GCC Code Coverage Report

Directory: ./
File: tasks/kruglova_a_conjugate_gradient_sle/tbb/src/ops_tbb.cpp
Date: 2026-05-11 08:26:31
Exec Total Coverage
Lines: 48 50 96.0%
Functions: 8 8 100.0%
Branches: 26 46 56.5%
Line Branch Exec Source
1 #include "kruglova_a_conjugate_gradient_sle/tbb/include/ops_tbb.hpp"
2
3 #include <oneapi/tbb/blocked_range.h>
4 #include <oneapi/tbb/parallel_for.h>
5 #include <oneapi/tbb/parallel_reduce.h>
6
7 #include <cmath>
8 #include <cstddef>
9 #include <vector>
10
11 #include "kruglova_a_conjugate_gradient_sle/common/include/common.hpp"
12
13 namespace kruglova_a_conjugate_gradient_sle {
14
15 namespace {
16 void MatrixVectorMultiply(const std::vector<double> &a, const std::vector<double> &p, std::vector<double> &ap, int n) {
17
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 264 tbb::parallel_for(tbb::blocked_range<int>(0, n, 256), [&](const tbb::blocked_range<int> &range) {
18
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 5640 for (int i = range.begin(); i < range.end(); ++i) {
19 double sum = 0.0;
20 5508 const size_t row_offset = static_cast<size_t>(i) * n;
21
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 426080 for (int j = 0; j < n; ++j) {
22 420572 sum += a[row_offset + j] * p[j];
23 }
24 5508 ap[i] = sum;
25 }
26 132 });
27 }
28
29 288 double DotProduct(const std::vector<double> &v1, const std::vector<double> &v2, int n) {
30 576 return tbb::parallel_reduce(tbb::blocked_range<int>(0, n, 512), 0.0,
31 288 [&](const tbb::blocked_range<int> &range, double init) {
32
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 11984 for (int i = range.begin(); i < range.end(); ++i) {
33 11696 init += v1[i] * v2[i];
34 }
35 return init;
36 ✗ }, [](double a, double b) { return a + b; });
37 }
38 } // namespace
39
40
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 24 KruglovaAConjGradSleTBB::KruglovaAConjGradSleTBB(const InType &in) {
41 SetTypeOfTask(GetStaticTypeOfTask());
42 GetInput() = in;
43 24 }
44
45 24 bool KruglovaAConjGradSleTBB::ValidationImpl() {
46 const auto &in = GetInput();
47
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 24 if (in.size <= 0) {
48 return false;
49 }
50
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 24 if (in.A.size() != static_cast<size_t>(in.size) * static_cast<size_t>(in.size)) {
51 return false;
52 }
53
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 24 if (in.b.size() != static_cast<size_t>(in.size)) {
54 ✗ return false;
55 }
56 return true;
57 }
58
59 24 bool KruglovaAConjGradSleTBB::PreProcessingImpl() {
60 24 GetOutput().assign(GetInput().size, 0.0);
61 24 return true;
62 }
63
64 24 bool KruglovaAConjGradSleTBB::RunImpl() {
65 24 const auto &a = GetInput().A;
66 24 const auto &b = GetInput().b;
67 24 int n = GetInput().size;
68 auto &x = GetOutput();
69
70 24 std::vector<double> r = b;
71
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 24 std::vector<double> p = r;
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 24 std::vector<double> ap(n, 0.0);
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74
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 24 double rsold = DotProduct(r, r, n);
75
76 const double tolerance = 1e-8;
77
78
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 132 for (int iter = 0; iter < n * 2; ++iter) {
79 132 MatrixVectorMultiply(a, p, ap, n);
80
81
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 132 double p_ap = DotProduct(p, ap, n);
82
83
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 132 if (std::abs(p_ap) < 1e-15) {
84 break;
85 }
86
87 132 double alpha = rsold / p_ap;
88
89
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 264 tbb::parallel_for(tbb::blocked_range<int>(0, n, 1024), [&](const tbb::blocked_range<int> &range) {
90
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 5640 for (int i = range.begin(); i < range.end(); ++i) {
91 5508 x[i] += alpha * p[i];
92 5508 r[i] -= alpha * ap[i];
93 }
94 132 });
95
96
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 132 double rsnew = DotProduct(r, r, n);
97
98
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 132 if (std::sqrt(rsnew) < tolerance) {
99 break;
100 }
101
102 108 double beta = rsnew / rsold;
103
104
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 108 tbb::parallel_for(tbb::blocked_range<int>(0, n, 1024), [&](const tbb::blocked_range<int> &range) {
105
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 4936 for (int i = range.begin(); i < range.end(); ++i) {
106 4828 p[i] = r[i] + (beta * p[i]);
107 }
108 });
109
110 rsold = rsnew;
111 }
112 24 return true;
113 }
114
115 24 bool KruglovaAConjGradSleTBB::PostProcessingImpl() {
116 24 return true;
117 }
118
119 } // namespace kruglova_a_conjugate_gradient_sle
120