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<a href=#2 id=2 data-nosnippet>2</a>///
<a href=#3 id=3 data-nosnippet>3</a>/// This function executes the closure.
<a href=#4 id=4 data-nosnippet>4</a>///
<a href=#5 id=5 data-nosnippet>5</a>/// # Type Parameters
<a href=#6 id=6 data-nosnippet>6</a>/// - `F`: The type of the closure, must be `FnOnce() -> R`.
<a href=#7 id=7 data-nosnippet>7</a>/// - `R`: The return type.
<a href=#8 id=8 data-nosnippet>8</a>///
<a href=#9 id=9 data-nosnippet>9</a>/// # Parameters
<a href=#10 id=10 data-nosnippet>10</a>/// - `f`: The closure to execute.
<a href=#11 id=11 data-nosnippet>11</a>///
<a href=#12 id=12 data-nosnippet>12</a>/// # Returns
<a href=#13 id=13 data-nosnippet>13</a>/// The result of executing the closure.
<a href=#14 id=14 data-nosnippet>14</a>///
<a href=#15 id=15 data-nosnippet>15</a>/// # Examples
<a href=#16 id=16 data-nosnippet>16</a>/// ```rust,no_run
<a href=#17 id=17 data-nosnippet>17</a>/// use trash_analyzer::parallel::execute;
<a href=#18 id=18 data-nosnippet>18</a>///
<a href=#19 id=19 data-nosnippet>19</a>/// let result = execute(|| {
<a href=#20 id=20 data-nosnippet>20</a>/// // Some computation
<a href=#21 id=21 data-nosnippet>21</a>/// 42
<a href=#22 id=22 data-nosnippet>22</a>/// });
<a href=#23 id=23 data-nosnippet>23</a>/// assert_eq!(result, 42);
<a href=#24 id=24 data-nosnippet>24</a>/// ```
<a href=#25 id=25 data-nosnippet>25</a></span><span class="kw">pub fn </span>execute<F, R>(f: F) -> R
<a href=#26 id=26 data-nosnippet>26</a><span class="kw">where
<a href=#27 id=27 data-nosnippet>27</a> </span>F: FnOnce() -> R,
<a href=#28 id=28 data-nosnippet>28</a>{
<a href=#29 id=29 data-nosnippet>29</a> f()
<a href=#30 id=30 data-nosnippet>30</a>}
<a href=#31 id=31 data-nosnippet>31</a>
<a href=#32 id=32 data-nosnippet>32</a><span class="doccomment">/// Apply a function to each element of a vector in parallel.
<a href=#33 id=33 data-nosnippet>33</a>///
<a href=#34 id=34 data-nosnippet>34</a>/// This function provides a parallel-ready interface for mapping operations.
<a href=#35 id=35 data-nosnippet>35</a>/// Currently uses sequential processing but is designed to be easily extended
<a href=#36 id=36 data-nosnippet>36</a>/// with actual parallel execution backends.
<a href=#37 id=37 data-nosnippet>37</a>///
<a href=#38 id=38 data-nosnippet>38</a>/// # Type Parameters
<a href=#39 id=39 data-nosnippet>39</a>/// - `T`: The input element type, must be `Send`.
<a href=#40 id=40 data-nosnippet>40</a>/// - `U`: The output element type, must be `Send`.
<a href=#41 id=41 data-nosnippet>41</a>/// - `F`: The mapping function type.
<a href=#42 id=42 data-nosnippet>42</a>///
<a href=#43 id=43 data-nosnippet>43</a>/// # Parameters
<a href=#44 id=44 data-nosnippet>44</a>/// - `data`: The input vector to map over.
<a href=#45 id=45 data-nosnippet>45</a>/// - `f`: The function to apply to each element.
<a href=#46 id=46 data-nosnippet>46</a>///
<a href=#47 id=47 data-nosnippet>47</a>/// # Returns
<a href=#48 id=48 data-nosnippet>48</a>/// A new vector containing the results of applying `f` to each element.
<a href=#49 id=49 data-nosnippet>49</a>///
<a href=#50 id=50 data-nosnippet>50</a>/// # Examples
<a href=#51 id=51 data-nosnippet>51</a>/// ```rust
<a href=#52 id=52 data-nosnippet>52</a>/// use trash_analyzer::parallel::parallel_map;
<a href=#53 id=53 data-nosnippet>53</a>///
<a href=#54 id=54 data-nosnippet>54</a>/// let data = vec![1, 2, 3, 4, 5];
<a href=#55 id=55 data-nosnippet>55</a>/// let result = parallel_map(data, |x| x * 2);
<a href=#56 id=56 data-nosnippet>56</a>/// assert_eq!(result, vec![2, 4, 6, 8, 10]);
<a href=#57 id=57 data-nosnippet>57</a>/// ```
<a href=#58 id=58 data-nosnippet>58</a></span><span class="kw">pub fn </span>parallel_map<T, U, F>(data: Vec<T>, f: F) -> Vec<U>
<a href=#59 id=59 data-nosnippet>59</a><span class="kw">where
<a href=#60 id=60 data-nosnippet>60</a> </span>T: Send,
<a href=#61 id=61 data-nosnippet>61</a> U: Send,
<a href=#62 id=62 data-nosnippet>62</a> F: Fn(T) -> U + Sync + Send,
<a href=#63 id=63 data-nosnippet>63</a>{
<a href=#64 id=64 data-nosnippet>64</a> data.into_iter().map(f).collect()
<a href=#65 id=65 data-nosnippet>65</a>}
<a href=#66 id=66 data-nosnippet>66</a>
<a href=#67 id=67 data-nosnippet>67</a><span class="doccomment">/// Apply a function to each element of a vector in parallel without collecting results.
