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<li class="navelem"><a class="el" href="dir_d44c64559bbebec7f509842c48db8b23.html">include</a></li><li class="navelem"><a class="el" href="dir_6baf2bb612a2f0daa69af3101ede80a1.html">cutlass</a></li><li class="navelem"><a class="el" href="dir_ac488927e63b76ba9cb3ad9c317bbde9.html">reduction</a></li><li class="navelem"><a class="el" href="dir_f97022a05803191deba9644b471136c4.html">thread</a></li> </ul>
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<a href="reduce_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/***************************************************************************************************</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved.</span></div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * Redistribution and use in source and binary forms, with or without modification, are permitted</span></div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> * provided that the following conditions are met:</span></div><div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> * * Redistributions of source code must retain the above copyright notice, this list of</span></div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment"> * conditions and the following disclaimer.</span></div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment"> * * Redistributions in binary form must reproduce the above copyright notice, this list of</span></div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment"> * conditions and the following disclaimer in the documentation and/or other materials</span></div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment"> * provided with the distribution.</span></div><div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment"> * * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used</span></div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment"> * to endorse or promote products derived from this software without specific prior written</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> <span class="comment"> * permission.</span></div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="comment"> *</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="comment"> * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR</span></div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> <span class="comment"> * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND</span></div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="comment"> * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="comment"> * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="comment"> * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;</span></div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="comment"> * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,</span></div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="comment"> * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE</span></div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment"> * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment"> *</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="comment"> **************************************************************************************************/</span></div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> <span class="preprocessor">#pragma once</span></div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span> </div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span> <span class="preprocessor">#include "<a class="code" href="cutlass_8h.html">cutlass/cutlass.h</a>"</span></div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="preprocessor">#include "<a class="code" href="numeric__types_8h.html">cutlass/numeric_types.h</a>"</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> <span class="preprocessor">#include "<a class="code" href="array_8h.html">cutlass/array.h</a>"</span></div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span> <span class="preprocessor">#include "<a class="code" href="half_8h.html">cutlass/half.h</a>"</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span> <span class="preprocessor">#include "<a class="code" href="functional_8h.html">cutlass/functional.h</a>"</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span> </div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span> <span class="keyword">namespace </span><a class="code" href="namespacecutlass.html">cutlass</a> {</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span> <span class="keyword">namespace </span>reduction {</div><div class="line"><a name="l00039"></a><span class="lineno"><a class="line" href="namespacecutlass_1_1reduction_1_1thread.html"> 39</a></span> <span class="keyword">namespace </span>thread {</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span> </div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span> <span class="keyword">template</span> <<span class="keyword">typename</span> Op, <span class="keyword">typename</span> T></div><div class="line"><a name="l00043"></a><span class="lineno"><a class="line" href="structcutlass_1_1reduction_1_1thread_1_1Reduce.html"> 43</a></span> <span class="keyword">struct </span><a