[1022] | 1 | // -*- C++ -*- |
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| 2 | /*************************************************************************** |
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| 3 | * blitz/array/reduce.h Reductions of an array (or array expression) in a |
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| 4 | * single rank: sum, mean, min, minIndex, max, maxIndex, |
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| 5 | * product, count, any, all |
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| 6 | * |
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| 7 | * $Id$ |
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| 8 | * |
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| 9 | * Copyright (C) 1997-2011 Todd Veldhuizen <tveldhui@acm.org> |
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| 10 | * |
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| 11 | * This file is a part of Blitz. |
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| 12 | * |
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| 13 | * Blitz is free software: you can redistribute it and/or modify |
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| 14 | * it under the terms of the GNU Lesser General Public License |
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| 15 | * as published by the Free Software Foundation, either version 3 |
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| 16 | * of the License, or (at your option) any later version. |
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| 17 | * |
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| 18 | * Blitz is distributed in the hope that it will be useful, |
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| 19 | * but WITHOUT ANY WARRANTY; without even the implied warranty of |
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| 20 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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| 21 | * GNU Lesser General Public License for more details. |
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| 22 | * |
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| 23 | * You should have received a copy of the GNU Lesser General Public |
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| 24 | * License along with Blitz. If not, see <http://www.gnu.org/licenses/>. |
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| 25 | * |
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| 26 | * Suggestions: blitz-devel@lists.sourceforge.net |
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| 27 | * Bugs: blitz-support@lists.sourceforge.net |
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| 28 | * |
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| 29 | * For more information, please see the Blitz++ Home Page: |
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| 30 | * https://sourceforge.net/projects/blitz/ |
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| 31 | * |
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| 32 | ****************************************************************************/ |
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| 33 | #ifndef BZ_ARRAYREDUCE_H |
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| 34 | #define BZ_ARRAYREDUCE_H |
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| 35 | |
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| 36 | #include <blitz/reduce.h> |
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| 37 | #include <blitz/meta/vecassign.h> |
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| 38 | |
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| 39 | BZ_NAMESPACE(blitz) |
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| 40 | |
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| 41 | template<bool needIndex,bool needInit> struct _bz_ReduceReset; |
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| 42 | |
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| 43 | template<> |
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| 44 | struct _bz_ReduceReset<true,true> { |
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| 45 | template<typename T_reduction,typename T_index,typename T_expr> |
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| 46 | void operator()(T_reduction& reduce,const T_index& index,const T_expr& expr) { |
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| 47 | reduce.reset(index,expr.first_value()); |
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| 48 | } |
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| 49 | }; |
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| 50 | |
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| 51 | template<> |
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| 52 | struct _bz_ReduceReset<false,true> { |
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| 53 | template<typename T_reduction,typename T_index,typename T_expr> |
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| 54 | void operator()(T_reduction& reduce,const T_index&,const T_expr& expr) { |
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| 55 | reduce.reset(expr.first_value()); |
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| 56 | } |
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| 57 | }; |
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| 58 | |
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| 59 | template<> |
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| 60 | struct _bz_ReduceReset<true,false> { |
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| 61 | template<typename T_reduction,typename T_index,typename T_expr> |
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| 62 | void operator()(T_reduction& reduce,const T_index& index,const T_expr&) { |
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| 63 | reduce.reset(index); |
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| 64 | } |
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| 65 | }; |
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| 66 | |
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| 67 | template<> |
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| 68 | struct _bz_ReduceReset<false,false> { |
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| 69 | template<typename T_reduction,typename T_index,typename T_expr> |
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| 70 | void operator()(T_reduction& reduce,const T_index&,const T_expr&) { |
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| 71 | reduce.reset(); |
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| 72 | } |
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| 73 | }; |
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| 74 | |
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| 75 | |
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| 76 | /** Expression template class for reductions. \todo We should be able |
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| 77 | to do vectorization, at least for complete reduction. */ |
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| 78 | template<typename T_expr, int N_index, typename T_reduction> |
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| 79 | class _bz_ArrayExprReduce { |