<a href=#68 id=68 data-nosnippet>68</a>///
<a href=#69 id=69 data-nosnippet>69</a>/// This function provides a parallel-ready interface for executing a function on each
<a href=#70 id=70 data-nosnippet>70</a>/// element of the input vector in parallel, discarding the results. Useful for
<a href=#71 id=71 data-nosnippet>71</a>/// side-effect operations like I/O or logging. Currently uses sequential processing
<a href=#72 id=72 data-nosnippet>72</a>/// but is designed to be easily extended with actual parallel execution backends.
<a href=#73 id=73 data-nosnippet>73</a>///
<a href=#74 id=74 data-nosnippet>74</a>/// # Type Parameters
<a href=#75 id=75 data-nosnippet>75</a>/// - `T`: The element type, must be `Send`.
<a href=#76 id=76 data-nosnippet>76</a>/// - `F`: The function type.
<a href=#77 id=77 data-nosnippet>77</a>///
<a href=#78 id=78 data-nosnippet>78</a>/// # Parameters
<a href=#79 id=79 data-nosnippet>79</a>/// - `data`: The input vector to iterate over.
<a href=#80 id=80 data-nosnippet>80</a>/// - `f`: The function to apply to each element.
<a href=#81 id=81 data-nosnippet>81</a>///
<a href=#82 id=82 data-nosnippet>82</a>/// # Examples
<a href=#83 id=83 data-nosnippet>83</a>/// ```rust
<a href=#84 id=84 data-nosnippet>84</a>/// use trash_analyzer::parallel::parallel_for_each;
<a href=#85 id=85 data-nosnippet>85</a>/// use std::sync::Mutex;
<a href=#86 id=86 data-nosnippet>86</a>///
<a href=#87 id=87 data-nosnippet>87</a>/// let data = vec![1, 2, 3, 4, 5];
<a href=#88 id=88 data-nosnippet>88</a>/// let sum = Mutex::new(0);
<a href=#89 id=89 data-nosnippet>89</a>/// parallel_for_each(data, |x| {
<a href=#90 id=90 data-nosnippet>90</a>/// let mut s = sum.lock().unwrap();
<a href=#91 id=91 data-nosnippet>91</a>/// *s += x;
<a href=#92 id=92 data-nosnippet>92</a>/// });
<a href=#93 id=93 data-nosnippet>93</a>/// assert_eq!(*sum.lock().unwrap(), 15);
<a href=#94 id=94 data-nosnippet>94</a>/// ```
<a href=#95 id=95 data-nosnippet>95</a></span><span class="kw">pub fn </span>parallel_for_each<T, F>(data: Vec<T>, f: F)
<a href=#96 id=96 data-nosnippet>96</a><span class="kw">where
<a href=#97 id=97 data-nosnippet>97</a> </span>T: Send,
<a href=#98 id=98 data-nosnippet>98</a> F: Fn(T) + Sync + Send,
<a href=#99 id=99 data-nosnippet>99</a>{
<a href=#100 id=100 data-nosnippet>100</a> data.into_iter().for_each(f);
<a href=#101 id=101 data-nosnippet>101</a>}
<a href=#102 id=102 data-nosnippet>102</a>
<a href=#103 id=103 data-nosnippet>103</a><span class="doccomment">/// Combine all elements of a vector using a folding function in parallel.
<a href=#104 id=104 data-nosnippet>104</a>///
<a href=#105 id=105 data-nosnippet>105</a>/// This function performs a parallel fold operation, first folding chunks of the
<a href=#106 id=106 data-nosnippet>106</a>/// data in parallel, then combining the results using the same folding function.
<a href=#107 id=107 data-nosnippet>107</a>/// Currently uses sequential processing but is designed to be easily extended
<a href=#108 id=108 data-nosnippet>108</a>/// with actual parallel execution backends.
<a href=#109 id=109 data-nosnippet>109</a>///
<a href=#110 id=110 data-nosnippet>110</a>/// # Type Parameters
<a href=#111 id=111 data-nosnippet>111</a>/// - `T`: The element type, must be `Send + Sync`.
<a href=#112 id=112 data-nosnippet>112</a>/// - `F`: The folding function type.
<a href=#113 id=113 data-nosnippet>113</a>///
<a href=#114 id=114 data-nosnippet>114</a>/// # Parameters
<a href=#115 id=115 data-nosnippet>115</a>/// - `data`: The input vector to fold.
