2017-02-23 06:26:44 +08:00
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< title > Faiss: DeviceVector.cuh Source File< / title >
2017-02-23 06:26:44 +08:00
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< li class = "navelem" > < a class = "el" href = "dir_6b3ae6988449b0834e9596fad5d75199.html" > gpu< / a > < / li > < li class = "navelem" > < a class = "el" href = "dir_498271007b03b2a0521055e88776887b.html" > utils< / a > < / li > < / ul >
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< div class = "title" > DeviceVector.cuh< / div > < / div >
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< 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" > /**< / span > < / div >
< div class = "line" > < a name = "l00003" > < / a > < span class = "lineno" > 3< / span >   < span class = "comment" > * Copyright (c) 2015-present, Facebook, Inc.< / span > < / div >
< div class = "line" > < a name = "l00004" > < / a > < span class = "lineno" > 4< / span >   < span class = "comment" > * All rights reserved.< / span > < / div >
< div class = "line" > < a name = "l00005" > < / a > < span class = "lineno" > 5< / span >   < span class = "comment" > *< / span > < / div >
< div class = "line" > < a name = "l00006" > < / a > < span class = "lineno" > 6< / span >   < span class = "comment" > * This source code is licensed under the CC-by-NC license found in the< / span > < / div >
< div class = "line" > < a name = "l00007" > < / a > < span class = "lineno" > 7< / span >   < span class = "comment" > * LICENSE file in the root directory of this source tree.< / span > < / div >
< div class = "line" > < a name = "l00008" > < / a > < span class = "lineno" > 8< / span >   < span class = "comment" > */< / span > < / div >
< div class = "line" > < a name = "l00009" > < / a > < span class = "lineno" > 9< / span >   < / div >
< div class = "line" > < a name = "l00010" > < / a > < span class = "lineno" > 10< / span >   < span class = "comment" > // Copyright 2004-present Facebook. All Rights Reserved.< / span > < / div >
< div class = "line" > < a name = "l00011" > < / a > < span class = "lineno" > 11< / span >   < / div >
< div class = "line" > < a name = "l00012" > < / a > < span class = "lineno" > 12< / span >   < span class = "preprocessor" > #pragma once< / span > < / div >
< div class = "line" > < a name = "l00013" > < / a > < span class = "lineno" > 13< / span >   < span class = "preprocessor" > < / span > < / div >
< div class = "line" > < a name = "l00014" > < / a > < span class = "lineno" > 14< / span >   < span class = "preprocessor" > #include " ../../FaissAssert.h" < / span > < / div >
< div class = "line" > < a name = "l00015" > < / a > < span class = "lineno" > 15< / span >   < span class = "preprocessor" > #include " DeviceUtils.h" < / span > < / div >
< div class = "line" > < a name = "l00016" > < / a > < span class = "lineno" > 16< / span >   < span class = "preprocessor" > #include " StaticUtils.h" < / span > < / div >
< div class = "line" > < a name = "l00017" > < / a > < span class = "lineno" > 17< / span >   < span class = "preprocessor" > #include < algorithm> < / span > < / div >
< div class = "line" > < a name = "l00018" > < / a > < span class = "lineno" > 18< / span >   < span class = "preprocessor" > #include < cuda.h> < / span > < / div >
< div class = "line" > < a name = "l00019" > < / a > < span class = "lineno" > 19< / span >   < span class = "preprocessor" > #include < vector> < / span > < / div >
< div class = "line" > < a name = "l00020" > < / a > < span class = "lineno" > 20< / span >   < / div >
< div class = "line" > < a name = "l00021" > < / a > < span class = "lineno" > 21< / span >   < span class = "keyword" > namespace < / span > faiss { < span class = "keyword" > namespace < / span > gpu {< / div >
< div class = "line" > < a name = "l00022" > < / a > < span class = "lineno" > 22< / span >   < span class = "comment" > < / span > < / div >
< div class = "line" > < a name = "l00023" > < / a > < span class = "lineno" > 23< / span >   < span class = "comment" > /// A simple version of thrust::device_vector< T> , but has more control< / span > < / div >
< div class = "line" > < a name = "l00024" > < / a > < span class = "lineno" > 24< / span >   < span class = "comment" > /// over whether resize() initializes new space with T() (which we< / span > < / div >