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| 80 | |
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| 81 | public: |
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| 82 | typedef _bz_typename T_reduction::T_numtype T_numtype; |
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| 83 | |
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| 84 | // select return type |
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| 85 | typedef typename unwrapET<typename T_expr::T_result>::T_unwrapped test; |
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| 86 | typedef typename selectET<typename T_expr::T_typeprop, |
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| 87 | T_numtype, |
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| 88 | _bz_ArrayExprReduce<test, N_index, T_reduction> >::T_selected T_typeprop; |
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| 89 | typedef typename unwrapET<T_typeprop>::T_unwrapped T_result; |
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| 90 | typedef T_numtype T_optype; |
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| 91 | |
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| 92 | typedef T_expr T_ctorArg1; |
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| 93 | typedef T_reduction T_ctorArg2; |
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| 94 | typedef int T_range_result; // dummy |
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| 95 | |
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| 96 | static const int |
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| 97 | numArrayOperands = T_expr::numArrayOperands, |
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| 98 | numTVOperands = T_expr::numTVOperands, |
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| 99 | numTMOperands = T_expr::numTMOperands, |
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| 100 | numIndexPlaceholders = T_expr::numIndexPlaceholders + 1, |
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| 101 | minWidth = simdTypes<T_numtype>::vecWidth, |
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| 102 | maxWidth = simdTypes<T_numtype>::vecWidth, |
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| 103 | rank_ = T_expr::rank_ - 1; |
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| 104 | |
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| 105 | /** Vectorization doesn't work for index expressions, so we can use |
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| 106 | a dummy here. */ |
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| 107 | template<int N> struct tvresult { |
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| 108 | typedef FastTV2Iterator<T_numtype, N> Type; |
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| 109 | }; |
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| 110 | |
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| 111 | _bz_ArrayExprReduce(const _bz_ArrayExprReduce& reduce) |
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| 112 | : reduce_(reduce.reduce_), iter_(reduce.iter_), ordering_(reduce.ordering_) { } |
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| 113 | |
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| 114 | _bz_ArrayExprReduce(T_expr expr) |
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| 115 | : iter_(expr) |
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| 116 | { computeOrdering(); } |
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| 117 | |
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| 118 | #if 0 |
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| 119 | _bz_ArrayExprReduce(T_expr expr, T_reduction reduce) |
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| 120 | : iter_(expr), reduce_(reduce) |
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| 121 | { computeOrdering(); } |
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| 122 | #endif |
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| 123 | |
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| 124 | int ascending(const int r) const { return iter_.ascending(r); } |
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| 125 | int ordering(const int r) const { return ordering_[r]; } |
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| 126 | int lbound(const int r) const { return iter_.lbound(r); } |
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| 127 | int ubound(const int r) const { return iter_.ubound(r); } |
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| 128 | RectDomain<rank_> domain() const { return iter_.domain(); } |
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| 129 | |
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| 130 | template<int N_destRank> |
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| 131 | T_numtype operator()(const TinyVector<int, N_destRank>& destIndex) const |
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| 132 | { |
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| 133 | BZPRECHECK(N_destRank == N_index, |
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| 134 | "Array reduction performed over rank " << N_index |
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| 135 | << " to produce a rank " << N_destRank << " expression." << endl |
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| 136 | << "You must reduce over rank " << N_destRank << " instead."); |
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| 137 | |
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| 138 | TinyVector<int, N_destRank + 1> index; |
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| 139 | |
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| 140 | // This metaprogram copies elements 0..N-1 of destIndex into index |
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| 141 | _bz_meta_vecAssign<N_index, 0>::assign(index, destIndex, |
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| 142 | _bz_update<int,int>()); |
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| 143 | |
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| 144 | int lbound = iter_.lbound(N_index); |
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| 145 | int ubound = iter_.ubound(N_index); |
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| 146 | |
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| 147 | BZPRECHECK((lbound != tiny(int())) && (ubound != huge(int())), |
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| 148 | "Array reduction performed over rank " << N_index |
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| 149 | << " is unbounded." << endl |
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| 150 | << "There must be an array object in the expression being reduced" |
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| 151 | << endl << "which provides a bound in rank " << N_index << "."); |
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| 152 | |
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| 153 | // If we are doing minIndex/maxIndex, initialize with lower bound |
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| 154 | |
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| 155 | _bz_ReduceReset<T_reduction::needIndex,T_reduction::needInit> reset; |
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| 156 | reset(reduce_,lbound,iter_); |
