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<li class="navelem"><a class="el" href="dir_5956a3e80a20e8e03eb577bedb92689f.html">gpu</a></li><li class="navelem"><a class="el" href="dir_2be73404b46ec2282840cd36fdb9a907.html">impl</a></li> </ul>
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<div class="title">BinaryFlatIndex.cu</div> </div>
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<div class="contents">
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">/**</span></div>
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<div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment"> * Copyright (c) Facebook, Inc. and its affiliates.</span></div>
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<div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment"> *</span></div>
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<div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment"> * This source code is licensed under the MIT license found in the</span></div>
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<div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment"> * LICENSE file in the root directory of this source tree.</span></div>
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<div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment"> */</span></div>
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<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> </div>
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<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> </div>
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<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="preprocessor">#include "BinaryFlatIndex.cuh"</span></div>
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<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="preprocessor">#include "BinaryDistance.cuh"</span></div>
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<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="preprocessor">#include "../utils/DeviceUtils.h"</span></div>
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<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="preprocessor">#include "../GpuResources.h"</span></div>
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<div class="line"><a name="l00013"></a><span class="lineno"> 13</span> </div>
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<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="keyword">namespace </span>faiss { <span class="keyword">namespace </span>gpu {</div>
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<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> </div>
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<div class="line"><a name="l00016"></a><span class="lineno"> 16</span> BinaryFlatIndex::BinaryFlatIndex(GpuResources* res,</div>
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<div class="line"><a name="l00017"></a><span class="lineno"> 17</span>  <span class="keywordtype">int</span> dim,</div>
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<div class="line"><a name="l00018"></a><span class="lineno"> 18</span>  MemorySpace space) :</div>
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<div class="line"><a name="l00019"></a><span class="lineno"> 19</span>  resources_(res),</div>
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<div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  dim_(dim),</div>
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<div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  space_(space),</div>
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<div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  num_(0),</div>
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<div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  rawData_(space) {</div>
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<div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  FAISS_ASSERT(dim % 8 == 0);</div>
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<div class="line"><a name="l00025"></a><span class="lineno"> 25</span> }</div>
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<div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="comment"></span></div>
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<div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="comment">/// Returns the number of vectors we contain</span></div>
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<div class="line"><a name="l00028"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1BinaryFlatIndex.html#aa3440ccb9bc693cbc89386b8cc981565"> 28</a></span> <span class="comment"></span><span class="keywordtype">int</span> <a class="code" href="classfaiss_1_1gpu_1_1BinaryFlatIndex.html#aa3440ccb9bc693cbc89386b8cc981565">BinaryFlatIndex::getSize</a>()<span class="keyword"> const </span>{</div>
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<div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <span class="keywordflow">return</span> vectors_.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(0);</div>
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<div class="line"><a name="l00030"></a><span class="lineno"> 30</span> }</div>
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<div class="line"><a name="l00031"></a><span class="lineno"> 31</span> </div>
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<div class="line"><a name="l00032"></a><span class="lineno"> 32</span> <span class="keywordtype">int</span> BinaryFlatIndex::getDim()<span class="keyword"> const </span>{</div>
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<div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keywordflow">return</span> vectors_.<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">getSize</a>(1) * 8;</div>
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<div class="line"><a name="l00034"></a><span class="lineno"> 34</span> }</div>
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<div class="line"><a name="l00035"></a><span class="lineno"> 35</span> </div>
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<div class="line"><a name="l00036"></a><span class="lineno"> 36</span> <span class="keywordtype">void</span></div>
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<div class="line"><a name="l00037"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1BinaryFlatIndex.html#a897e1b584bda7a949e5e8644d0bc972b"> 37</a></span> <a class="code" href="classfaiss_1_1gpu_1_1BinaryFlatIndex.html#a897e1b584bda7a949e5e8644d0bc972b">BinaryFlatIndex::reserve</a>(<span class="keywordtype">size_t</span> numVecs, cudaStream_t stream) {</div>