< div class = "line" > < a name = "l00025" > < / a > < span class = "lineno" > 25< / span >   < span class = "comment" > /// don' t want), and control on how much the reserved space grows by< / span > < / div >
< div class = "line" > < a name = "l00026" > < / a > < span class = "lineno" > 26< / span >   < span class = "comment" > /// upon resize/reserve. It is also meant for POD types only.< / span > < / div >
< div class = "line" > < a name = "l00027" > < / a > < span class = "lineno" > 27< / span >   < span class = "comment" > < / span > < span class = "keyword" > template< / span > < < span class = "keyword" > typename< / span > T> < / div >
< div class = "line" > < a name = "l00028" > < / a > < span class = "lineno" > < a class = "line" href = "classfaiss_1_1gpu_1_1DeviceVector.html" > 28< / a > < / span >   < span class = "keyword" > class < / span > < a class = "code" href = "classfaiss_1_1gpu_1_1DeviceVector.html" > DeviceVector< / a > {< / div >
< div class = "line" > < a name = "l00029" > < / a > < span class = "lineno" > 29< / span >   < span class = "keyword" > public< / span > :< / div >
< div class = "line" > < a name = "l00030" > < / a > < span class = "lineno" > 30< / span >   < a class = "code" href = "classfaiss_1_1gpu_1_1DeviceVector.html" > DeviceVector< / a > ()< / div >
< div class = "line" > < a name = "l00031" > < / a > < span class = "lineno" > 31< / span >   : data_(< span class = "keyword" > nullptr< / span > ),< / div >
< div class = "line" > < a name = "l00032" > < / a > < span class = "lineno" > 32< / span >   num_(0),< / div >
< div class = "line" > < a name = "l00033" > < / a > < span class = "lineno" > 33< / span >   capacity_(0) {< / div >
< div class = "line" > < a name = "l00034" > < / a > < span class = "lineno" > 34< / span >   }< / div >
< div class = "line" > < a name = "l00035" > < / a > < span class = "lineno" > 35< / span >   < / div >
< div class = "line" > < a name = "l00036" > < / a > < span class = "lineno" > 36< / span >   ~< a class = "code" href = "classfaiss_1_1gpu_1_1DeviceVector.html" > DeviceVector< / a > () {< / div >
< div class = "line" > < a name = "l00037" > < / a > < span class = "lineno" > 37< / span >   clear();< / div >
< div class = "line" > < a name = "l00038" > < / a > < span class = "lineno" > 38< / span >   }< / div >
< div class = "line" > < a name = "l00039" > < / a > < span class = "lineno" > 39< / span >   < / div >
< div class = "line" > < a name = "l00040" > < / a > < span class = "lineno" > 40< / span >   < span class = "comment" > // Clear all allocated memory; reset to zero size< / span > < / div >
< div class = "line" > < a name = "l00041" > < / a > < span class = "lineno" > 41< / span >   < span class = "keywordtype" > void< / span > clear() {< / div >
< div class = "line" > < a name = "l00042" > < / a > < span class = "lineno" > 42< / span >   CUDA_VERIFY(cudaFree(data_));< / div >
< div class = "line" > < a name = "l00043" > < / a > < span class = "lineno" > 43< / span >   data_ = < span class = "keyword" > nullptr< / span > ;< / div >
< div class = "line" > < a name = "l00044" > < / a > < span class = "lineno" > 44< / span >   num_ = 0;< / div >
< div class = "line" > < a name = "l00045" > < / a > < span class = "lineno" > 45< / span >   capacity_ = 0;< / div >
< div class = "line" > < a name = "l00046" > < / a > < span class = "lineno" > 46< / span >   }< / div >
< div class = "line" > < a name = "l00047" > < / a > < span class = "lineno" > 47< / span >   < / div >
< div class = "line" > < a name = "l00048" > < / a > < span class = "lineno" > 48< / span >   < span class = "keywordtype" > size_t< / span > size()< span class = "keyword" > const < / span > { < span class = "keywordflow" > return< / span > num_; }< / div >
< div class = "line" > < a name = "l00049" > < / a > < span class = "lineno" > 49< / span >   < span class = "keywordtype" > size_t< / span > capacity()< span class = "keyword" > const < / span > { < span class = "keywordflow" > return< / span > capacity_; }< / div >
< div class = "line" > < a name = "l00050" > < / a > < span class = "lineno" > 50< / span >   T* data() { < span class = "keywordflow" > return< / span > data_; }< / div >
