1 | #include "grid_remote_connector.hpp" |
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2 | #include "client_client_dht_template.hpp" |
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3 | #include "leader_process.hpp" |
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4 | #include "mpi.hpp" |
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5 | #include "element.hpp" |
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6 | |
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7 | |
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8 | |
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9 | namespace xios |
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10 | { |
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11 | /** |
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12 | * \brief class constructor. |
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13 | * \param srcView List of sources views. |
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14 | * \param dstView List of remotes views. |
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15 | * \param localComm Local MPI communicator |
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16 | * \param remoteSize Size of the remote communicator |
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17 | */ |
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18 | CGridRemoteConnector::CGridRemoteConnector(vector<shared_ptr<CLocalView>>& srcView, vector<shared_ptr<CDistributedView>>& dstView, MPI_Comm localComm, int remoteSize) |
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19 | : srcView_(srcView), dstView_(dstView), localComm_(localComm), remoteSize_(remoteSize) |
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20 | {} |
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21 | |
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22 | /** |
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23 | * \brief class constructor. |
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24 | * \param srcView List of sources views. |
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25 | * \param dstView List of remotes views. |
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26 | * \param localComm Local MPI communicator |
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27 | * \param remoteSize Size of the remote communicator |
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28 | */ |
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29 | CGridRemoteConnector::CGridRemoteConnector(vector<shared_ptr<CLocalView>>& srcView, vector< shared_ptr<CLocalView> >& dstView, MPI_Comm localComm, int remoteSize) |
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30 | : srcView_(srcView), localComm_(localComm), remoteSize_(remoteSize) |
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31 | { |
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32 | for(auto& it : dstView) dstView_.push_back((shared_ptr<CDistributedView>) it) ; |
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33 | } |
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34 | |
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35 | |
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36 | /** |
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37 | * \brief Compute if each view composing the source grid and the remote grid is distributed or not. |
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38 | * Result is stored on internal attributes \b isSrcViewDistributed_ and \b isDstViewDistributed_. |
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39 | * \detail To compute this, a hash is computed for each array on indices. The hash must permutable, i.e. |
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40 | * the order of the list of global indices doesn't influence the value of the hash. So simply a sum of |
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41 | * hash of each indices is used for the whole array. After, the computed hash are compared with each other |
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42 | * ranks of \b localComm_ MPI communicator using an MPI_ALLReduce. If, for each ranks, the hash is the same |
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43 | * then the view is not distributed |
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44 | */ |
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45 | void CGridRemoteConnector::computeViewDistribution(void) |
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46 | { |
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47 | HashXIOS<size_t> hashGlobalIndex; // hash function-object |
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48 | |
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49 | int nDst = dstView_.size() ; |
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50 | vector<size_t> hashRank(remoteSize_) ; |
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51 | vector<size_t> sizeRank(remoteSize_) ; |
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52 | isDstViewDistributed_.resize(nDst) ; |
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53 | |
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54 | for(int i=0; i<nDst; i++) |
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55 | { |
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56 | map<int,CArray<size_t,1>> globalIndexView ; |
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57 | dstView_[i]->getGlobalIndexView(globalIndexView) ; |
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58 | hashRank.assign(remoteSize_,0) ; // everybody ranks to 0 except rank of the remote view I have |
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59 | // that would be assign to my local hash |
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60 | sizeRank.assign(remoteSize_,0) ; |
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61 | |
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62 | for(auto& it : globalIndexView) |
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63 | { |
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64 | int rank=it.first ; |
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65 | CArray<size_t,1>& globalIndex = it.second ; |
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66 | size_t globalIndexSize = globalIndex.numElements(); |
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67 | size_t hashValue=0 ; |
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68 | for(size_t ind=0;ind<globalIndexSize;ind++) hashValue += hashGlobalIndex(globalIndex(ind)) ; |
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69 | hashRank[rank] += hashValue ; |
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70 | sizeRank[rank] += globalIndexSize ; |
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71 | } |
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72 | // sum all the hash for every process of the local comm. The reduce is on the size of remote view (remoteSize_) |
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73 | // after that for each rank of the remote view, we get the hash |
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74 | MPI_Allreduce(MPI_IN_PLACE, hashRank.data(), remoteSize_, MPI_SIZE_T, MPI_SUM, localComm_) ; |
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75 | MPI_Allreduce(MPI_IN_PLACE, sizeRank.data(), remoteSize_, MPI_SIZE_T, MPI_SUM, localComm_) ; |
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76 | size_t value = hashRank[0] ; |
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77 | size_t size = sizeRank[0] ; |
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78 | isDstViewDistributed_[i]=false ; |
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79 | for(int j=0 ; j<remoteSize_ ; j++) |