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| 157 | |
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| 158 | for (index[N_index]=lbound; index[N_index]<=ubound; ++index[N_index]) { |
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| 159 | if (!reduce_(iter_(index), index[N_index])) |
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| 160 | break; |
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| 161 | } |
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| 162 | |
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| 163 | return reduce_.result(ubound-lbound+1); |
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| 164 | } |
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| 165 | |
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| 166 | // If you have a precondition failure on these routines, it means |
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| 167 | // you are trying to use stack iteration mode on an expression |
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| 168 | // which contains an index placeholder. You must use index |
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| 169 | // iteration mode instead. |
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| 170 | |
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| 171 | int operator*() const { |
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| 172 | BZPRECHECK(0,"Can't use stack iteration on a reduction."); return 0; } |
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| 173 | int suggestStride(int) const { |
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| 174 | BZPRECHECK(0,"Can't use stack iteration on a reduction."); return 0; } |
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| 175 | |
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| 176 | void push(int) const { |
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| 177 | BZPRECHECK(0,"Can't use stack iteration on a reduction."); } |
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| 178 | void pop(int) const { |
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| 179 | BZPRECHECK(0,"Can't use stack iteration on a reduction."); } |
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| 180 | void advance() const { |
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| 181 | BZPRECHECK(0,"Can't use stack iteration on a reduction."); } |
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| 182 | void advance(int) const { |
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| 183 | BZPRECHECK(0,"Can't use stack iteration on a reduction."); } |
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| 184 | void loadStride(int) const { |
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| 185 | BZPRECHECK(0,"Can't use stack iteration on a reduction."); } |
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| 186 | void advanceUnitStride() const { |
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| 187 | BZPRECHECK(0,"Can't use stack iteration on a reduction."); } |
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| 188 | |
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| 189 | template<int N_rank> |
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| 190 | void moveTo(const TinyVector<int,N_rank>&) const { |
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| 191 | BZPRECHECK(0,"Stencils of reductions are not implemented"); } |
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| 192 | |
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| 193 | bool isUnitStride(int) const { |
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| 194 | BZPRECHECK(0,"Can't use stack iteration on a reduction."); return false; } |
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| 195 | bool isUnitStride() const { |
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| 196 | BZPRECHECK(0,"Can't use stack iteration on a reduction."); return false; } |
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| 197 | bool canCollapse(int,int) const { |
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| 198 | BZPRECHECK(0,"Can't use stack iteration on a reduction."); return false; } |
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| 199 | bool isStride(int,int) const { |
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| 200 | BZPRECHECK(0,"Can't use stack iteration on a reduction."); return true; } |
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| 201 | |
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| 202 | T_numtype operator[](int) const { |
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| 203 | BZPRECHECK(0,"Can't use stack iteration on a reduction."); return T_numtype(); } |
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| 204 | T_numtype fastRead(int) const { |
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| 205 | BZPRECHECK(0,"Can't use stack iteration on a reduction."); return T_numtype(); } |
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| 206 | |
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| 207 | template<int N> |
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| 208 | typename tvresult<N>::Type fastRead_tv(int) const { |
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| 209 | BZPRECHECK(0,"Can't use stack iteration on an index mapping."); |
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| 210 | return TinyVector<T_numtype, N>(); |
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| 211 | } |
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| 212 | |
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| 213 | /** Determining whether the resulting expression is aligned is |
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| 214 | difficult, so to be safe we say no. It shouldn't be attempted |
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| 215 | anyway, though. */ |
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| 216 | bool isVectorAligned(diffType offset) const { |
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| 217 | return false; } |
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| 218 | |
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| 219 | // don't know how to define these, so stencil expressions won't work |
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| 220 | T_result shift(int offset, int dim) const |
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| 221 | { BZPRECHECK(0,"Stencils of reductions are not implemented"); return T_numtype(); } |
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| 222 | T_result shift(int offset1, int dim1,int offset2, int dim2) const |
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| 223 | { BZPRECHECK(0,"Stencils of reductions are not implemented"); return T_numtype(); } |
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| 224 | void _bz_offsetData(sizeType i) { BZPRECONDITION(0); } |
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| 225 | |
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| 226 | // Unclear how to define this, and stencils don't work anyway |
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| 227 | T_range_result operator()(RectDomain<rank_> d) const |
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| 228 | { BZPRECHECK(0,"Stencils of reductions are not implemented"); |
