GCC Code Coverage Report

Directory: ./
File: tasks/terekhov_d_gauss_vert/all/src/ops_all.cpp
Date: 2026-05-11 08:26:31
Exec Total Coverage
Lines: 66 66 100.0%
Functions: 8 8 100.0%
Branches: 32 56 57.1%
Line Branch Exec Source
1 #include "terekhov_d_gauss_vert/all/include/ops_all.hpp"
2
3 #include <mpi.h>
4 #include <omp.h>
5
6 #include <algorithm>
7 #include <cmath>
8 #include <cstddef>
9 #include <vector>
10
11 #include "terekhov_d_gauss_vert/common/include/common.hpp"
12
13 namespace terekhov_d_gauss_vert {
14
15 namespace {
16
17 inline int Mirror(int coord, int max_val) {
18
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 1632 if (coord < 0) {
19 58 return -coord - 1;
20 }
21
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 3142 if (coord >= max_val) {
22 122 return (2 * max_val) - coord - 1;
23 }
24 return coord;
25 }
26
27 6 inline void FillLocalPadded(std::vector<int> &local_padded, const InType &in, int padded_width, int width, int height,
28 int start_row, int local_padded_height) {
29
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 70 for (int row = 0; row < local_padded_height; ++row) {
30 64 const int global_row = start_row + row - 1;
31
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 1696 for (int col = 0; col < padded_width; ++col) {
32
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 1632 const int src_x = Mirror(col - 1, width);
33 const int src_y = Mirror(global_row, height);
34 1632 const size_t src_idx = (static_cast<size_t>(src_y) * static_cast<size_t>(width)) + static_cast<size_t>(src_x);
35 1632 const size_t padded_idx =
36 1632 (static_cast<size_t>(row) * static_cast<size_t>(padded_width)) + static_cast<size_t>(col);
37 1632 local_padded[padded_idx] = in.data[src_idx];
38 }
39 }
40 6 }
41
42 inline void ProcessLocalRows(std::vector<int> &local_result, const std::vector<int> &local_padded, int padded_width,
43 int width, int local_height) {
44 6 #pragma omp parallel for schedule(static) default(none) \
45 shared(local_result, local_padded, padded_width, width, local_height) shared(kGaussKernel)
46 for (int row = 0; row < local_height; ++row) {
47 for (int col = 0; col < width; ++col) {
48 const size_t idx = (static_cast<size_t>(row) * static_cast<size_t>(width)) + static_cast<size_t>(col);
49 float sum = 0.0F;
50
51 for (int ky = -1; ky <= 1; ++ky) {
52 for (int kx = -1; kx <= 1; ++kx) {
53 const int px = col + kx + 1;
54 const int py = row + ky + 1;
55 const int kernel_idx = ((ky + 1) * 3) + (kx + 1);
56 const size_t padded_idx =
57 (static_cast<size_t>(py) * static_cast<size_t>(padded_width)) + static_cast<size_t>(px);
58 sum += static_cast<float>(local_padded[padded_idx]) * kGaussKernel[static_cast<size_t>(kernel_idx)];
59 }
60 }
61
62 local_result[idx] = static_cast<int>(std::lround(sum));
63 }
64 }
65 }
66
67 6 inline void GatherResults(std::vector<int> &output_data, const std::vector<int> &local_result, int size) {
68 6 std::vector<int> recv_counts(static_cast<size_t>(size));
69
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 6 std::vector<int> displs(static_cast<size_t>(size));
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71
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 6 int local_count = static_cast<int>(local_result.size());
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 6 MPI_Allgather(&local_count, 1, MPI_INT, recv_counts.data(), 1, MPI_INT, MPI_COMM_WORLD);
73
74 6 displs[0] = 0;
75
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 12 for (int idx = 1; idx < size; ++idx) {
76 6 displs[idx] = displs[idx - 1] + recv_counts[idx - 1];
77 }
78
79
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 6 MPI_Allgatherv(local_result.data(), local_count, MPI_INT, output_data.data(), recv_counts.data(), displs.data(),
80 MPI_INT, MPI_COMM_WORLD);
81 6 }
82
83 6 inline OutType SolveALL(const InType &in) {
84 6 int rank = 0;
85 6 int size = 1;
86 6 MPI_Comm_rank(MPI_COMM_WORLD, &rank);
87 6 MPI_Comm_size(MPI_COMM_WORLD, &size);
88
89 6 const int width = in.width;
90 6 const int height = in.height;
91
92 6 OutType output;
93 6 output.width = width;
94 6 output.height = height;
95
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 6 output.data.resize(static_cast<size_t>(width) * static_cast<size_t>(height));
96
97 6 int rows_per_proc = height / size;
98 6 int remainder = height % size;
99
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 6 int start_row = (rank * rows_per_proc) + std::min(rank, remainder);
100
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 6 int end_row = start_row + rows_per_proc + ((rank < remainder) ? 1 : 0);
101
102
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 6 if (start_row >= end_row) {
103 return output;
104 }
105
106 6 const int padded_width = width + 2;
107 6 const int local_height = end_row - start_row;
108 6 const int local_padded_height = local_height + 2;
109
110
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 6 std::vector<int> local_padded(static_cast<size_t>(padded_width) * static_cast<size_t>(local_padded_height), 0);
111 6 FillLocalPadded(local_padded, in, padded_width, width, height, start_row, local_padded_height);
112
113
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 6 std::vector<int> local_result(static_cast<size_t>(local_height) * static_cast<size_t>(width), 0);
114 ProcessLocalRows(local_result, local_padded, padded_width, width, local_height);
115
116
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 6 GatherResults(output.data, local_result, size);
117
118 return output;
119 }
120
121 } // namespace
122
123
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 6 TerekhovDGaussVertALL::TerekhovDGaussVertALL(const InType &in) {
124 SetTypeOfTask(GetStaticTypeOfTask());
125 GetInput() = in;
126 6 local_out_.width = 0;
127
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 6 local_out_.height = 0;
128 local_out_.data.clear();
129 6 }
130
131 12 bool TerekhovDGaussVertALL::ValidationImpl() {
132 const auto &in = GetInput();
133
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 12 if ((in.width <= 0) || (in.height <= 0)) {
134 return false;
135 }
136 12 const std::size_t need = static_cast<std::size_t>(in.width) * static_cast<std::size_t>(in.height);
137 12 return in.data.size() == need;
138 }
139
140 6 bool TerekhovDGaussVertALL::PreProcessingImpl() {
141 6 local_out_.width = 0;
142
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 6 local_out_.height = 0;
143 local_out_.data.clear();
144 6 return true;
145 }
146
147 6 bool TerekhovDGaussVertALL::RunImpl() {
148
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 6 if (!ValidationImpl()) {
149 return false;
150 }
151 6 local_out_ = SolveALL(GetInput());
152 6 return true;
153 }
154
155 6 bool TerekhovDGaussVertALL::PostProcessingImpl() {
156 GetOutput() = local_out_;
157 const auto &out = GetOutput();
158 6 return out.data.size() == (static_cast<size_t>(out.width) * static_cast<size_t>(out.height));
159 }
160
161 } // namespace terekhov_d_gauss_vert
162