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80 | if (size!=sizeRank[j] || value != hashRank[j]) |
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81 | { |
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82 | isDstViewDistributed_[i]=true ; |
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83 | break ; |
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84 | } |
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85 | } |
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86 | |
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87 | int nSrc = srcView_.size() ; |
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88 | int commSize,commRank ; |
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89 | MPI_Comm_size(localComm_,&commSize) ; |
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90 | MPI_Comm_rank(localComm_,&commRank) ; |
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91 | hashRank.resize(commSize,0) ; |
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92 | isSrcViewDistributed_.resize(nSrc) ; |
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93 | |
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94 | for(int i=0; i<nSrc; i++) |
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95 | { |
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96 | CArray<size_t,1> globalIndex ; |
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97 | srcView_[i]->getGlobalIndexView(globalIndex) ; |
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98 | hashRank.assign(commSize,0) ; // 0 for everybody except my rank |
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99 | size_t globalIndexSize = globalIndex.numElements() ; |
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100 | |
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101 | size_t minVal,maxVal ; |
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102 | MPI_Allreduce(&globalIndexSize, &minVal, 1, MPI_SIZE_T, MPI_MIN, localComm_) ; |
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103 | MPI_Allreduce(&globalIndexSize, &maxVal, 1, MPI_SIZE_T, MPI_MAX, localComm_) ; |
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104 | if (minVal!=maxVal) |
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105 | { |
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106 | isSrcViewDistributed_[i]=true ; |
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107 | break ; |
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108 | } |
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109 | |
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110 | // warning : jenkins hash : 0 --> 0 : need to compare number of element for each ranks |
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111 | size_t hashValue=0 ; |
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112 | for(size_t ind=0;ind<globalIndexSize;ind++) hashValue += hashGlobalIndex(globalIndex(ind)) ; |
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113 | MPI_Allreduce(&hashValue, &minVal, 1, MPI_SIZE_T, MPI_MIN, localComm_) ; |
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114 | MPI_Allreduce(&hashValue, &maxVal, 1, MPI_SIZE_T, MPI_MAX, localComm_) ; |
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115 | if (minVal!=maxVal) isSrcViewDistributed_[i]=true ; |
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116 | else isSrcViewDistributed_[i]=false ; |
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117 | } |
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118 | |
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119 | } |
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120 | |
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121 | /** |
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122 | * \brief Compute the connector, i.e. compute the \b elements_ attribute. |
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123 | * \detail Depending of the distributions of the view computed in the computeViewDistribution() call, the connector is computed in computeConnectorMethods(), and to achieve better optimisation |
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124 | * some redondant ranks can be removed from the elements_ map. |
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125 | */ |
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126 | void CGridRemoteConnector::computeConnector(bool eliminateRedundant) |
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127 | { |
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128 | if (eliminateRedundant) |
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129 | { |
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130 | computeViewDistribution() ; |
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131 | computeConnectorMethods() ; |
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132 | computeRedondantRanks() ; |
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133 | for(auto& rank : rankToRemove_) |
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134 | for(auto& element : elements_) element.erase(rank) ; |
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135 | } |
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136 | else |
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137 | { |
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138 | computeViewDistribution() ; |
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139 | computeConnectorRedundant() ; |
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140 | } |
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141 | } |
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142 | |
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143 | /** |
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144 | * \brief Compute the connector, i.e. compute the \b elements_ attribute. |
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145 | * \detail In this routine we don't eliminate redundant cells as it it performed in |
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146 | * computeConnectorMethods. It can be usefull to perform reduce operation over processes. |
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147 | In future, some optimisation could be done considering full redondance of the |
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148 | source view or the destination view. |
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149 | */ |
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150 | void CGridRemoteConnector::computeConnectorRedundant(void) |
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151 | { |
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152 | vector<shared_ptr<CLocalView>> srcView ; |
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153 | vector<shared_ptr<CDistributedView>> dstView ; |
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154 | vector<int> indElements ; |
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155 | elements_.resize(srcView_.size()) ; |
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156 | |
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157 | bool srcViewsNonDistributed=true ; // not usefull now but later for optimization |
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158 | for(int i=0;i<srcView_.size();i++) srcViewsNonDistributed = srcViewsNonDistributed && !isSrcViewDistributed_[i] ; |
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159 | |
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160 | bool dstViewsNonDistributed=true ; // not usefull now but later for optimization |
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161 | for(int i=0;i<dstView_.size();i++) dstViewsNonDistributed = dstViewsNonDistributed && !isDstViewDistributed_[i] ; |
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162 | |
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163 | for(int i=0;i<srcView_.size();i++) |