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| 229 | return T_range_result(); } |
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| 230 | |
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| 231 | void prettyPrint(BZ_STD_SCOPE(string) &str, prettyPrintFormat& format) const |
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| 232 | { |
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| 233 | // NEEDS_WORK-- do real formatting for reductions |
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| 234 | str += "reduce[NEEDS_WORK]("; |
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| 235 | iter_.prettyPrint(str,format); |
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| 236 | str += ")"; |
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| 237 | } |
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| 238 | |
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| 239 | /** \todo do a real shape check (tricky) */ |
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| 240 | template<typename T_shape> |
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| 241 | bool shapeCheck(const T_shape&) const |
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| 242 | { |
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| 243 | return true; |
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| 244 | } |
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| 245 | |
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| 246 | // sliceinfo for expressions |
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| 247 | template<typename T1, typename T2 = nilArraySection, |
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| 248 | class T3 = nilArraySection, typename T4 = nilArraySection, |
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| 249 | class T5 = nilArraySection, typename T6 = nilArraySection, |
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| 250 | class T7 = nilArraySection, typename T8 = nilArraySection, |
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| 251 | class T9 = nilArraySection, typename T10 = nilArraySection, |
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| 252 | class T11 = nilArraySection> |
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| 253 | class SliceInfo { |
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| 254 | public: |
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| 255 | typedef typename T_expr::template SliceInfo<T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11>::T_slice T_slice1; |
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| 256 | typedef _bz_ArrayExprReduce<T_slice1, N_index, T_reduction> T_slice; |
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| 257 | }; |
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| 258 | |
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| 259 | template<typename T1, typename T2, typename T3, typename T4, typename T5, typename T6, |
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| 260 | typename T7, typename T8, typename T9, typename T10, typename T11> |
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| 261 | typename SliceInfo<T1,T2,T3,T4,T5,T6,T7,T8,T9,T10,T11>::T_slice |
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| 262 | operator()(T1 r1, T2 r2, T3 r3, T4 r4, T5 r5, T6 r6, T7 r7, T8 r8, T9 r9, T10 r10, T11 r11) const |
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| 263 | { |
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| 264 | // for slicing reduction results, we would need to set the |
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| 265 | // dimension reduced over to Range::all(). That's not easy to do |
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| 266 | // because it requires us to change the type of one of the rn's. |
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| 267 | BZPRECONDITION(0); |
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| 268 | } |
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| 269 | |
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| 270 | private: |
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| 271 | _bz_ArrayExprReduce() { } |
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| 272 | |
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| 273 | /** Method for properly initializing the ordering values. \todo If |
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| 274 | the expression being reduced consist of arrays with different |
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| 275 | orderings, the call to iter_.ordering() will fail with a |
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| 276 | "different orderings" error. But just like it can happen that |
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| 277 | ordering values are missing from the expression, it seems |
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| 278 | equally valid that ordering is indefinite in cases where the |
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| 279 | expression has differing values. This doesn't prevent us from |
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| 280 | assigning the expression to an array, and it shouldn't prevent |
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| 281 | the expression from being used in a reduction either. (This is |
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| 282 | bug 2058441.) */ |
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| 283 | void computeOrdering() |
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| 284 | { |
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| 285 | TinyVector<bool,rank_> in_ordering; |
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| 286 | in_ordering = false; |
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| 287 | |
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| 288 | int j = 0; |
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| 289 | for (int i=0; i<rank_; ++i) { |
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| 290 | const int orderingj = iter_.ordering(i); |
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| 291 | if (orderingj != tiny(int()) && orderingj < rank_ && !in_ordering(orderingj)) { |
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| 292 | // unique value in ordering array |
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| 293 | in_ordering(orderingj) = true; |
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| 294 | ordering_(j++) = orderingj; |
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| 295 | } |
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| 296 | } |
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| 297 | |
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| 298 | // It is possible that ordering is not a permutation of 0,...,rank-1. |
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| 299 | // In that case j will be less than rank. We fill in ordering with |
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| 300 | // the unused values in decreasing order. |
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| 301 | for (int i = rank_; j < rank_; ++j) { |
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| 302 | while (in_ordering(--i)); // find an unused index |
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| 303 | ordering_(j) = i; |