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<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  rawData_.reserve(numVecs * (dim_ / 8) * <span class="keyword">sizeof</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>), stream);</div>
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<div class="line"><a name="l00039"></a><span class="lineno"> 39</span> }</div>
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<div class="line"><a name="l00040"></a><span class="lineno"> 40</span> </div>
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<div class="line"><a name="l00041"></a><span class="lineno"> 41</span> <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<unsigned char, 2, true></a>&</div>
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<div class="line"><a name="l00042"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1BinaryFlatIndex.html#a0cc143e913b7a06e3ed31e5a6b546a6b"> 42</a></span> <a class="code" href="classfaiss_1_1gpu_1_1BinaryFlatIndex.html#a0cc143e913b7a06e3ed31e5a6b546a6b">BinaryFlatIndex::getVectorsRef</a>() {</div>
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<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  <span class="keywordflow">return</span> vectors_;</div>
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<div class="line"><a name="l00044"></a><span class="lineno"> 44</span> }</div>
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<div class="line"><a name="l00045"></a><span class="lineno"> 45</span> </div>
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<div class="line"><a name="l00046"></a><span class="lineno"> 46</span> <span class="keywordtype">void</span></div>
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<div class="line"><a name="l00047"></a><span class="lineno"> 47</span> BinaryFlatIndex::query(<a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<unsigned char, 2, true></a>& input,</div>
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<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  <span class="keywordtype">int</span> k,</div>
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<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<int, 2, true></a>& outDistances,</div>
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<div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <a class="code" href="classfaiss_1_1gpu_1_1Tensor.html">Tensor<int, 2, true></a>& outIndices) {</div>
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<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  <span class="keyword">auto</span> stream = resources_-><a class="code" href="classfaiss_1_1gpu_1_1GpuResources.html#aa0354aa570c24e17a9f8a6a45b153ed2">getDefaultStreamCurrentDevice</a>();</div>
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<div class="line"><a name="l00052"></a><span class="lineno"> 52</span> </div>
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<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  runBinaryDistance(vectors_,</div>
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<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  input,</div>
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<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  outDistances,</div>
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<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  outIndices,</div>
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<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  k,</div>
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<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  stream);</div>
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<div class="line"><a name="l00059"></a><span class="lineno"> 59</span> }</div>
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<div class="line"><a name="l00060"></a><span class="lineno"> 60</span> </div>
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<div class="line"><a name="l00061"></a><span class="lineno"> 61</span> <span class="keywordtype">void</span></div>
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<div class="line"><a name="l00062"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1BinaryFlatIndex.html#aa633251a14afbbf6a6652ae193b68790"> 62</a></span> <a class="code" href="classfaiss_1_1gpu_1_1BinaryFlatIndex.html#aa633251a14afbbf6a6652ae193b68790">BinaryFlatIndex::add</a>(<span class="keyword">const</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">char</span>* data,</div>
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<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="keywordtype">int</span> numVecs,</div>
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<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  cudaStream_t stream) {</div>
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<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keywordflow">if</span> (numVecs == 0) {</div>
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<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="keywordflow">return</span>;</div>
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<div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  }</div>
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<div class="line"><a name="l00068"></a><span class="lineno"> 68</span> </div>
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<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  rawData_.append((<span class="keywordtype">char</span>*) data,</div>
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<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  (<span class="keywordtype">size_t</span>) (dim_ / 8) * numVecs * <span class="keyword">sizeof</span>(<span class="keywordtype">unsigned</span> <span class="keywordtype">char</span>),</div>
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<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  stream,</div>
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<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keyword">true</span> <span class="comment">/* reserve exactly */</span>);</div>
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<div class="line"><a name="l00073"></a><span class="lineno"> 73</span> </div>