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164 | { |
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165 | srcView.push_back(srcView_[i]) ; |
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166 | dstView.push_back(dstView_[i]) ; |
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167 | indElements.push_back(i) ; |
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168 | } |
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169 | |
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170 | computeGenericMethod(srcView, dstView, indElements) ; |
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171 | |
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172 | map<int,bool> ranks ; |
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173 | for(auto& it : elements_[indElements[0]]) |
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174 | { |
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175 | if (it.second.numElements()==0) ranks[it.first] = false ; |
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176 | else ranks[it.first] = true ; |
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177 | } |
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178 | |
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179 | } |
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180 | |
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181 | |
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182 | /** |
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183 | * \brief Compute the connector, i.e. compute the \b elements_ attribute. |
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184 | * \detail In order to achive better optimisation, |
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185 | * we distingute the case when the grid is not distributed on source grid (\bcomputeSrcNonDistributed), |
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186 | * or the remote grid (\b computeDstNonDistributed), or the both (\b computeSrcDstNonDistributed). |
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187 | * Otherwise the generic method is called computeGenericMethod. Note that in the case, if one element view |
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188 | * is not distributed on the source and on the remote grid, then we can used the tensorial product |
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189 | * property to computing it independently using \b computeSrcDstNonDistributed method. |
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190 | * After that, we call the \b removeRedondantRanks method to supress blocks of data that can be sent |
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191 | * redondantly the the remote servers |
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192 | */ |
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193 | void CGridRemoteConnector::computeConnectorMethods(bool reverse) |
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194 | { |
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195 | vector<shared_ptr<CLocalView>> srcView ; |
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196 | vector<shared_ptr<CDistributedView>> dstView ; |
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197 | vector<int> indElements ; |
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198 | elements_.resize(srcView_.size()) ; |
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199 | |
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200 | bool srcViewsNonDistributed=true ; |
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201 | for(int i=0;i<srcView_.size();i++) srcViewsNonDistributed = srcViewsNonDistributed && !isSrcViewDistributed_[i] ; |
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202 | |
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203 | bool dstViewsNonDistributed=true ; |
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204 | for(int i=0;i<dstView_.size();i++) dstViewsNonDistributed = dstViewsNonDistributed && !isDstViewDistributed_[i] ; |
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205 | |
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206 | //***************************************************** |
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207 | if (srcViewsNonDistributed && dstViewsNonDistributed) |
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208 | { |
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209 | int commRank, commSize ; |
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210 | MPI_Comm_rank(localComm_,&commRank) ; |
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211 | MPI_Comm_size(localComm_,&commSize) ; |
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212 | |
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213 | map<int,bool> ranks ; |
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214 | if (reverse) |
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215 | { |
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216 | int leaderRank=getLeaderRank(remoteSize_, commSize, commRank) ; |
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217 | ranks[leaderRank] = true ; |
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218 | } |
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219 | else |
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220 | { |
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221 | list<int> remoteRanks; |
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222 | list<int> notUsed ; |
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223 | computeLeaderProcess(commRank, commSize, remoteSize_, remoteRanks, notUsed) ; |
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224 | for(int rank : remoteRanks) ranks[rank]=true ; |
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225 | } |
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226 | for(int i=0; i<srcView_.size(); i++) computeSrcDstNonDistributed(i,ranks) ; |
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227 | } |
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228 | |
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229 | //***************************************************** |
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230 | else if (srcViewsNonDistributed) |
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231 | { |
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232 | int commRank, commSize ; |
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233 | MPI_Comm_rank(localComm_,&commRank) ; |
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234 | MPI_Comm_size(localComm_,&commSize) ; |
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235 | list<int> remoteRanks; |
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236 | list<int> notUsed ; |
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237 | map<int,bool> ranks ; |
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238 | |
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239 | if (reverse) |
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240 | { |
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241 | shared_ptr<CLocalElement> voidElement = make_shared<CLocalElement>(commRank, 0, CArray<size_t,1>()) ; |
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242 | shared_ptr<CLocalView> voidView = make_shared<CLocalView>(voidElement, CElementView::FULL, CArray<int,1>()) ; |
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243 | |
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244 | for(int i=0;i<srcView_.size();i++) |
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245 | if (isDstViewDistributed_[i]) |
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246 | { |
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247 | if (commRank==0) srcView.push_back(srcView_[i]) ; |