<a href=#116 id=116 data-nosnippet>116</a>/// - `init`: The initial value for the fold.
<a href=#117 id=117 data-nosnippet>117</a>/// - `f`: The folding function that takes two values and combines them.
<a href=#118 id=118 data-nosnippet>118</a>///
<a href=#119 id=119 data-nosnippet>119</a>/// # Returns
<a href=#120 id=120 data-nosnippet>120</a>/// The final folded value.
<a href=#121 id=121 data-nosnippet>121</a>///
<a href=#122 id=122 data-nosnippet>122</a>/// # Examples
<a href=#123 id=123 data-nosnippet>123</a>/// ```rust
<a href=#124 id=124 data-nosnippet>124</a>/// use trash_analyzer::parallel::parallel_fold;
<a href=#125 id=125 data-nosnippet>125</a>///
<a href=#126 id=126 data-nosnippet>126</a>/// let data = vec![1, 2, 3, 4, 5];
<a href=#127 id=127 data-nosnippet>127</a>/// let sum = parallel_fold(data, 0, |acc, x| acc + x);
<a href=#128 id=128 data-nosnippet>128</a>/// assert_eq!(sum, 15);
<a href=#129 id=129 data-nosnippet>129</a>/// ```
<a href=#130 id=130 data-nosnippet>130</a></span><span class="kw">pub fn </span>parallel_fold<T, F>(data: Vec<T>, init: T, f: F) -> T
<a href=#131 id=131 data-nosnippet>131</a><span class="kw">where
<a href=#132 id=132 data-nosnippet>132</a> </span>T: Send + Sync,
<a href=#133 id=133 data-nosnippet>133</a> F: Fn(T, T) -> T + Sync + Send,
<a href=#134 id=134 data-nosnippet>134</a>{
<a href=#135 id=135 data-nosnippet>135</a> data.into_iter().fold(init, f)
<a href=#136 id=136 data-nosnippet>136</a>}
<a href=#137 id=137 data-nosnippet>137</a>
<a href=#138 id=138 data-nosnippet>138</a><span class="doccomment">/// Apply a predicate to filter elements in parallel.
<a href=#139 id=139 data-nosnippet>139</a>///
<a href=#140 id=140 data-nosnippet>140</a>/// This function uses parallel processing to filter elements from a vector
<a href=#141 id=141 data-nosnippet>141</a>/// based on a predicate function, returning only elements that satisfy the condition.
<a href=#142 id=142 data-nosnippet>142</a>///
<a href=#143 id=143 data-nosnippet>143</a>/// # Type Parameters
<a href=#144 id=144 data-nosnippet>144</a>/// - `T`: The element type, must be `Send + Sync`.
<a href=#145 id=145 data-nosnippet>145</a>/// - `F`: The predicate function type, must be `Fn(&T) -> bool + Send + Sync`.
<a href=#146 id=146 data-nosnippet>146</a>///
<a href=#147 id=147 data-nosnippet>147</a>/// # Parameters
<a href=#148 id=148 data-nosnippet>148</a>/// - `data`: The input vector to filter.
<a href=#149 id=149 data-nosnippet>149</a>/// - `predicate`: Function that returns true for elements to keep.
<a href=#150 id=150 data-nosnippet>150</a>///
<a href=#151 id=151 data-nosnippet>151</a>/// # Returns
<a href=#152 id=152 data-nosnippet>152</a>/// A new vector containing only elements that satisfy the predicate.
<a href=#153 id=153 data-nosnippet>153</a>///
<a href=#154 id=154 data-nosnippet>154</a>/// # Examples
<a href=#155 id=155 data-nosnippet>155</a>/// ```rust
<a href=#156 id=156 data-nosnippet>156</a>/// use trash_analyzer::parallel::parallel_filter;
<a href=#157 id=157 data-nosnippet>157</a>///
<a href=#158 id=158 data-nosnippet>158</a>/// let data = vec![1, 2, 3, 4, 5, 6, 7, 8, 9, 10];
<a href=#159 id=159 data-nosnippet>159</a>/// let evens = parallel_filter(data, |&x| x % 2 == 0);
<a href=#160 id=160 data-nosnippet>160</a>/// assert_eq!(evens, vec![2, 4, 6, 8, 10]);
<a href=#161 id=161 data-nosnippet>161</a>/// ```
<a href=#162 id=162 data-nosnippet>162</a></span><span class="kw">pub fn </span>parallel_filter<T, F>(data: Vec<T>, predicate: F) -> Vec<T>
<a href=#163 id=163 data-nosnippet>163</a><span class="kw">where
<a href=#164 id=164 data-nosnippet>164</a> </span>T: Send + Sync,
<a href=#165 id=165 data-nosnippet>165</a> F: Fn(<span class="kw-2">&</span>T) -> bool + Send + Sync,
<a href=#166 id=166 data-nosnippet>166</a>{
<a href=#167 id=167 data-nosnippet>167</a> data.into_iter().filter(predicate).collect()
<a href=#168 id=168 data-nosnippet>168</a>}</code></pre></div></section></main></body></html>