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<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  num_ += numVecs;</div>
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<div class="line"><a name="l00075"></a><span class="lineno"> 75</span> </div>
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<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <a class="code" href="classfaiss_1_1gpu_1_1DeviceTensor.html">DeviceTensor<unsigned char, 2, true></a> vectors(</div>
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<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  (<span class="keywordtype">unsigned</span> <span class="keywordtype">char</span>*) rawData_.data(), {(int) num_, (dim_ / 8)}, space_);</div>
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<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  vectors_ = std::move(vectors);</div>
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<div class="line"><a name="l00079"></a><span class="lineno"> 79</span> }</div>
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<div class="line"><a name="l00080"></a><span class="lineno"> 80</span> </div>
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<div class="line"><a name="l00081"></a><span class="lineno"> 81</span> <span class="keywordtype">void</span></div>
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<div class="line"><a name="l00082"></a><span class="lineno"><a class="line" href="classfaiss_1_1gpu_1_1BinaryFlatIndex.html#a6b31cef9564d47f0d188ae34c4b5eacc"> 82</a></span> <a class="code" href="classfaiss_1_1gpu_1_1BinaryFlatIndex.html#a6b31cef9564d47f0d188ae34c4b5eacc">BinaryFlatIndex::reset</a>() {</div>
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<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  rawData_.clear();</div>
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<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  vectors_ = std::move(<a class="code" href="classfaiss_1_1gpu_1_1DeviceTensor.html">DeviceTensor<unsigned char, 2, true></a>());</div>
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<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  num_ = 0;</div>
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<div class="line"><a name="l00086"></a><span class="lineno"> 86</span> }</div>
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<div class="line"><a name="l00087"></a><span class="lineno"> 87</span> </div>
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<div class="line"><a name="l00088"></a><span class="lineno"> 88</span> } }</div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1BinaryFlatIndex_html_a6b31cef9564d47f0d188ae34c4b5eacc"><div class="ttname"><a href="classfaiss_1_1gpu_1_1BinaryFlatIndex.html#a6b31cef9564d47f0d188ae34c4b5eacc">faiss::gpu::BinaryFlatIndex::reset</a></div><div class="ttdeci">void reset()</div><div class="ttdoc">Free all storage. </div><div class="ttdef"><b>Definition:</b> <a href="BinaryFlatIndex_8cu_source.html#l00082">BinaryFlatIndex.cu:82</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1GpuResources_html_aa0354aa570c24e17a9f8a6a45b153ed2"><div class="ttname"><a href="classfaiss_1_1gpu_1_1GpuResources.html#aa0354aa570c24e17a9f8a6a45b153ed2">faiss::gpu::GpuResources::getDefaultStreamCurrentDevice</a></div><div class="ttdeci">cudaStream_t getDefaultStreamCurrentDevice()</div><div class="ttdoc">Calls getDefaultStream with the current device. </div><div class="ttdef"><b>Definition:</b> <a href="GpuResources_8cpp_source.html#l00023">GpuResources.cpp:23</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html_a6699c311648457f257afa340c61f417c"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c">faiss::gpu::Tensor::getSize</a></div><div class="ttdeci">__host__ __device__ IndexT getSize(int i) const </div><div class="ttdef"><b>Definition:</b> <a href="Tensor_8cuh_source.html#l00222">Tensor.cuh:222</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1Tensor_html"><div class="ttname"><a href="classfaiss_1_1gpu_1_1Tensor.html">faiss::gpu::Tensor</a></div><div class="ttdoc">Our tensor type. </div><div class="ttdef"><b>Definition:</b> <a href="Tensor_8cuh_source.html#l00028">Tensor.cuh:28</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1BinaryFlatIndex_html_aa3440ccb9bc693cbc89386b8cc981565"><div class="ttname"><a href="classfaiss_1_1gpu_1_1BinaryFlatIndex.html#aa3440ccb9bc693cbc89386b8cc981565">faiss::gpu::BinaryFlatIndex::getSize</a></div><div class="ttdeci">int getSize() const </div><div class="ttdoc">Returns the number of vectors we contain. </div><div class="ttdef"><b>Definition:</b> <a href="BinaryFlatIndex_8cu_source.html#l00028">BinaryFlatIndex.cu:28</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1BinaryFlatIndex_html_a897e1b584bda7a949e5e8644d0bc972b"><div class="ttname"><a href="classfaiss_1_1gpu_1_1BinaryFlatIndex.html#a897e1b584bda7a949e5e8644d0bc972b">faiss::gpu::BinaryFlatIndex::reserve</a></div><div class="ttdeci">void reserve(size_t numVecs, cudaStream_t stream)</div><div class="ttdoc">Reserve storage that can contain at least this many vectors. </div><div class="ttdef"><b>Definition:</b> <a href="BinaryFlatIndex_8cu_source.html#l00037">BinaryFlatIndex.cu:37</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1DeviceTensor_html"><div class="ttname"><a href="classfaiss_1_1gpu_1_1DeviceTensor.html">faiss::gpu::DeviceTensor< unsigned char, 2, true ></a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1BinaryFlatIndex_html_a0cc143e913b7a06e3ed31e5a6b546a6b"><div class="ttname"><a href="classfaiss_1_1gpu_1_1BinaryFlatIndex.html#a0cc143e913b7a06e3ed31e5a6b546a6b">faiss::gpu::BinaryFlatIndex::getVectorsRef</a></div><div class="ttdeci">Tensor< unsigned char, 2, true > & getVectorsRef()</div><div class="ttdoc">Returns a reference to our vectors currently in use. </div><div class="ttdef"><b>Definition:</b> <a href="BinaryFlatIndex_8cu_source.html#l00042">BinaryFlatIndex.cu:42</a></div></div>
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<div class="ttc" id="classfaiss_1_1gpu_1_1BinaryFlatIndex_html_aa633251a14afbbf6a6652ae193b68790"><div class="ttname"><a href="classfaiss_1_1gpu_1_1BinaryFlatIndex.html#aa633251a14afbbf6a6652ae193b68790">faiss::gpu::BinaryFlatIndex::add</a></div><div class="ttdeci">void add(const unsigned char *data, int numVecs, cudaStream_t stream)</div><div class="ttdef"><b>Definition:</b> <a href="BinaryFlatIndex_8cu_source.html#l00062">BinaryFlatIndex.cu:62</a></div></div>
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