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248 | else srcView.push_back(make_shared<CLocalView>(make_shared<CLocalElement>(commRank, srcView_[i]->getGlobalSize(), CArray<size_t,1>()), |
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249 | CElementView::FULL, CArray<int,1>())) ; // void view |
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250 | dstView.push_back(dstView_[i]) ; |
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251 | indElements.push_back(i) ; |
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252 | } |
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253 | |
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254 | computeGenericMethod(srcView, dstView, indElements) ; |
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255 | |
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256 | for(int i=0;i<srcView_.size();i++) |
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257 | if (isDstViewDistributed_[i]) |
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258 | { |
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259 | size_t sizeElement ; |
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260 | int nRank ; |
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261 | if (commRank==0) nRank = elements_[i].size() ; |
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262 | MPI_Bcast(&nRank, 1, MPI_INT, 0, localComm_) ; |
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263 | |
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264 | auto it=elements_[i].begin() ; |
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265 | for(int j=0;j<nRank;j++) |
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266 | { |
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267 | int rank ; |
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268 | size_t sizeElement ; |
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269 | if (commRank==0) { rank = it->first ; sizeElement=it->second.numElements(); } |
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270 | MPI_Bcast(&rank, 1, MPI_INT, 0, localComm_) ; |
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271 | MPI_Bcast(&sizeElement, 1, MPI_SIZE_T, 0, localComm_) ; |
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272 | if (commRank!=0) elements_[i][rank].resize(sizeElement) ; |
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273 | MPI_Bcast(elements_[i][rank].dataFirst(), sizeElement, MPI_SIZE_T, 0, localComm_) ; |
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274 | if (commRank==0) ++it ; |
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275 | } |
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276 | } |
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277 | |
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278 | for(auto& it : elements_[indElements[0]]) |
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279 | { |
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280 | if (it.second.numElements()==0) ranks[it.first] = false ; |
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281 | else ranks[it.first] = true ; |
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282 | } |
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283 | |
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284 | for(int i=0;i<srcView_.size();i++) |
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285 | if (!isDstViewDistributed_[i]) computeSrcDstNonDistributed(i, ranks) ; |
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286 | |
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287 | } |
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288 | else |
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289 | { |
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290 | computeLeaderProcess(commRank, commSize, remoteSize_, remoteRanks, notUsed) ; |
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291 | for(int rank : remoteRanks) ranks[rank]=true ; |
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292 | |
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293 | for(int i=0; i<srcView_.size(); i++) |
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294 | { |
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295 | if (isDstViewDistributed_[i]) computeSrcNonDistributed(i) ; |
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296 | else computeSrcDstNonDistributed(i, ranks) ; |
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297 | } |
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298 | } |
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299 | |
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300 | } |
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301 | //***************************************************** |
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302 | else if (dstViewsNonDistributed) |
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303 | { |
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304 | int commRank, commSize ; |
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305 | MPI_Comm_rank(localComm_,&commRank) ; |
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306 | MPI_Comm_size(localComm_,&commSize) ; |
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307 | |
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308 | map<int,bool> ranks ; |
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309 | if (reverse) |
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310 | { |
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311 | int leaderRank=getLeaderRank(remoteSize_, commSize, commRank) ; |
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312 | ranks[leaderRank] = true ; |
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313 | } |
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314 | else for(int i=0;i<remoteSize_;i++) ranks[i]=true ; |
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315 | |
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316 | for(int i=0; i<srcView_.size(); i++) |
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317 | { |
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318 | if (isSrcViewDistributed_[i]) computeDstNonDistributed(i,ranks) ; |
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319 | else computeSrcDstNonDistributed(i,ranks) ; |
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320 | } |
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321 | } |
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322 | //***************************************************** |
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323 | else |
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324 | { |
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325 | for(int i=0;i<srcView_.size();i++) |
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326 | if (isSrcViewDistributed_[i] || isDstViewDistributed_[i]) |
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327 | { |
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328 | srcView.push_back(srcView_[i]) ; |
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329 | dstView.push_back(dstView_[i]) ; |
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330 | indElements.push_back(i) ; |
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331 | } |
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332 | |
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333 | computeGenericMethod(srcView, dstView, indElements) ; |
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334 | |
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335 | map<int,bool> ranks ; |
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336 | for(auto& it : elements_[indElements[0]]) |
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337 | { |
