[630] | 1 | /*! |
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| 2 | \file axis_algorithm_interpolate.cpp |
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| 3 | \author Ha NGUYEN |
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| 4 | \since 23 June 2015 |
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| 5 | \date 02 Jul 2015 |
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| 6 | |
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| 7 | \brief Algorithm for interpolation on an axis. |
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| 8 | */ |
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| 9 | #include "axis_algorithm_interpolate.hpp" |
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| 10 | #include <algorithm> |
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| 11 | #include "context.hpp" |
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| 12 | #include "context_client.hpp" |
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| 13 | #include "utils.hpp" |
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| 14 | |
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| 15 | namespace xios { |
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| 16 | |
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| 17 | CAxisAlgorithmInterpolate::CAxisAlgorithmInterpolate(CAxis* axisDestination, CAxis* axisSource, CInterpolateAxis* interpAxis) |
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| 18 | : CAxisAlgorithmTransformation(axisDestination, axisSource) |
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| 19 | { |
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| 20 | interpAxis->checkValid(axisSource); |
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| 21 | order_ = interpAxis->order.getValue(); |
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[666] | 22 | if (order_ >= axisSource->n_glo.getValue()) |
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[630] | 23 | { |
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| 24 | ERROR("CAxisAlgorithmInterpolate::CAxisAlgorithmInterpolate(CAxis* axisDestination, CAxis* axisSource, CInterpolateAxis* interpAxis)", |
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[666] | 25 | << "Order of interpolation is greater than global size of axis source" |
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| 26 | << "Size of axis source " <<axisSource->getId() << " is " << axisSource->n_glo.getValue() << std::endl |
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[630] | 27 | << "Order of interpolation is " << order_ ); |
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| 28 | } |
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| 29 | |
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| 30 | computeIndexSourceMapping(); |
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| 31 | } |
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| 32 | |
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| 33 | /*! |
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| 34 | Compute the index mapping between axis on grid source and one on grid destination |
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| 35 | */ |
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| 36 | void CAxisAlgorithmInterpolate::computeIndexSourceMapping() |
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| 37 | { |
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| 38 | CArray<double,1>& axisValue = axisSrc_->value; |
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| 39 | CArray<bool,1>& axisMask = axisSrc_->mask; |
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| 40 | |
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| 41 | CContext* context = CContext::getCurrent(); |
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| 42 | CContextClient* client=context->client; |
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| 43 | int nbClient = client->clientSize; |
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| 44 | |
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[666] | 45 | int srcSize = axisSrc_->n_glo.getValue(); |
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[630] | 46 | int numValue = axisValue.numElements(); |
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| 47 | |
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| 48 | std::vector<double> recvBuff(srcSize); |
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| 49 | std::vector<int> indexVec(srcSize); |
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| 50 | |
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| 51 | retrieveAllAxisValue(recvBuff, indexVec); |
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[668] | 52 | XIOSAlgorithms::sortWithIndex<double, CVectorStorage>(recvBuff, indexVec); |
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[630] | 53 | computeInterpolantPoint(recvBuff, indexVec); |
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| 54 | } |
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| 55 | |
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| 56 | /*! |
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| 57 | Compute the interpolant points |
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| 58 | Assume that we have all value of axis source, with these values, need to calculate weight (coeff) of Lagrange polynomial |
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| 59 | \param [in] axisValue all value of axis source |
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| 60 | \param [in] indexVec permutation index of axisValue |
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| 61 | */ |
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| 62 | void CAxisAlgorithmInterpolate::computeInterpolantPoint(const std::vector<double>& axisValue, const std::vector<int>& indexVec) |
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| 63 | { |
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| 64 | std::vector<double>::const_iterator itb = axisValue.begin(), ite = axisValue.end(); |
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| 65 | std::vector<double>::const_iterator itLowerBound, itUpperBound, it; |
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| 66 | std::vector<int>::const_iterator itbVec = indexVec.begin(), itVec; |
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| 67 | const double sfmax = NumTraits<double>::sfmax(); |
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| 68 | |
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[666] | 69 | int ibegin = axisDest_->begin.getValue(); |
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[630] | 70 | CArray<double,1>& axisDestValue = axisDest_->value; |
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| 71 | int numValue = axisDestValue.numElements(); |
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| 72 | std::map<int, std::vector<std::pair<int,double> > > interpolatingIndexValues; |
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| 73 | |
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| 74 | for (int idx = 0; idx < numValue; ++idx) |