< div class = "line" > < a name = "l00051" > < / a > < span class = "lineno" > 51< / span >   < span class = "keyword" > const< / span > T* data()< span class = "keyword" > const < / span > { < span class = "keywordflow" > return< / span > data_; }< / div >
< div class = "line" > < a name = "l00052" > < / a > < span class = "lineno" > 52< / span >   < / div >
< div class = "line" > < a name = "l00053" > < / a > < span class = "lineno" > 53< / span >   < span class = "keyword" > template< / span > < < span class = "keyword" > typename< / span > OutT> < / div >
< div class = "line" > < a name = "l00054" > < / a > < span class = "lineno" > 54< / span >   std::vector< OutT> copyToHost(cudaStream_t stream)< span class = "keyword" > const < / span > {< / div >
< div class = "line" > < a name = "l00055" > < / a > < span class = "lineno" > 55< / span >   FAISS_ASSERT(num_ * < span class = "keyword" > sizeof< / span > (T) % < span class = "keyword" > sizeof< / span > (OutT) == 0);< / 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 >   std::vector< OutT> out((num_ * < span class = "keyword" > sizeof< / span > (T)) / < span class = "keyword" > sizeof< / span > (OutT));< / div >
< div class = "line" > < a name = "l00058" > < / a > < span class = "lineno" > 58< / span >   CUDA_VERIFY(cudaMemcpyAsync(out.data(), data_, num_ * < span class = "keyword" > sizeof< / span > (T),< / div >
< div class = "line" > < a name = "l00059" > < / a > < span class = "lineno" > 59< / span >   cudaMemcpyDeviceToHost, stream));< / div >
< div class = "line" > < a name = "l00060" > < / a > < span class = "lineno" > 60< / span >   < / div >
< div class = "line" > < a name = "l00061" > < / a > < span class = "lineno" > 61< / span >   < span class = "keywordflow" > return< / span > out;< / div >
< div class = "line" > < a name = "l00062" > < / a > < span class = "lineno" > 62< / span >   }< / 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 >   < span class = "comment" > // Returns true if we actually reallocated memory< / span > < / div >
< div class = "line" > < a name = "l00065" > < / a > < span class = "lineno" > 65< / span >   < span class = "comment" > // If `reserveExact` is true, then we reserve only the memory that< / span > < / div >
< div class = "line" > < a name = "l00066" > < / a > < span class = "lineno" > 66< / span >   < span class = "comment" > // we need for what we' re appending< / span > < / div >
< div class = "line" > < a name = "l00067" > < / a > < span class = "lineno" > 67< / span >   < span class = "keywordtype" > bool< / span > append(< span class = "keyword" > const< / span > T* d,< / div >
< div class = "line" > < a name = "l00068" > < / a > < span class = "lineno" > 68< / span >   < span class = "keywordtype" > size_t< / span > n,< / div >
< div class = "line" > < a name = "l00069" > < / a > < span class = "lineno" > 69< / span >   cudaStream_t stream,< / div >
< div class = "line" > < a name = "l00070" > < / a > < span class = "lineno" > 70< / span >   < span class = "keywordtype" > bool< / span > reserveExact = < span class = "keyword" > false< / span > ) {< / div >
< div class = "line" > < a name = "l00071" > < / a > < span class = "lineno" > 71< / span >   < span class = "keywordtype" > bool< / span > mem = < span class = "keyword" > false< / span > ;< / div >
< div class = "line" > < a name = "l00072" > < / a > < span class = "lineno" > 72< / span >   < / div >
< div class = "line" > < a name = "l00073" > < / a > < span class = "lineno" > 73< / span >   < span class = "keywordflow" > if< / span > (n > 0) {< / div >
< div class = "line" > < a name = "l00074" > < / a > < span class = "lineno" > 74< / span >   < span class = "keywordtype" > size_t< / span > reserveSize = num_ + n;< / div >
< div class = "line" > < a name = "l00075" > < / a > < span class = "lineno" > 75< / span >   < span class = "keywordflow" > if< / span > (!reserveExact) {< / div >
< div class = "line" > < a name = "l00076" > < / a > < span class = "lineno" > 76< / span >   reserveSize = getNewCapacity_(reserveSize);< / 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 >   mem = reserve(reserveSize, stream);< / div >
< div class = "line" > < a name = "l00080" > < / a > < span class = "lineno" > 80< / span >   < / div >