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338 | if (it.second.numElements()==0) ranks[it.first] = false ; |
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339 | else ranks[it.first] = true ; |
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340 | } |
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341 | |
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342 | for(int i=0;i<srcView_.size();i++) |
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343 | if (!isSrcViewDistributed_[i] && !isDstViewDistributed_[i]) computeSrcDstNonDistributed(i, ranks) ; |
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344 | } |
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345 | |
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346 | } |
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347 | |
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348 | |
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349 | /** |
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350 | * \brief Compute the connector for the element \b i when the source view is not distributed. |
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351 | * After the call element_[i] is defined. |
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352 | * \param i Indice of the element composing the source grid. |
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353 | */ |
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354 | |
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355 | void CGridRemoteConnector::computeSrcNonDistributed(int i) |
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356 | { |
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357 | auto& element = elements_[i] ; |
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358 | map<int,CArray<size_t,1>> globalIndexView ; |
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359 | dstView_[i]->getGlobalIndexView(globalIndexView) ; |
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360 | |
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361 | CClientClientDHTTemplate<int>::Index2InfoTypeMap dataInfo; |
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362 | |
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363 | for(auto& it : globalIndexView) |
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364 | { |
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365 | auto& globalIndex=it.second ; |
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366 | for(size_t ind : globalIndex) dataInfo[ind]=it.first ; |
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367 | } |
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368 | |
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369 | // First we feed the distributed hash map with key (remote global index) |
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370 | // associated with the value of the remote rank |
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371 | CClientClientDHTTemplate<int> DHT(dataInfo, localComm_) ; |
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372 | // after we feed the DHT with the local global indices of the source view |
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373 | |
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374 | int commRank, commSize ; |
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375 | MPI_Comm_rank(localComm_,&commRank) ; |
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376 | MPI_Comm_size(localComm_,&commSize) ; |
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377 | CArray<size_t,1> srcIndex ; |
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378 | // like the source view is not distributed, then only the rank 0 need to feed the DHT |
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379 | if (commRank==0) srcView_[i]->getGlobalIndexView(srcIndex) ; |
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380 | |
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381 | // compute the mapping |
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382 | DHT.computeIndexInfoMapping(srcIndex) ; |
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383 | auto& returnInfo = DHT.getInfoIndexMap() ; |
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384 | |
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385 | // returnInfo contains now the map for each global indices to send to a list of remote rank |
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386 | // only for the rank=0 because it is the one to feed the DHT |
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387 | // so it need to send the list to each server leader i.e. the local process that handle specifically one or more |
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388 | // servers |
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389 | |
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390 | // rankIndGlo : rankIndGlo[rank][indGlo] : list of indice to send the the remote server of rank "rank" |
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391 | vector<vector<size_t>> rankIndGlo(remoteSize_) ; |
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392 | if (commRank==0) |
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393 | for(auto& it1 : returnInfo) |
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394 | for(auto& it2 : it1.second) rankIndGlo[it2].push_back(it1.first) ; |
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395 | |
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396 | |
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397 | vector<MPI_Request> requests ; |
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398 | |
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399 | if (commRank==0) |
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400 | { |
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401 | requests.resize(remoteSize_) ; |
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402 | for(int i=0 ; i<remoteSize_;i++) |
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403 | { |
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404 | // ok send only the global indices for a server to the "server leader" |
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405 | int rank = getLeaderRank(commSize, remoteSize_, i) ; |
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406 | MPI_Isend(rankIndGlo[i].data(), rankIndGlo[i].size(), MPI_SIZE_T, rank, i ,localComm_, &requests[i]) ; |
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407 | } |
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408 | } |
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409 | |
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410 | list<int> remoteRanks; |
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411 | list<int> notUsed ; |
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412 | // I am a server leader of which remote ranks ? |
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413 | computeLeaderProcess(commRank, commSize, remoteSize_, remoteRanks, notUsed) ; |
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414 | |
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415 | for(auto remoteRank : remoteRanks) |
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416 | { |
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417 | MPI_Status status ; |
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418 | int size ; |
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419 | MPI_Probe(0,remoteRank,localComm_, &status); |
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420 | MPI_Get_count(&status, MPI_SIZE_T, &size) ; |
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421 | elements_[i][remoteRank].resize(size) ; |
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422 | // for each remote ranks receive the global indices from proc 0 |