class="code" href="structcutlass_1_1reduction_1_1thread_1_1Reduce.html">Reduce</a>;</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> </div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span> </div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T></div><div class="line"><a name="l00049"></a><span class="lineno"><a class="line" href="structcutlass_1_1reduction_1_1thread_1_1Reduce_3_01plus_3_01T_01_4_00_01T_01_4.html"> 49</a></span> <span class="keyword">struct </span><a class="code" href="structcutlass_1_1reduction_1_1thread_1_1Reduce.html">Reduce</a>< <a class="code" href="structcutlass_1_1plus.html">plus</a><T>, T > {</div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span> </div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <a class="code" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a></div><div class="line"><a name="l00052"></a><span class="lineno"><a class="line" href="structcutlass_1_1reduction_1_1thread_1_1Reduce_3_01plus_3_01T_01_4_00_01T_01_4.html#adc27a687496822f8477a427bf6ecd8b5"> 52</a></span>  T <a class="code" href="structcutlass_1_1reduction_1_1thread_1_1Reduce_3_01plus_3_01T_01_4_00_01T_01_4.html#adc27a687496822f8477a427bf6ecd8b5">operator()</a>(T lhs, T <span class="keyword">const</span> &rhs)<span class="keyword"> const </span>{</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <a class="code" href="structcutlass_1_1plus.html">plus<T></a> _op;</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="keywordflow">return</span> _op(lhs, rhs);</div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  } </div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span> };</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span> </div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> </div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> <span class="keyword">template</span> <<span class="keyword">typename</span> T, <span class="keywordtype">int</span> N></div><div class="line"><a name="l00062"></a><span class="lineno"><a class="line" href="structcutlass_1_1reduction_1_1thread_1_1Reduce_3_01plus_3_01T_01_4_00_01Array_3_01T_00_01N_01_4_01_4.html"> 62</a></span> <span class="keyword">struct </span><a class="code" href="structcutlass_1_1reduction_1_1thread_1_1Reduce.html">Reduce</a> < <a class="code" href="structcutlass_1_1plus.html">plus</a><T>, Array<T, N>> {</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  </div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <a class="code" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a></div><div class="line"><a name="l00065"></a><span class="lineno"><a class="line" href="structcutlass_1_1reduction_1_1thread_1_1Reduce_3_01plus_3_01T_01_4_00_01Array_3_01T_00_01N_01_4_01_4.html#abb3c25f1d38931ffe40a24307fbf49b1"> 65</a></span>  Array<T, 1> <a class="code" href="structcutlass_1_1reduction_1_1thread_1_1Reduce_3_01plus_3_01T_01_4_00_01Array_3_01T_00_01N_01_4_01_4.html#abb3c25f1d38931ffe40a24307fbf49b1">operator()</a>(Array<T, N> <span class="keyword">const</span> &in)<span class="keyword"> const </span>{</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> </div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  Array<T, 1> result;</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <a class="code" href="structcutlass_1_1reduction_1_1thread_1_1Reduce.html">Reduce< plus<T></a>, T > scalar_reduce;</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  result.clear();</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> </div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <a class="code" href="cutlass_8h.html#a4b1c9f25ab6eaa25e1f2258dd63e6ce4">CUTLASS_PRAGMA_UNROLL</a></div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i < N; ++i) {</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  result[0] = scalar_reduce(result[0], in[i]);</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  }</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span> </div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  }</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span> };</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span> </div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span> </div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> <span class="keyword">template</span> <<span class="keywordtype">int</span> N></div><div class="line"><a name="l00084"></a><span class="lineno"><a class="line" href="structcutlass_1_1reduction_1_1thread_1_1Reduce_3_01plus_3_01half__t_01_4_00_01Array_3_01half__t_00_01N_01_4_01_4.html"> 84</a></span> <span class="keyword">struct </span><a class="code" href="structcutlass_1_1reduction_1_1thread_1_1Reduce.html">Reduce</a> < <a class="code" href="structcutlass_1_1plus.html">plus</a><<a class="code" href="structcutlass_1_1half__t.html">half_t</a>>, Array<<a class="code" href="structcutlass_1_1half__t.html">half_t</a>, N> > {</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  </div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <a class="code" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a></div><div class="line"><a name="l00087"></a><span class="lineno"><a class="line" href="structcutlass_1_1reduction_1_1thread_1_1Reduce_3_01plus_3_01half__t_01_4_00_01Array_3_01half__t_00_01N_01_4_01_4.html#afe8938d7e9d806511480831668ef1563"> 87</a></span>  Array<half_t, 1> <a