< div class = "line" > < a name = "l00081" > < / a > < span class = "lineno" > 81< / span >   < span class = "keywordtype" > int< / span > dev = getDeviceForAddress(d);< / div >
< div class = "line" > < a name = "l00082" > < / a > < span class = "lineno" > 82< / span >   < span class = "keywordflow" > if< / span > (dev == -1) {< / div >
< div class = "line" > < a name = "l00083" > < / a > < span class = "lineno" > 83< / span >   CUDA_VERIFY(cudaMemcpyAsync(data_ + num_, d, n * < span class = "keyword" > sizeof< / span > (T),< / div >
< div class = "line" > < a name = "l00084" > < / a > < span class = "lineno" > 84< / span >   cudaMemcpyHostToDevice, stream));< / div >
< div class = "line" > < a name = "l00085" > < / a > < span class = "lineno" > 85< / span >   } < span class = "keywordflow" > else< / span > {< / div >
< div class = "line" > < a name = "l00086" > < / a > < span class = "lineno" > 86< / span >   CUDA_VERIFY(cudaMemcpyAsync(data_ + num_, d, n * < span class = "keyword" > sizeof< / span > (T),< / div >
< div class = "line" > < a name = "l00087" > < / a > < span class = "lineno" > 87< / span >   cudaMemcpyDeviceToDevice, stream));< / 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 >   num_ += n;< / 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 >   < / div >
< div class = "line" > < a name = "l00092" > < / a > < span class = "lineno" > 92< / span >   < span class = "keywordflow" > return< / span > mem;< / 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 >   < / div >
< div class = "line" > < a name = "l00095" > < / a > < span class = "lineno" > 95< / span >   < span class = "comment" > // Returns true if we actually reallocated memory< / span > < / div >
< div class = "line" > < a name = "l00096" > < / a > < span class = "lineno" > 96< / span >   < span class = "keywordtype" > bool< / span > resize(< span class = "keywordtype" > size_t< / span > newSize, cudaStream_t stream) {< / div >
< div class = "line" > < a name = "l00097" > < / a > < span class = "lineno" > 97< / span >   < span class = "keywordtype" > bool< / span > mem = < span class = "keyword" > false< / span > ;< / div >
< div class = "line" > < a name = "l00098" > < / a > < span class = "lineno" > 98< / span >   < / div >
< div class = "line" > < a name = "l00099" > < / a > < span class = "lineno" > 99< / span >   < span class = "keywordflow" > if< / span > (num_ < newSize) {< / div >
< div class = "line" > < a name = "l00100" > < / a > < span class = "lineno" > 100< / span >   mem = reserve(getNewCapacity_(newSize), stream);< / div >
< div class = "line" > < a name = "l00101" > < / a > < span class = "lineno" > 101< / span >   }< / div >
< div class = "line" > < a name = "l00102" > < / a > < span class = "lineno" > 102< / span >   < / div >
< div class = "line" > < a name = "l00103" > < / a > < span class = "lineno" > 103< / span >   < span class = "comment" > // Don' t bother zero initializing the newly accessible memory< / span > < / div >
< div class = "line" > < a name = "l00104" > < / a > < span class = "lineno" > 104< / span >   < span class = "comment" > // (unlike thrust::device_vector)< / span > < / div >
< div class = "line" > < a name = "l00105" > < / a > < span class = "lineno" > 105< / span >   num_ = newSize;< / div >
< div class = "line" > < a name = "l00106" > < / a > < span class = "lineno" > 106< / span >   < / div >
< div class = "line" > < a name = "l00107" > < / a > < span class = "lineno" > 107< / span >   < span class = "keywordflow" > return< / span > mem;< / div >
< div class = "line" > < a name = "l00108" > < / a > < span class = "lineno" > 108< / span >   }< / div >
< div class = "line" > < a name = "l00109" > < / a > < span class = "lineno" > 109< / span >   < / div >
< div class = "line" > < a name = "l00110" > < / a > < span class = "lineno" > 110< / span >   < span class = "comment" > // Clean up after oversized allocations, while leaving some space to< / span > < / div >
< div class = "line" > < a name = "l00111" > < / a > < span class = "lineno" > 111< / span >   < span class = "comment" > // remain for subsequent allocations (if `exact` false) or to< / span > < / div >
< div class = "line" > < a name = "l00112" > < / a > < span class = "lineno" > 112< / span >   < span class = "comment" > // exactly the space we need (if `exact` true); returns space< / span > < / div >