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| 75 | { |
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| 76 | double destValue = axisDestValue(idx); |
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| 77 | itLowerBound = std::lower_bound(itb, ite, destValue); |
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| 78 | itUpperBound = std::upper_bound(itb, ite, destValue); |
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| 79 | if ((ite != itUpperBound) && (sfmax == *itUpperBound)) itUpperBound = ite; |
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| 80 | |
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| 81 | |
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| 82 | // If the value is not in the range, that means we'll do extra-polation |
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| 83 | if (ite == itLowerBound) // extra-polation |
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| 84 | { |
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| 85 | itLowerBound = itb; |
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| 86 | itUpperBound = itb + order_+1; |
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| 87 | } |
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| 88 | else if (ite == itUpperBound) // extra-polation |
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| 89 | { |
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| 90 | itLowerBound = itUpperBound - order_-1; |
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| 91 | } |
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| 92 | else |
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| 93 | { |
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| 94 | if (itb != itLowerBound) --itLowerBound; |
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| 95 | if (ite != itUpperBound) ++itUpperBound; |
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| 96 | int order = (order_ + 1) - 2; |
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| 97 | bool down = true; |
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| 98 | for (int k = 0; k < order; ++k) |
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| 99 | { |
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| 100 | if ((itb != itLowerBound) && down) |
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| 101 | { |
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| 102 | --itLowerBound; |
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| 103 | down = false; |
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| 104 | continue; |
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| 105 | } |
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| 106 | if ((ite != itUpperBound) && (sfmax != *itUpperBound)) |
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| 107 | { |
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| 108 | ++itUpperBound; |
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| 109 | down = true; |
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| 110 | } |
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| 111 | } |
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| 112 | } |
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| 113 | |
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| 114 | for (it = itLowerBound; it != itUpperBound; ++it) |
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| 115 | { |
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| 116 | int index = std::distance(itb, it); |
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| 117 | interpolatingIndexValues[idx+ibegin].push_back(make_pair(indexVec[index],*it)); |
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| 118 | } |
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| 119 | } |
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| 120 | computeWeightedValueAndMapping(interpolatingIndexValues); |
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| 121 | } |
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| 122 | |
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| 123 | /*! |
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| 124 | Compute weight (coeff) of Lagrange's polynomial |
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| 125 | \param [in] interpolatingIndexValues the necessary axis value to calculate the coeffs |
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| 126 | */ |
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| 127 | void CAxisAlgorithmInterpolate::computeWeightedValueAndMapping(const std::map<int, std::vector<std::pair<int,double> > >& interpolatingIndexValues) |
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| 128 | { |
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| 129 | std::map<int, std::vector<int> >& transMap = this->transformationMapping_; |
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| 130 | std::map<int, std::vector<double> >& transWeight = this->transformationWeight_; |
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| 131 | std::map<int, std::vector<std::pair<int,double> > >::const_iterator itb = interpolatingIndexValues.begin(), it, |
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| 132 | ite = interpolatingIndexValues.end(); |
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[666] | 133 | int ibegin = axisDest_->begin.getValue(); |
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[630] | 134 | for (it = itb; it != ite; ++it) |
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| 135 | { |
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| 136 | int globalIndexDest = it->first; |
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| 137 | double localValue = axisDest_->value(globalIndexDest - ibegin); |
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| 138 | const std::vector<std::pair<int,double> >& interpVal = it->second; |
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| 139 | int interpSize = interpVal.size(); |
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| 140 | for (int idx = 0; idx < interpSize; ++idx) |
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| 141 | { |
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| 142 | int index = interpVal[idx].first; |
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| 143 | double weight = 1.0; |
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| 144 | |
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| 145 | for (int k = 0; k < interpSize; ++k) |
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| 146 | { |
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| 147 | if (k == idx) continue; |
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| 148 | weight *= (localValue - interpVal[k].second); |
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| 149 | weight /= (interpVal[idx].second - interpVal[k].second); |
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| 150 | } |
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| 151 | transMap[globalIndexDest].push_back(index); |