class="code" href="structcutlass_1_1reduction_1_1thread_1_1Reduce_3_01plus_3_01half__t_01_4_00_01Array_3_01half__t_00_01N_01_4_01_4.html#afe8938d7e9d806511480831668ef1563">operator()</a>(Array<half_t, N> <span class="keyword">const</span> &input) {</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> </div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  Array<half_t, 1> result;</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span> </div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  <span class="comment">// If there is only 1 element - there is nothing to reduce</span></div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="keywordflow">if</span>( N ==1 ){</div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span> </div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  result[0] = input.front();</div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span> </div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  </div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span> <span class="preprocessor"> #if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 600)</span></div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  </div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  __half result_d;</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  Array<half_t, 1> <span class="keyword">const</span> *in_ptr_half = <span class="keyword">reinterpret_cast<</span>Array<half_t, 1> <span class="keyword">const </span>*<span class="keyword">></span>(&input);</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  Array<half_t, 2> <span class="keyword">const</span> *in_ptr_half2 = <span class="keyword">reinterpret_cast<</span>Array<half_t, 2> <span class="keyword">const </span>*<span class="keyword">></span>(&input);</div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  __half2 <span class="keyword">const</span> *x_in_half2 = <span class="keyword">reinterpret_cast<</span>__half2 <span class="keyword">const </span>*<span class="keyword">></span>(in_ptr_half2);</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span> </div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="comment">// Set initial result = first half2, in case N==2</span></div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  __half2 tmp_result = x_in_half2[0];</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span> </div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <a class="code" href="cutlass_8h.html#a4b1c9f25ab6eaa25e1f2258dd63e6ce4">CUTLASS_PRAGMA_UNROLL</a></div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 1; i < N/2; ++i) {</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> </div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  tmp_result = __hadd2(x_in_half2[i], tmp_result);</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> </div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  }</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  </div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  result_d = __hadd(__low2half(tmp_result), __high2half(tmp_result));</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  </div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="comment">// One final step is needed for odd "N" (to add the (N-1)th element)</span></div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="keywordflow">if</span>( N%2 ){</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> </div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  __half last_element;</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  Array<half_t, 1> tmp_last;</div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  Array<half_t, 1> *tmp_last_ptr = &tmp_last;</div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  tmp_last_ptr[0] = in_ptr_half[N-1];</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  last_element = <span class="keyword">reinterpret_cast<</span>__half <span class="keyword">const </span>&<span class="keyword">></span>(tmp_last);</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span> </div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  result_d = __hadd(result_d, last_element);</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> </div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  } </div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span> </div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  Array<half_t, 1> *result_ptr = &result;</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  *result_ptr = <span class="keyword">reinterpret_cast<</span>Array<half_t, 1> &<span class="keyword">></span>(result_d);</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span> </div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> <span class="preprocessor"> #else</span></div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  </div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <a class="code" href="structcutlass_1_1reduction_1_1thread_1_1Reduce.html">Reduce< plus<half_t></a>, <a class="code" href="structcutlass_1_1half__t.html">half_t</a> > scalar_reduce;</div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  result.clear();</div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span> </div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <a class="code" href="cutlass_8h.html#a4b1c9f25ab6eaa25e1f2258dd63e6ce4">CUTLASS_PRAGMA_UNROLL</a></div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i < N; ++i) {</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span> </div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  result[0] = scalar_reduce(result[0], input[i]);</div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span> </div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  }</div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span> </div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span> <span class="preprocessor"> #endif</span></div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  }</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span> </div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  </div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  }</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span> };</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span> </div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span> </div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span> </div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> <span class="keyword">template</span> <<span class="keywordtype">int</span> N></div><div class="line"><a name="l00158"></a><span class="lineno"><a class="line" href="structcutlass_1_1reduction_1_1thread_1_1Reduce_3_01plus_3_01half__t_01_4_00_01AlignedArray_3_01half__t_00_01N_01_4_01_4.html"> 158</a></span> <span class="keyword">struct </span><a class="code" href="structcutlass_1_1reduction_1_1thread_1_1Reduce.html">Reduce</a> < <a class="code" href="structcutlass_1_1plus.html">plus</a><<a class="code" href="structcutlass_1_1half__t.html">half_t</a>>, <a class="code" href="classcutlass_1_1AlignedArray.html">AlignedArray</a><<a class="code" href="structcutlass_1_1half__t.html">half_t</a>, N> > {</div><div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  </div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <a class="code" href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a></div><div class="line"><a name="l00161"></a><span class="lineno"><a class="line" href="structcutlass_1_1reduction_1_1thread_1_1Reduce_3_01plus_3_01half__t_01_4_00_01AlignedArray_3_01half__t_00_01N_01_4_01_4.html#a37155e3dcc591e896df2d80c8daff4d4"> 161</a></span>  Array<half_t, 1> <a class="code" href="structcutlass_1_1reduction_1_1thread_1_1Reduce_3_01plus_3_01half__t_01_4_00_01AlignedArray_3_01half__t_00_01N_01_4_01_4.html#a37155e3dcc591e896df2d80c8daff4d4">operator()</a>(<a class="code" href="classcutlass_1_1AlignedArray.html">AlignedArray<half_t, N></a> <span class="keyword">const</span> &input) {</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span> </div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  Array<half_t, 1> result;</div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span> </div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="comment">// If there is only 1 element - there is nothing to reduce</span></div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <span class="keywordflow">if</span>( N ==1 ){</div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span> </div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  result[0] = input.front();</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span> </div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  } <span class="keywordflow">else</span> {</div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  </div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span> <span class="preprocessor"> #if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 600)</span></div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  </div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  __half result_d;</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  <a class="code" href="classcutlass_1_1AlignedArray.html">AlignedArray<half_t, 1></a> <span class="keyword">const</span> *in_ptr_half = <span class="keyword">reinterpret_cast<</span><a class="code" href="classcutlass_1_1AlignedArray.html">AlignedArray<half_t, 1></a> <span class="keyword">const </span>*<span class="keyword">></span>(&input);</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  <a class="code" href="classcutlass_1_1AlignedArray.html">AlignedArray<half_t, 2></a> <span class="keyword">const</span> *in_ptr_half2 = <span class="keyword">reinterpret_cast<</span><a class="code" href="classcutlass_1_1AlignedArray.html">AlignedArray<half_t, 2></a> <span class="keyword">const </span>*<span class="keyword">></span>(&input);</div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  __half2 <span class="keyword">const</span> *x_in_half2 = <span class="keyword">reinterpret_cast<</span>__half2 <span class="keyword">const </span>*<span class="keyword">></span>(in_ptr_half2);</div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span> </div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="comment">// Set initial result = first half2, in case N==2</span></div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  __half2 tmp_result = x_in_half2[0];</div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span> </div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <a class="code" href="cutlass_8h.html#a4b1c9f25ab6eaa25e1f2258dd63e6ce4">CUTLASS_PRAGMA_UNROLL</a></div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 1; i < N/2; ++i) {</div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span> </div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  tmp_result = __hadd2(x_in_half2[i], tmp_result);</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span> </div><div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  }</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  </div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  result_d = __hadd(__low2half(tmp_result), __high2half(tmp_result));</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  </div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="comment">// One final step is needed for odd "N" (to add the (N-1)th element)</span></div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="keywordflow">if</span>( N%2 ){</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span> </div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  __half last_element;</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  <a class="code" href="classcutlass_1_1AlignedArray.html">AlignedArray<half_t, 1></a> tmp_last;</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <a class="code" href="classcutlass_1_1AlignedArray.html">AlignedArray<half_t, 1></a> *tmp_last_ptr = &tmp_last;</div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  tmp_last_ptr[0] = in_ptr_half[N-1];</div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  last_element = <span class="keyword">reinterpret_cast<</span>__half <span class="keyword">const </span>&<span class="keyword">></span>(tmp_last);</div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> </div><div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  result_d = __hadd(result_d, last_element);</div><div class="line"><a name="l00201"></a><span class="lineno"> 201</span> </div><div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  } </div><div class="line"><a name="l00203"></a><span class="lineno"> 203</span> </div><div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  Array<half_t, 1> *result_ptr = &result;</div><div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  *result_ptr = <span class="keyword">reinterpret_cast<</span>Array<half_t, 1> &<span class="keyword">></span>(result_d);</div><div class="line"><a name="l00206"></a><span class="lineno"> 206</span> </div><div class="line"><a name="l00207"></a><span class="lineno"> 207</span> <span class="preprocessor"> #else</span></div><div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  </div><div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <a class="code" href="structcutlass_1_1reduction_1_1thread_1_1Reduce.html">Reduce< plus<half_t></a>, <a class="code" href="structcutlass_1_1half__t.html">half_t</a> > scalar_reduce;</div><div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  result.clear();</div><div class="line"><a name="l00211"></a><span class="lineno"> 211</span> </div><div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  <a class="code" href="cutlass_8h.html#a4b1c9f25ab6eaa25e1f2258dd63e6ce4">CUTLASS_PRAGMA_UNROLL</a></div><div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i < N; ++i) {</div><div class="line"><a name="l00214"></a><span class="lineno"> 214</span> </div><div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  result[0] = scalar_reduce(result[0], input[i]);</div><div class="line"><a name="l00216"></a><span class="lineno"> 216</span> </div><div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  }</div><div class="line"><a name="l00218"></a><span class="lineno"> 218</span> </div><div class="line"><a name="l00219"></a><span class="lineno"> 219</span> <span class="preprocessor"> #endif</span></div><div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  }</div><div class="line"><a name="l00221"></a><span class="lineno"> 221</span> </div><div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  <span class="keywordflow">return</span> result;</div><div class="line"><a name="l00223"></a><span class="lineno"> 223</span>  </div><div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  }</div><div class="line"><a name="l00225"></a><span class="lineno"> 225</span> };</div><div class="line"><a name="l00226"></a><span class="lineno"> 226</span> }</div><div class="line"><a name="l00227"></a><span class="lineno"> 227</span> }</div><div class="line"><a name="l00228"></a><span class="lineno"> 228</span> }</div><div class="ttc" id="namespacecutlass_html"><div class="ttname"><a href="namespacecutlass.html">cutlass</a></div><div class="ttdef"><b>Definition:</b> aligned_buffer.h:35</div></div>
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<div class="ttc" id="half_8h_html"><div class="ttname"><a href="half_8h.html">half.h</a></div><div class="ttdoc">Defines a class for using IEEE half-precision floating-point types in host or device code...</div></div>
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<div class="ttc" id="classcutlass_1_1AlignedArray_html"><div class="ttname"><a href="classcutlass_1_1AlignedArray.html">cutlass::AlignedArray</a></div><div class="ttdoc">Aligned array type. </div><div class="ttdef"><b>Definition:</b> array.h:511</div></div>