< div class = "line" > < a name = "l00113" > < / a > < span class = "lineno" > 113< / span >   < span class = "comment" > // reclaimed in bytes< / span > < / div >
< div class = "line" > < a name = "l00114" > < / a > < span class = "lineno" > 114< / span >   < span class = "keywordtype" > size_t< / span > reclaim(< span class = "keywordtype" > bool< / span > exact, cudaStream_t stream) {< / div >
< div class = "line" > < a name = "l00115" > < / a > < span class = "lineno" > 115< / span >   < span class = "keywordtype" > size_t< / span > free = capacity_ - num_;< / 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 = "keywordflow" > if< / span > (exact) {< / div >
< div class = "line" > < a name = "l00118" > < / a > < span class = "lineno" > 118< / span >   realloc_(num_, stream);< / div >
< div class = "line" > < a name = "l00119" > < / a > < span class = "lineno" > 119< / span >   < span class = "keywordflow" > return< / span > free * < span class = "keyword" > sizeof< / span > (T);< / div >
< div class = "line" > < a name = "l00120" > < / a > < span class = "lineno" > 120< / span >   }< / div >
< div class = "line" > < a name = "l00121" > < / a > < span class = "lineno" > 121< / span >   < / div >
< div class = "line" > < a name = "l00122" > < / a > < span class = "lineno" > 122< / span >   < span class = "comment" > // If more than 1/4th of the space is free, then we want to< / span > < / div >
< div class = "line" > < a name = "l00123" > < / a > < span class = "lineno" > 123< / span >   < span class = "comment" > // truncate to only having 1/8th of the space free; this still< / span > < / div >
< div class = "line" > < a name = "l00124" > < / a > < span class = "lineno" > 124< / span >   < span class = "comment" > // preserves some space for new elements, but won' t force us to< / span > < / div >
< div class = "line" > < a name = "l00125" > < / a > < span class = "lineno" > 125< / span >   < span class = "comment" > // double our size right away< / span > < / div >
< div class = "line" > < a name = "l00126" > < / a > < span class = "lineno" > 126< / span >   < span class = "keywordflow" > if< / span > (free > (capacity_ / 4)) {< / div >
< div class = "line" > < a name = "l00127" > < / a > < span class = "lineno" > 127< / span >   < span class = "keywordtype" > size_t< / span > newFree = capacity_ / 8;< / div >
< div class = "line" > < a name = "l00128" > < / a > < span class = "lineno" > 128< / span >   < span class = "keywordtype" > size_t< / span > newCapacity = num_ + newFree;< / 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 >   < span class = "keywordtype" > size_t< / span > oldCapacity = capacity_;< / div >
< div class = "line" > < a name = "l00131" > < / a > < span class = "lineno" > 131< / span >   FAISS_ASSERT(newCapacity < oldCapacity);< / 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 >   realloc_(newCapacity, stream);< / 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 >   < span class = "keywordflow" > return< / span > (oldCapacity - newCapacity) * < span class = "keyword" > sizeof< / span > (T);< / div >
< div class = "line" > < a name = "l00136" > < / a > < span class = "lineno" > 136< / span >   }< / 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 >   < span class = "keywordflow" > return< / span > 0;< / div >
< div class = "line" > < a name = "l00139" > < / a > < span class = "lineno" > 139< / span >   }< / 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 >   < span class = "comment" > // Returns true if we actually reallocated memory< / span > < / div >
< div class = "line" > < a name = "l00142" > < / a > < span class = "lineno" > 142< / span >   < span class = "keywordtype" > bool< / span > reserve(< span class = "keywordtype" > size_t< / span > newCapacity, cudaStream_t stream) {< / div >
< div class = "line" > < a name = "l00143" > < / a > < span class = "lineno" > 143< / span >   < span class = "keywordflow" > if< / span > (newCapacity < = capacity_) {< / div >
< div class = "line" > < a name = "l00144" > < / a > < span class = "lineno" > 144< / span >   < span class = "keywordflow" > return< / span > < span class = "keyword" > false< / span > ;< / div >
