 const std::vector<double> &local_vector, const std::vector<int> &row_counts,
117 const std::vector<int> &row_displs, std::vector<double> &local_x) {
118 const double eps = 1e-7;
119 const int iters = 2000;
120
121 24 std::vector<double> local_res(local_rows);
122
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 24 std::vector<double> local_dir(local_rows);
123
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 24 std::vector<double> local_matdir(local_rows);
124
125
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 24 std::vector<double> x(size);
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 24 std::vector<double> dir(size);
127
128
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 24 MPI_Allgatherv(local_x.data(), local_rows, MPI_DOUBLE, x.data(), row_counts.data(), row_displs.data(), MPI_DOUBLE,
129 MPI_COMM_WORLD);
130 24 LocalMatrixVectorMult(size, local_rows, local_matrix, x, local_matdir);
131
132
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 216 for (int i = 0; i < local_rows; ++i) {
133 192 local_res[i] = local_vector[i] - local_matdir[i];
134 192 local_dir[i] = local_res[i];
135 }
136
137 24 double local_res_dot = LocalDotProduct(local_rows, local_res, local_res);
138 24 double res_dot = 0.0;
139
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 24 MPI_Allreduce(&local_res_dot, &res_dot, 1, MPI_DOUBLE, MPI_SUM, MPI_COMM_WORLD);
140
141
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 84 for (int it = 0; it < iters; ++it) {
142
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 84 if (std::sqrt(res_dot) < eps) {
143 break;
144 }
145
146
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 60 MPI_Allgatherv(local_dir.data(), local_rows, MPI_DOUBLE, dir.data(), row_counts.data(), row_displs.data(),
147 MPI_DOUBLE, MPI_COMM_WORLD);
148 60 LocalMatrixVectorMult(size, local_rows, local_matrix, dir, local_matdir);
149
150 60 double local_dir_dot = LocalDotProduct(local_rows, local_dir, local_matdir);
151 60 double dir_dot = 0.0;
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 60 MPI_Allreduce(&local_dir_dot, &dir_dot, 1, MPI_DOUBLE, MPI_SUM, MPI_COMM_WORLD);
153
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 60 if (std::fabs(dir_dot) < 1e-15) {
155 break; // so we dont divide by 0
156 }
157
158 60 double step = res_dot / dir_dot;
159
160
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 898 for (int i = 0; i < local_rows; ++i) {
161 838 local_x[i] += step * local_dir[i];
162 838 local_res[i] -= step * local_matdir[i];
163 }
164
165 60 double local_res_dot_new = LocalDotProduct(local_rows, local_res, local_res);
166 60 double res_dot_new = 0.0;
167
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 60 MPI_Allreduce(&local_res_dot_new, &res_dot_new, 1, MPI_DOUBLE, MPI_SUM, MPI_COMM_WORLD);
168
169 60 double conj_coef = res_dot_new / res_dot;
170
171
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 898 for (int i = 0; i < local_rows; ++i) {
172 838 local_dir[i] = local_res[i] + (conj_coef * local_dir[i]);
173 }
174
175 60 res_dot = res_dot_new;
176 }
177 24 }
178
179 } // namespace
180
181 24 bool SamoylenkoIConjGradMethodMPI::RunImpl() {
182 24 int world_rank = 0;
183 24 int world_size = 0;
184 24 MPI_Comm_rank(MPI_COMM_WORLD, &world_rank);
185 24 MPI_Comm_size(MPI_COMM_WORLD, &world_size);
186
187 24 int n = 0;
188 24 int variant = 0;
189
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 24 if (world_rank == 0) {
190 12 n = GetInput().first;
191 12 variant = GetInput().second;
192 }
193
194 24 MPI_Bcast(&n, 1, MPI_INT, 0, MPI_COMM_WORLD);
195 24 MPI_Bcast(&variant, 1, MPI_INT, 0, MPI_COMM_WORLD);
196
197
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 24 if (n <= 0) {
198 return false;
199 }
200
201 24 auto size = static_cast<size_t>(n);
202
203 24 std::vector<int> row_counts(world_size);
204
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 24 std::vector<int> row_displs(world_size);
205 24 CalculateDistribution(size, world_size, row_counts, row_displs);
206
207
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 24 int local_rows = row_counts[world_rank];
208 24 int local_start = row_displs[world_rank];
209
210
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 24 std::vector<double> local_matrix = BuildLocalMatrix(size, local_rows, local_start, variant);
211
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 24 std::vector<double> vector(size);
212
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 24 if (world_rank == 0) {
213
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 204 for (size_t i = 0; i < size; ++i) {
214 192 vector[i] = 1.0;
215 }
216 }
217
218
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 24 std::vector<double> local_vector(local_rows);
219
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 24 MPI_Scatterv(vector.data(), row_counts.data(), row_displs.data(), MPI_DOUBLE, local_vector.data(), local_rows,
220 MPI_DOUBLE, 0, MPI_COMM_WORLD);
221
222
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 24 std::vector<double> local_x(local_rows, 0.0);
223
224
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 24 ConjugateGradient(size, local_rows, local_matrix, local_vector, row_counts, row_displs, local_x);
225
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 24 std::vector<double> x(size);
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 24 MPI_Gatherv(local_x.data(), local_rows, MPI_DOUBLE, x.data(), row_counts.data(), row_displs.data(), MPI_DOUBLE, 0,
228 MPI_COMM_WORLD);
229
230
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 24 if (world_rank == 0) {
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 12 GetOutput() = x;
232 }
233
234 return true;
235 }
236
237 24 bool SamoylenkoIConjGradMethodMPI::PostProcessingImpl() {
238 24 int world_rank = 0;
239 24 MPI_Comm_rank(MPI_COMM_WORLD, &world_rank);
240
241
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 24 if (world_rank == 0) {
242 12 return !GetOutput().empty();
243 }
244
245 return true;
246 }
247
248 } // namespace samoylenko_i_conj_grad_method
249