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<div class="ttc" id="structcutlass_1_1half__t_html"><div class="ttname"><a href="structcutlass_1_1half__t.html">cutlass::half_t</a></div><div class="ttdoc">IEEE half-precision floating-point type. </div><div class="ttdef"><b>Definition:</b> half.h:126</div></div>
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<div class="ttc" id="structcutlass_1_1reduction_1_1thread_1_1Reduce_3_01plus_3_01half__t_01_4_00_01AlignedArray_3_01half__t_00_01N_01_4_01_4_html_a37155e3dcc591e896df2d80c8daff4d4"><div class="ttname"><a href="structcutlass_1_1reduction_1_1thread_1_1Reduce_3_01plus_3_01half__t_01_4_00_01AlignedArray_3_01half__t_00_01N_01_4_01_4.html#a37155e3dcc591e896df2d80c8daff4d4">cutlass::reduction::thread::Reduce< plus< half_t >, AlignedArray< half_t, N > >::operator()</a></div><div class="ttdeci">CUTLASS_HOST_DEVICE Array< half_t, 1 > operator()(AlignedArray< half_t, N > const &input)</div><div class="ttdef"><b>Definition:</b> reduce.h:161</div></div>
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<div class="ttc" id="structcutlass_1_1plus_html"><div class="ttname"><a href="structcutlass_1_1plus.html">cutlass::plus</a></div><div class="ttdef"><b>Definition:</b> functional.h:46</div></div>
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<div class="ttc" id="array_8h_html"><div class="ttname"><a href="array_8h.html">array.h</a></div><div class="ttdoc">Statically sized array of elements that accommodates all CUTLASS-supported numeric types and is safe ...</div></div>
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<div class="ttc" id="cutlass_8h_html_a4b1c9f25ab6eaa25e1f2258dd63e6ce4"><div class="ttname"><a href="cutlass_8h.html#a4b1c9f25ab6eaa25e1f2258dd63e6ce4">CUTLASS_PRAGMA_UNROLL</a></div><div class="ttdeci">#define CUTLASS_PRAGMA_UNROLL</div><div class="ttdef"><b>Definition:</b> cutlass.h:110</div></div>
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<div class="ttc" id="structcutlass_1_1reduction_1_1thread_1_1Reduce_3_01plus_3_01T_01_4_00_01Array_3_01T_00_01N_01_4_01_4_html_abb3c25f1d38931ffe40a24307fbf49b1"><div class="ttname"><a href="structcutlass_1_1reduction_1_1thread_1_1Reduce_3_01plus_3_01T_01_4_00_01Array_3_01T_00_01N_01_4_01_4.html#abb3c25f1d38931ffe40a24307fbf49b1">cutlass::reduction::thread::Reduce< plus< T >, Array< T, N > >::operator()</a></div><div class="ttdeci">CUTLASS_HOST_DEVICE Array< T, 1 > operator()(Array< T, N > const &in) const </div><div class="ttdef"><b>Definition:</b> reduce.h:65</div></div>
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<div class="ttc" id="structcutlass_1_1reduction_1_1thread_1_1Reduce_3_01plus_3_01half__t_01_4_00_01Array_3_01half__t_00_01N_01_4_01_4_html_afe8938d7e9d806511480831668ef1563"><div class="ttname"><a href="structcutlass_1_1reduction_1_1thread_1_1Reduce_3_01plus_3_01half__t_01_4_00_01Array_3_01half__t_00_01N_01_4_01_4.html#afe8938d7e9d806511480831668ef1563">cutlass::reduction::thread::Reduce< plus< half_t >, Array< half_t, N > >::operator()</a></div><div class="ttdeci">CUTLASS_HOST_DEVICE Array< half_t, 1 > operator()(Array< half_t, N > const &input)</div><div class="ttdef"><b>Definition:</b> reduce.h:87</div></div>
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<div class="ttc" id="cutlass_8h_html_a28c2443a142676d3d71effdae1a986b1"><div class="ttname"><a href="cutlass_8h.html#a28c2443a142676d3d71effdae1a986b1">CUTLASS_HOST_DEVICE</a></div><div class="ttdeci">#define CUTLASS_HOST_DEVICE</div><div class="ttdef"><b>Definition:</b> cutlass.h:89</div></div>
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<div class="ttc" id="numeric__types_8h_html"><div class="ttname"><a href="numeric__types_8h.html">numeric_types.h</a></div><div class="ttdoc">Top-level include for all CUTLASS numeric types. </div></div>
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<div class="ttc" id="cutlass_8h_html"><div class="ttname"><a href="cutlass_8h.html">cutlass.h</a></div><div class="ttdoc">Basic include for CUTLASS. </div></div>
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<div class="ttc" id="structcutlass_1_1reduction_1_1thread_1_1Reduce_3_01plus_3_01T_01_4_00_01T_01_4_html_adc27a687496822f8477a427bf6ecd8b5"><div class="ttname"><a href="structcutlass_1_1reduction_1_1thread_1_1Reduce_3_01plus_3_01T_01_4_00_01T_01_4.html#adc27a687496822f8477a427bf6ecd8b5">cutlass::reduction::thread::Reduce< plus< T >, T >::operator()</a></div><div class="ttdeci">CUTLASS_HOST_DEVICE T operator()(T lhs, T const &rhs) const </div><div class="ttdef"><b>Definition:</b> reduce.h:52</div></div>
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<div class="ttc" id="functional_8h_html"><div class="ttname"><a href="functional_8h.html">functional.h</a></div><div class="ttdoc">Define basic numeric operators with specializations for Array<T, N>. SIMD-ize where possible...</div></div>
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<div class="ttc" id="structcutlass_1_1reduction_1_1thread_1_1Reduce_html"><div class="ttname"><a href="structcutlass_1_1reduction_1_1thread_1_1Reduce.html">cutlass::reduction::thread::Reduce</a></div><div class="ttdoc">Structure to compute the thread level reduction. </div><div class="ttdef"><b>Definition:</b> reduce.h:43</div></div>
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