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423 | MPI_Recv(elements_[i][remoteRank].dataFirst(),size, MPI_SIZE_T,0,remoteRank, localComm_,&status) ; |
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424 | } |
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425 | |
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426 | if (commRank==0) |
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427 | { |
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428 | vector<MPI_Status> status(remoteSize_) ; |
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429 | // asynchronous for sender, wait for completion |
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430 | MPI_Waitall(remoteSize_, requests.data(), status.data()) ; |
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431 | } |
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432 | } |
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433 | |
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434 | /** |
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435 | * \brief Compute the connector for the element \b i when the source view is not distributed. |
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436 | * After the call element_[i] is defined. |
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437 | * \param i Indice of the element composing the source grid. |
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438 | */ |
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439 | |
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440 | void CGridRemoteConnector::computeSrcNonDistributedReverse(int i) |
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441 | { |
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442 | auto& element = elements_[i] ; |
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443 | map<int,CArray<size_t,1>> globalIndexView ; |
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444 | dstView_[i]->getGlobalIndexView(globalIndexView) ; |
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445 | |
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446 | CClientClientDHTTemplate<int>::Index2InfoTypeMap dataInfo; |
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447 | |
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448 | for(auto& it : globalIndexView) |
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449 | { |
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450 | auto& globalIndex=it.second ; |
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451 | for(size_t ind : globalIndex) dataInfo[ind]=it.first ; |
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452 | } |
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453 | |
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454 | // First we feed the distributed hash map with key (remote global index) |
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455 | // associated with the value of the remote rank |
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456 | CClientClientDHTTemplate<int> DHT(dataInfo, localComm_) ; |
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457 | // after we feed the DHT with the local global indices of the source view |
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458 | |
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459 | int commRank, commSize ; |
---|
460 | MPI_Comm_rank(localComm_,&commRank) ; |
---|
461 | MPI_Comm_size(localComm_,&commSize) ; |
---|
462 | CArray<size_t,1> srcIndex ; |
---|
463 | // like the source view is not distributed, then only the rank 0 need to feed the DHT |
---|
464 | if (commRank==0) srcView_[i]->getGlobalIndexView(srcIndex) ; |
---|
465 | |
---|
466 | // compute the mapping |
---|
467 | DHT.computeIndexInfoMapping(srcIndex) ; |
---|
468 | auto& returnInfo = DHT.getInfoIndexMap() ; |
---|
469 | |
---|
470 | // returnInfo contains now the map for each global indices to send to a list of remote rank |
---|
471 | // only for the rank=0 because it is the one to feed the DHT |
---|
472 | // so it need to send the list to each server leader i.e. the local process that handle specifically one or more |
---|
473 | // servers |
---|
474 | |
---|
475 | // rankIndGlo : rankIndGlo[rank][indGlo] : list of indice to send the the remote server of rank "rank" |
---|
476 | vector<vector<size_t>> rankIndGlo(remoteSize_) ; |
---|
477 | if (commRank==0) |
---|
478 | for(auto& it1 : returnInfo) |
---|
479 | for(auto& it2 : it1.second) rankIndGlo[it2].push_back(it1.first) ; |
---|
480 | |
---|
481 | // bcast the same for each client |
---|
482 | for(int remoteRank=0 ; remoteRank<remoteSize_ ; remoteRank++) |
---|
483 | { |
---|
484 | int remoteDataSize ; |
---|
485 | if (commRank==0) remoteDataSize = rankIndGlo[remoteRank].size() ; |
---|
486 | MPI_Bcast(&remoteDataSize, 1, MPI_INT, 0, localComm_) ; |
---|
487 | |
---|
488 | auto& element = elements_[i][remoteRank] ; |
---|
489 | element.resize(remoteDataSize) ; |
---|
490 | if (commRank==0) for(int j=0 ; j<remoteDataSize; j++) element(j)=rankIndGlo[remoteRank][j] ; |
---|
491 | MPI_Bcast(element.dataFirst(), remoteDataSize, MPI_SIZE_T, 0, localComm_) ; |
---|
492 | } |
---|
493 | } |
---|
494 | |
---|
495 | |
---|
496 | |
---|
497 | /** |
---|
498 | * \brief Compute the remote connector for the element \b i when the remote view is not distributed. |
---|
499 | * After the call, element_[i] is defined. |
---|
500 | * \param i Indice of the element composing the remote grid. |
---|
501 | * \param ranks The list of rank for which the local proc is in charge to compute the connector |
---|
502 | * (if leader server for exemple). if ranks[rank] == false the corresponding elements_ |
---|
503 | * is set to void array (no data to sent) just in order to notify corresponding remote server |
---|
504 | * that the call is collective with each other one |
---|
505 | */ |
---|
506 | void CGridRemoteConnector::computeDstNonDistributed(int i, map<int,bool>& ranks) |
---|
507 | { |
---|
508 | auto& element = elements_[i] ; |
---|
509 | map<int,CArray<size_t,1>> globalIndexView ; |
---|
510 | dstView_[i]->getGlobalIndexView(globalIndexView) ; |
---|
511 | |
---|
512 | |
---|
513 | CClientClientDHTTemplate<int>::Index2InfoTypeMap dataInfo; |
---|
514 | |
---|
515 | // First we feed the distributed hash map with key (remote global index) |
---|
516 | // associated with the value of the remote rank |
---|
517 | for(auto& it : globalIndexView) |
---|
518 | if (it.first==0) // since the remote view is not distributed, insert only the remote rank 0 |
---|
519 | { |
---|
520 | auto& globalIndex=it.second ; |
---|
521 | for(size_t ind : globalIndex) dataInfo[ind]=0 ; // associated the the rank 0 |
---|
522 | } |
---|
523 | |
---|
524 | CClientClientDHTTemplate<int> DHT(dataInfo, localComm_) ; |
---|
525 | // after we feed the DHT with the local global indices of the source view |
---|
526 | |
---|
527 | CArray<size_t,1> srcIndex ; |
---|
528 | srcView_[i]->getGlobalIndexView(srcIndex) ; |
---|
529 | DHT.computeIndexInfoMapping(srcIndex) ; |
---|
530 | auto& returnInfo = DHT.getInfoIndexMap() ; |
---|
531 | |
---|
532 | // returnInfo contains now the map for each global indices to send to a list of remote rank |
---|
533 | // now construct the element_ list of global indices for each rank in my list except if the erray must be empty |
---|
534 | for (auto& rank : ranks) |
---|
535 | { |
---|
536 | if (rank.second) // non empty array => for rank that have not any data to be received |
---|
537 | { |
---|
538 | int size=0 ; |
---|
539 | for(auto& it : returnInfo) if (!it.second.empty()) size++ ; |
---|
540 | auto& array = element[rank.first] ; |
---|
541 | array.resize(size) ; |
---|
542 | size=0 ; |
---|
543 | for(auto& it : returnInfo) |
---|
544 | if (!it.second.empty()) |
---|
545 | { |
---|
546 | array(size)=it.first ; |
---|
547 | size++ ; |
---|
548 | } |
---|
549 | } |
---|