< div class = "line" > < a name = "l00145" > < / a > < span class = "lineno" > 145< / 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 >   < span class = "comment" > // Otherwise, we need new space.< / span > < / div >
< div class = "line" > < a name = "l00148" > < / a > < span class = "lineno" > 148< / span >   realloc_(newCapacity, stream);< / div >
< div class = "line" > < a name = "l00149" > < / a > < span class = "lineno" > 149< / span >   < span class = "keywordflow" > return< / span > < span class = "keyword" > true< / 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 >   < span class = "keyword" > private< / span > :< / div >
< div class = "line" > < a name = "l00153" > < / a > < span class = "lineno" > 153< / span >   < span class = "keywordtype" > void< / span > realloc_(< span class = "keywordtype" > size_t< / span > newCapacity, cudaStream_t stream) {< / div >
< div class = "line" > < a name = "l00154" > < / a > < span class = "lineno" > 154< / span >   FAISS_ASSERT(num_ < = newCapacity);< / div >
< div class = "line" > < a name = "l00155" > < / a > < span class = "lineno" > 155< / span >   < / div >
< div class = "line" > < a name = "l00156" > < / a > < span class = "lineno" > 156< / span >   T* newData = < span class = "keyword" > nullptr< / span > ;< / div >
< div class = "line" > < a name = "l00157" > < / a > < span class = "lineno" > 157< / span >   CUDA_VERIFY(cudaMalloc(& newData, newCapacity * < span class = "keyword" > sizeof< / span > (T)));< / div >
< div class = "line" > < a name = "l00158" > < / a > < span class = "lineno" > 158< / span >   CUDA_VERIFY(cudaMemcpyAsync(newData, data_, num_ * < span class = "keyword" > sizeof< / span > (T),< / div >
< div class = "line" > < a name = "l00159" > < / a > < span class = "lineno" > 159< / span >   cudaMemcpyDeviceToDevice, stream));< / div >
< div class = "line" > < a name = "l00160" > < / a > < span class = "lineno" > 160< / span >   < span class = "comment" > // FIXME: keep on reclamation queue to avoid hammering cudaFree?< / span > < / div >
< div class = "line" > < a name = "l00161" > < / a > < span class = "lineno" > 161< / span >   CUDA_VERIFY(cudaFree(data_));< / 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 >   data_ = newData;< / div >
< div class = "line" > < a name = "l00164" > < / a > < span class = "lineno" > 164< / span >   capacity_ = newCapacity;< / div >
< div class = "line" > < a name = "l00165" > < / a > < span class = "lineno" > 165< / span >   }< / div >
< div class = "line" > < a name = "l00166" > < / a > < span class = "lineno" > 166< / span >   < / div >
< div class = "line" > < a name = "l00167" > < / a > < span class = "lineno" > 167< / span >   < span class = "keywordtype" > size_t< / span > getNewCapacity_(< span class = "keywordtype" > size_t< / span > preferredSize) {< / div >
< div class = "line" > < a name = "l00168" > < / a > < span class = "lineno" > 168< / span >   < span class = "keywordflow" > return< / span > utils::nextHighestPowerOf2(preferredSize);< / 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 >   < / div >
< div class = "line" > < a name = "l00171" > < / a > < span class = "lineno" > 171< / span >   T* data_;< / div >
< div class = "line" > < a name = "l00172" > < / a > < span class = "lineno" > 172< / span >   < span class = "keywordtype" > size_t< / span > num_;< / div >
< div class = "line" > < a name = "l00173" > < / a > < span class = "lineno" > 173< / span >   < span class = "keywordtype" > size_t< / span > capacity_;< / div >
< div class = "line" > < a name = "l00174" > < / a > < span class = "lineno" > 174< / span >   };< / div >
< div class = "line" > < a name = "l00175" > < / a > < span class = "lineno" > 175< / span >   < / div >
< div class = "line" > < a name = "l00176" > < / a > < span class = "lineno" > 176< / span >   } } < span class = "comment" > // namespace< / span > < / div >
< div class = "ttc" id = "classfaiss_1_1gpu_1_1DeviceVector_html" > < div class = "ttname" > < a href = "classfaiss_1_1gpu_1_1DeviceVector.html" > faiss::gpu::DeviceVector< / a > < / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "DeviceVector_8cuh_source.html#l00028" > DeviceVector.cuh:28< / a > < / div > < / div >
< / div > <!-- fragment --> < / div > <!-- contents -->
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