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| 152 | transWeight[globalIndexDest].push_back(weight); |
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| 153 | } |
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| 154 | } |
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| 155 | } |
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| 156 | |
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| 157 | /*! |
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| 158 | Each client retrieves all values of an axis |
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| 159 | \param [in/out] recvBuff buffer for receiving values (already allocated) |
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| 160 | \param [in/out] indexVec mapping between values and global index of axis |
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| 161 | */ |
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| 162 | void CAxisAlgorithmInterpolate::retrieveAllAxisValue(std::vector<double>& recvBuff, std::vector<int>& indexVec) |
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| 163 | { |
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| 164 | CArray<double,1>& axisValue = axisSrc_->value; |
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| 165 | CArray<bool,1>& axisMask = axisSrc_->mask; |
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| 166 | |
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| 167 | CContext* context = CContext::getCurrent(); |
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| 168 | CContextClient* client=context->client; |
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| 169 | int nbClient = client->clientSize; |
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| 170 | |
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[666] | 171 | int srcSize = axisSrc_->n_glo.getValue(); |
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[630] | 172 | int numValue = axisValue.numElements(); |
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| 173 | |
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| 174 | if (srcSize == numValue) // Only one client or axis not distributed |
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| 175 | { |
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| 176 | for (int idx = 0; idx < srcSize; ++idx) |
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| 177 | { |
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| 178 | if (axisMask(idx)) |
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| 179 | { |
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| 180 | recvBuff[idx] = axisValue(idx); |
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| 181 | indexVec[idx] = idx; |
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| 182 | } |
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| 183 | else recvBuff[idx] = NumTraits<double>::sfmax(); |
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| 184 | } |
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| 185 | |
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| 186 | } |
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| 187 | else // Axis distributed |
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| 188 | { |
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| 189 | double* sendValueBuff = new double [numValue]; |
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| 190 | int* sendIndexBuff = new int [numValue]; |
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| 191 | int* recvIndexBuff = new int [srcSize]; |
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| 192 | |
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[666] | 193 | int ibegin = axisSrc_->begin.getValue(); |
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[630] | 194 | for (int idx = 0; idx < numValue; ++idx) |
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| 195 | { |
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| 196 | if (axisMask(idx)) |
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| 197 | { |
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| 198 | sendValueBuff[idx] = axisValue(idx); |
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| 199 | sendIndexBuff[idx] = idx + ibegin; |
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| 200 | } |
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| 201 | else |
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| 202 | { |
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| 203 | sendValueBuff[idx] = NumTraits<double>::sfmax(); |
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| 204 | sendIndexBuff[idx] = -1; |
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| 205 | } |
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| 206 | } |
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| 207 | |
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| 208 | int* recvCount=new int[nbClient]; |
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| 209 | MPI_Allgather(&numValue,1,MPI_INT,recvCount,1,MPI_INT,client->intraComm); |
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| 210 | |
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| 211 | int* displ=new int[nbClient]; |
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| 212 | displ[0]=0 ; |
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| 213 | for(int n=1;n<nbClient;n++) displ[n]=displ[n-1]+recvCount[n-1]; |
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| 214 | |
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| 215 | // Each client have enough global info of axis |
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| 216 | MPI_Allgatherv(sendIndexBuff,numValue,MPI_INT,recvIndexBuff,recvCount,displ,MPI_INT,client->intraComm); |
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| 217 | MPI_Allgatherv(sendValueBuff,numValue,MPI_DOUBLE,&(recvBuff[0]),recvCount,displ,MPI_DOUBLE,client->intraComm); |
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| 218 | |
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| 219 | for (int idx = 0; idx < srcSize; ++idx) |
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| 220 | { |
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| 221 | indexVec[idx] = recvIndexBuff[idx]; |
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| 222 | } |
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| 223 | |
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| 224 | delete [] displ; |
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| 225 | delete [] recvCount; |
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| 226 | delete [] recvIndexBuff; |
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| 227 | delete [] sendIndexBuff; |
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| 228 | delete [] sendValueBuff; |
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| 229 | } |
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| 230 | } |
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| 231 | |
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| 232 | } |
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