550 | else element[rank.first] = CArray<size_t,1>(0) ; // empty array => for rank that have not any data to be received |
---|
551 | } |
---|
552 | } |
---|
553 | |
---|
554 | /** |
---|
555 | * \brief Compute the remote connector for the element \b i when the source and the remote view are not distributed. |
---|
556 | * After the call, element_[i] is defined. |
---|
557 | * \param i Indice of the element composing the remote grid. |
---|
558 | * \param ranks The list of rank for which the local proc is in charge to compute the connector |
---|
559 | * (if leader server for exemple). if ranks[rank] == false the corresponding elements_ |
---|
560 | * is set to void array (no data to sent) just in order to notify corresponding remote server |
---|
561 | * that the call is collective with each other one |
---|
562 | */ |
---|
563 | |
---|
564 | void CGridRemoteConnector::computeSrcDstNonDistributed(int i, map<int,bool>& ranks) |
---|
565 | { |
---|
566 | auto& element = elements_[i] ; |
---|
567 | map<int,CArray<size_t,1>> globalIndexView ; |
---|
568 | dstView_[i]->getGlobalIndexView(globalIndexView) ; |
---|
569 | |
---|
570 | |
---|
571 | CClientClientDHTTemplate<int>::Index2InfoTypeMap dataInfo; |
---|
572 | // First we feed the distributed hash map with key (remote global index) |
---|
573 | // associated with the value of the remote rank |
---|
574 | |
---|
575 | for(auto& it : globalIndexView) |
---|
576 | if (it.first==0) // insert only the remote rank 0 since the remote view is not distributed |
---|
577 | { |
---|
578 | auto& globalIndex=it.second ; |
---|
579 | for(size_t ind : globalIndex) dataInfo[ind]=0 ; // associated the the rank 0 |
---|
580 | } |
---|
581 | |
---|
582 | CClientClientDHTTemplate<int> DHT(dataInfo, localComm_) ; |
---|
583 | // after we feed the DHT with the local global indices of the source view |
---|
584 | |
---|
585 | int commRank, commSize ; |
---|
586 | MPI_Comm_rank(localComm_,&commRank) ; |
---|
587 | MPI_Comm_size(localComm_,&commSize) ; |
---|
588 | CArray<size_t,1> srcIndex ; |
---|
589 | |
---|
590 | // like the source view is not distributed, then only the rank 0 need to feed the DHT |
---|
591 | if (commRank==0) srcView_[i]->getGlobalIndexView(srcIndex) ; |
---|
592 | DHT.computeIndexInfoMapping(srcIndex) ; |
---|
593 | auto& returnInfo = DHT.getInfoIndexMap() ; |
---|
594 | |
---|
595 | vector<size_t> indGlo ; |
---|
596 | if (commRank==0) |
---|
597 | for(auto& it1 : returnInfo) |
---|
598 | for(auto& it2 : it1.second) indGlo.push_back(it1.first) ; |
---|
599 | |
---|
600 | // now local rank 0 know which indices to send to remote rank 0, but all the server |
---|
601 | // must receive the same information. So only the leader rank will sent this. |
---|
602 | // So local rank 0 must broadcast the information to all leader. |
---|
603 | // for this we create a new communicator composed of local process that must send data |
---|
604 | // to a remote rank, data are broadcasted, and element_[i] is construction for each remote |
---|
605 | // rank in charge |
---|
606 | int color=0 ; |
---|
607 | if (ranks.empty()) color=0 ; |
---|
608 | else color=1 ; |
---|
609 | if (commRank==0) color=1 ; |
---|
610 | MPI_Comm newComm ; |
---|
611 | xios::MPI_Comm_split(localComm_, color, commRank, &newComm) ; |
---|
612 | if (color==1) |
---|
613 | { |
---|
614 | // ok, I am part of the process that must send something to one or more remote server |
---|
615 | // so I get the list of global indices from rank 0 |
---|
616 | int dataSize ; |
---|
617 | if (commRank==0) dataSize=indGlo.size() ; |
---|
618 | MPI_Bcast(&dataSize,1,MPI_INT, 0, newComm) ; |
---|
619 | indGlo.resize(dataSize) ; |
---|
620 | MPI_Bcast(indGlo.data(),dataSize,MPI_SIZE_T,0,newComm) ; |
---|
621 | } |
---|
622 | xios::MPI_Comm_free(&newComm) ; |
---|
623 | |
---|
624 | // construct element_[i] from indGlo |
---|
625 | for(auto& rank : ranks) |
---|
626 | { |
---|
627 | if (rank.second) |
---|
628 | { |
---|
629 | int dataSize=indGlo.size(); |
---|
630 | auto& element = elements_[i][rank.first] ; |
---|
631 | element.resize(dataSize) ; |
---|
632 | for(int i=0;i<dataSize; i++) element(i)=indGlo[i] ; |
---|
633 | } |
---|
634 | else element[rank.first] = CArray<size_t,1>(0) ; |
---|
635 | } |
---|
636 | |
---|
637 | } |
---|
638 | |
---|
639 | |
---|
640 | /** |
---|
641 | * \brief Generic method the compute the grid remote connector. Only distributed elements are specifed in the source view and remote view. |
---|
642 | * Connector for non distributed elements are computed separatly to improve performance and memory consumption. After the call, |
---|
643 | * \b elements_ is defined. |
---|
644 | * \param srcView List of the source views composing the grid, without non distributed views |
---|
645 | * \param dstView List of the remote views composing the grid, without non distributed views |
---|
646 | * \param indElements Index of the view making the correspondance between all views and views distributed (that are in input) |
---|
647 | */ |
---|
648 | void CGridRemoteConnector::computeGenericMethod(vector<shared_ptr<CLocalView>>& srcView, vector<shared_ptr<CDistributedView>>& dstView, vector<int>& indElements) |
---|
649 | { |
---|
650 | // generic method, every element can be distributed |
---|
651 | int nDst = dstView.size() ; |
---|
652 | vector<size_t> dstSliceSize(nDst) ; |
---|
653 | dstSliceSize[0] = 1 ; |
---|
654 | for(int i=1; i<nDst; i++) dstSliceSize[i] = dstView[i-1]->getGlobalSize()*dstSliceSize[i-1] ; |
---|
655 | |
---|
656 | CClientClientDHTTemplate<int>::Index2VectorInfoTypeMap dataInfo ; |
---|
657 | CClientClientDHTTemplate<size_t>::Index2VectorInfoTypeMap info ; // info map |
---|
658 | |
---|
659 | // first, we need to feed the DHT with the global index of the remote server |
---|
660 | // for that : |
---|
661 | // First the first element insert the in a DHT with key as the rank and value the list of global index associated |
---|
662 | // Then get the previously stored index associate with the remote rank I am in charge and reinsert the global index |
---|
663 | // corresponding to the position of the element in the remote view suing tensorial product |
---|
664 | // finaly we get only the list of remote global index I am in charge for the whole remote grid |
---|
665 | |
---|
666 | for(int pos=0; pos<nDst; pos++) |
---|
667 | { |
---|
668 | size_t sliceSize=dstSliceSize[pos] ; |
---|
669 | map<int,CArray<size_t,1>> globalIndexView ; |
---|
670 | dstView[pos]->getGlobalIndexView(globalIndexView) ; |
---|
671 | |
---|
672 | CClientClientDHTTemplate<size_t>::Index2VectorInfoTypeMap lastInfo(info) ; |
---|
673 | |
---|
674 | if (pos>0) |
---|
675 | { |
---|
676 | CArray<size_t,1> ranks(globalIndexView.size()) ; |
---|
677 | auto it=globalIndexView.begin() ; |
---|
678 | for(int i=0 ; i<ranks.numElements();i++,it++) ranks(i)=it->first ; |
---|
679 | CClientClientDHTTemplate<size_t> dataRanks(info, localComm_) ; |
---|
680 | dataRanks.computeIndexInfoMapping(ranks) ; |
---|
681 | lastInfo = dataRanks.getInfoIndexMap() ; |
---|
682 | } |
---|
683 | |
---|
684 | info.clear() ; |
---|
685 | for(auto& it : globalIndexView) |
---|
686 | { |
---|
687 | int rank = it.first ; |
---|
688 | auto& globalIndex = it.second ; |
---|
689 | auto& inf = info[rank] ; |
---|
690 | if (pos==0) for(int i=0;i<globalIndex.numElements();i++) inf.push_back(globalIndex(i)) ; |
---|
691 | else |
---|
692 | { |
---|
693 | auto& lastGlobalIndex = lastInfo[rank] ; |
---|
694 | for(size_t lastGlobalInd : lastGlobalIndex) |
---|
695 | { |
---|
696 | for(int i=0;i<globalIndex.numElements();i++) inf.push_back(globalIndex(i)*sliceSize+lastGlobalInd) ; |
---|
697 | } |
---|
698 | } |
---|
699 | } |
---|
700 | |
---|
701 | if (pos==nDst-1) |
---|
702 | { |
---|
703 | for(auto& it : info) |
---|
704 | { |
---|
705 | int rank=it.first ; |
---|
706 | auto& globalIndex = it.second ; |
---|
707 | for(auto globalInd : globalIndex) dataInfo[globalInd].push_back(rank) ; |
---|
708 | } |
---|
709 | } |
---|
710 | } |
---|
711 | |
---|
712 | // we feed the DHT with the remote global index |
---|
713 | CClientClientDHTTemplate<int> dataRanks(dataInfo, localComm_) ; |
---|
714 | |
---|
715 | // generate list of global index for src view |
---|
716 | int nSrc = srcView.size() ; |
---|
717 | vector<size_t> srcSliceSize(nSrc) ; |
---|
718 | |
---|
719 | srcSliceSize[0] = 1 ; |
---|
720 | for(int i=1; i<nSrc; i++) srcSliceSize[i] = srcView[i-1]->getGlobalSize()*srcSliceSize[i-1] ; |
---|
721 | |
---|
722 | vector<size_t> srcGlobalIndex ; |
---|
723 | size_t sliceIndex=0 ; |
---|
724 | srcView[nSrc-1]->getGlobalIndex(srcGlobalIndex, sliceIndex, srcSliceSize.data(), srcView.data(), nSrc-1) ; |
---|
725 | // now we have the global index of the source grid in srcGlobalIndex |
---|
726 | // we feed the DHT with the src global index (if we have) |
---|
727 | if (srcGlobalIndex.size()>0) |
---|
728 | { |
---|
729 | CArray<size_t,1> srcGlobalIndexArray(srcGlobalIndex.data(), shape(srcGlobalIndex.size()),neverDeleteData) ; |
---|
730 | dataRanks.computeIndexInfoMapping(srcGlobalIndexArray) ; |
---|
731 | } |
---|
732 | else |
---|
733 | { |
---|
734 | CArray<size_t,1> srcGlobalIndexArray ; |
---|
735 | dataRanks.computeIndexInfoMapping(srcGlobalIndexArray) ; |
---|
736 | } |
---|
737 | const auto& returnInfo = dataRanks.getInfoIndexMap() ; |
---|
738 | // returnInfo contains now the map for each global indices to send to a list of remote rank |
---|
739 | // but we want to use the tensorial product property to get the same information using only global |
---|
740 | // index of element view. So the idea is to reverse the information : for a global index of the grid |
---|
741 | // to send to the remote server, what is the global index of each element composing the grid ? |
---|
742 | |
---|
743 | vector<map<int, set<size_t>>> elements(nSrc) ; // internal representation of elements composing the grid |
---|
744 | |
---|
745 | for(auto& indRanks : returnInfo) |
---|
746 | { |
---|
747 | size_t gridIndexGlo=indRanks.first ; |
---|
748 | auto& ranks = indRanks.second ; |
---|
749 | for(int i=nSrc-1; i>=0; i--) |
---|
750 | { |
---|
751 | auto& element = elements[i] ; |
---|
752 | size_t localIndGlo = gridIndexGlo / srcSliceSize[i] ; |
---|
753 | gridIndexGlo = gridIndexGlo % srcSliceSize[i] ; |
---|
754 | for(int rank : ranks) element[rank].insert(localIndGlo) ; |
---|
755 | } |
---|
756 | } |
---|
757 | |
---|
758 | // elements_.resize(nSrc) ; |
---|
759 | for(int i=0 ; i<nSrc; i++) |
---|
760 | { |
---|
761 | auto& element=elements[i] ; |
---|
762 | for(auto& rankInd : element) |
---|
763 | { |
---|
764 | int rank=rankInd.first ; |
---|
765 | set<size_t>& indGlo = rankInd.second ; |
---|
766 | CArray<size_t,1>& indGloArray = elements_[indElements[i]][rank] ; |
---|
767 | indGloArray.resize(indGlo.size()) ; |
---|
768 | int j=0 ; |
---|
769 | for (auto index : indGlo) { indGloArray(j) = index ; j++; } |
---|
770 | } |
---|
771 | } |
---|
772 | |
---|
773 | // So what about when there is some server that have no data to receive |
---|
774 | // they must be inform they receive an event with no data. |
---|
775 | // So find remote servers with no data, and one client will take in charge |
---|
776 | // that it receive global index with no data (0-size) |
---|
777 | vector<int> ranks(remoteSize_,0) ; |
---|
778 | for(auto& it : elements_[indElements[0]]) ranks[it.first] = 1 ; |
---|
779 | MPI_Allreduce(MPI_IN_PLACE, ranks.data(), remoteSize_, MPI_INT, MPI_SUM, localComm_) ; |
---|
780 | int commRank, commSize ; |
---|
781 | MPI_Comm_rank(localComm_,&commRank) ; |
---|
782 | MPI_Comm_size(localComm_,&commSize) ; |
---|
783 | int pos=0 ; |
---|
784 | for(int i=0; i<remoteSize_ ; i++) |
---|
785 | if (ranks[i]==0) |
---|
786 | { |
---|
787 | if (pos%commSize==commRank) |
---|
788 | for(int j=0 ; j<nSrc; j++) elements_[indElements[j]][i] = CArray<size_t,1>(0) ; |
---|
789 | pos++ ; |
---|
790 | } |
---|
791 | } |
---|
792 | |
---|
793 | /** |
---|
794 | * \brief Once the connector is computed (compute \b elements_), redondant data can be avoid to be sent to the server. |
---|
795 | * This call compute the redondant rank and store them in \b rankToRemove_ attribute. |
---|
796 | * The goal of this method is to make a hash of each block of indice that determine wich data to send to a |
---|
797 | * of a specific server rank using a hash method. So data to send to a rank is associated to a hash. |
---|
798 | * After we compare hash between local rank and remove redondant data corresponding to the same hash. |
---|
799 | */ |
---|
800 | void CGridRemoteConnector::computeRedondantRanks(bool reverse) |
---|
801 | { |
---|
802 | int commRank ; |
---|
803 | MPI_Comm_rank(localComm_,&commRank) ; |
---|
804 | |
---|
805 | set<int> ranks; |
---|
806 | for(auto& element : elements_) |
---|
807 | for(auto& it : element) ranks.insert(it.first) ; |
---|
808 | |
---|
809 | for(auto& element : elements_) |
---|
810 | for(auto& it : element) |
---|
811 | if (ranks.count(it.first)==0) ERROR("void CGridRemoteConnector::removeRedondantRanks(void)",<<"number of ranks in elements is not coherent between each element") ; |
---|
812 | |
---|
813 | HashXIOS<size_t> hashGlobalIndex; |
---|
814 | |
---|
815 | map<int,size_t> hashRanks ; |
---|
816 | for(auto& element : elements_) |
---|
817 | for(auto& it : element) |
---|
818 | { |
---|
819 | auto& globalIndex=it.second ; |
---|
820 | int rank=it.first ; |
---|
821 | size_t hash ; |
---|
822 | hash=0 ; |
---|
823 | for(int i=0; i<globalIndex.numElements(); i++) hash+=hashGlobalIndex(globalIndex(i)) ; |
---|
824 | if (globalIndex.numElements()>0) |
---|
825 | { |
---|
826 | if (hashRanks.count(rank)==0) hashRanks[rank]=hash ; |
---|
827 | else hashRanks[rank]=hashGlobalIndex.hashCombine(hashRanks[rank],hash) ; |
---|
828 | } |
---|
829 | } |
---|
830 | |
---|
831 | if (reverse) |
---|
832 | { |
---|
833 | set<size_t> hashs ; |
---|
834 | //easy because local |
---|
835 | for(auto& hashRank : hashRanks) |
---|
836 | { |
---|
837 | if (hashs.count(hashRank.second)==0) hashs.insert(hashRank.second) ; |
---|
838 | else rankToRemove_.insert(hashRank.first) ; |
---|
839 | } |
---|
840 | |
---|
841 | } |
---|
842 | else |
---|
843 | { |
---|
844 | // a hash is now computed for data block I will sent to the server. |
---|
845 | |
---|
846 | CClientClientDHTTemplate<int>::Index2InfoTypeMap info ; |
---|
847 | |
---|
848 | map<size_t,int> hashRank ; |
---|
849 | HashXIOS<int> hashGlobalIndexRank; |
---|
850 | for(auto& it : hashRanks) |
---|
851 | { |
---|
852 | it.second = hashGlobalIndexRank.hashCombine(it.first,it.second) ; |
---|
853 | info[it.second]=commRank ; |
---|
854 | hashRank[it.second]=it.first ; |
---|
855 | } |
---|
856 | |
---|
857 | // we feed a DHT map with key : hash, value : myrank |
---|
858 | CClientClientDHTTemplate<int> dataHash(info, localComm_) ; |
---|
859 | CArray<size_t,1> hashList(hashRank.size()) ; |
---|
860 | |
---|
861 | int i=0 ; |
---|
862 | for(auto& it : hashRank) { hashList(i)=it.first ; i++; } |
---|
863 | |
---|
864 | // now who are the ranks that have the same hash : feed the DHT with my list of hash |
---|
865 | dataHash.computeIndexInfoMapping(hashList) ; |
---|
866 | auto& hashRankList = dataHash.getInfoIndexMap() ; |
---|
867 | |
---|
868 | |
---|
869 | for(auto& it : hashRankList) |
---|
870 | { |
---|
871 | size_t hash = it.first ; |
---|
872 | auto& ranks = it.second ; |
---|
873 | |
---|
874 | bool first=true ; |
---|
875 | // only the process with the lowest rank get in charge of sendinf data to remote server |
---|
876 | for(int rank : ranks) if (commRank>rank) first=false ; |
---|
877 | if (!first) rankToRemove_.insert(hashRank[hash]) ; |
---|
878 | } |
---|
879 | } |
---|
880 | } |
---|
881 | |
---|
882 | } |
---|