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| 304 | } |
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| 305 | } |
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| 306 | |
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| 307 | T_reduction reduce_; |
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| 308 | T_expr iter_; |
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| 309 | TinyVector<int,rank_> ordering_; |
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| 310 | }; |
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| 311 | |
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| 312 | #define BZ_DECL_ARRAY_PARTIAL_REDUCE(fn,reduction) \ |
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| 313 | template<typename T_expr, int N_index> \ |
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| 314 | inline \ |
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| 315 | _bz_ArrayExpr<_bz_ArrayExprReduce<_bz_typename BZ_BLITZ_SCOPE(asExpr)<T_expr>::T_expr, \ |
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| 316 | N_index, \ |
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| 317 | reduction<_bz_typename T_expr::T_numtype> > > \ |
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| 318 | fn(const BZ_BLITZ_SCOPE(ETBase)<T_expr>& expr, \ |
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| 319 | const IndexPlaceholder<N_index>&) \ |
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| 320 | { \ |
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| 321 | return _bz_ArrayExprReduce<_bz_typename BZ_BLITZ_SCOPE(asExpr)<T_expr>::T_expr, \ |
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| 322 | N_index, \ |
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| 323 | reduction<_bz_typename T_expr::T_numtype> > \ |
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| 324 | (BZ_BLITZ_SCOPE(asExpr)<T_expr>::getExpr(expr.unwrap())); \ |
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| 325 | } |
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| 326 | |
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| 327 | BZ_DECL_ARRAY_PARTIAL_REDUCE(sum, ReduceSum) |
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| 328 | BZ_DECL_ARRAY_PARTIAL_REDUCE(mean, ReduceMean) |
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| 329 | BZ_DECL_ARRAY_PARTIAL_REDUCE((min), ReduceMin) |
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| 330 | BZ_DECL_ARRAY_PARTIAL_REDUCE(minIndex, ReduceMinIndex) |
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| 331 | BZ_DECL_ARRAY_PARTIAL_REDUCE((max), ReduceMax) |
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| 332 | BZ_DECL_ARRAY_PARTIAL_REDUCE(maxIndex, ReduceMaxIndex) |
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| 333 | BZ_DECL_ARRAY_PARTIAL_REDUCE(product, ReduceProduct) |
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| 334 | BZ_DECL_ARRAY_PARTIAL_REDUCE(count, ReduceCount) |
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| 335 | BZ_DECL_ARRAY_PARTIAL_REDUCE(any, ReduceAny) |
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| 336 | BZ_DECL_ARRAY_PARTIAL_REDUCE(all, ReduceAll) |
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| 337 | BZ_DECL_ARRAY_PARTIAL_REDUCE(first, ReduceFirst) |
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| 338 | BZ_DECL_ARRAY_PARTIAL_REDUCE(last, ReduceLast) |
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| 339 | |
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| 340 | /* |
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| 341 | * Complete reductions |
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| 342 | */ |
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| 343 | |
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| 344 | // Prototype of reduction functions |
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| 345 | template<typename T_expr, typename T_reduction> |
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| 346 | _bz_typename T_reduction::T_resulttype |
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| 347 | _bz_ArrayExprFullReduce(T_expr expr, T_reduction reduction); |
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| 348 | |
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| 349 | template<typename T_expr, typename T_reduction> |
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| 350 | _bz_typename T_reduction::T_resulttype |
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| 351 | _bz_reduceWithIndexTraversal(T_expr expr, T_reduction reduction); |
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| 352 | |
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| 353 | template<typename T_expr, typename T_reduction> |
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| 354 | _bz_typename T_reduction::T_resulttype |
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| 355 | _bz_reduceWithIndexVectorTraversal(T_expr expr, T_reduction reduction); |
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| 356 | |
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| 357 | #define BZ_DECL_ARRAY_FULL_REDUCE(fn,reduction) \ |
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| 358 | template<typename T_expr> \ |
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| 359 | _bz_inline_et \ |
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| 360 | _bz_typename reduction<_bz_typename T_expr::T_numtype>::T_resulttype \ |
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| 361 | fn(const BZ_BLITZ_SCOPE(ETBase)<T_expr>& expr) \ |
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| 362 | { \ |
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| 363 | return _bz_ArrayExprFullReduce \ |
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| 364 | (BZ_BLITZ_SCOPE(asExpr)<T_expr>::getExpr(expr.unwrap()), \ |
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| 365 | reduction<_bz_typename T_expr::T_numtype>()); \ |
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| 366 | } \ |
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| 367 | |
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| 368 | BZ_DECL_ARRAY_FULL_REDUCE(sum, ReduceSum) |
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| 369 | BZ_DECL_ARRAY_FULL_REDUCE(mean, ReduceMean) |
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| 370 | BZ_DECL_ARRAY_FULL_REDUCE((min), ReduceMin) |
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| 371 | BZ_DECL_ARRAY_FULL_REDUCE((max), ReduceMax) |
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| 372 | BZ_DECL_ARRAY_FULL_REDUCE((minmax), ReduceMinMax) |
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| 373 | BZ_DECL_ARRAY_FULL_REDUCE(product, ReduceProduct) |
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| 374 | BZ_DECL_ARRAY_FULL_REDUCE(count, ReduceCount) |
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| 375 | BZ_DECL_ARRAY_FULL_REDUCE(any, ReduceAny) |
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| 376 | BZ_DECL_ARRAY_FULL_REDUCE(all, ReduceAll) |
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| 377 | BZ_DECL_ARRAY_FULL_REDUCE(first, ReduceFirst) |
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| 378 | BZ_DECL_ARRAY_FULL_REDUCE(last, ReduceLast) |
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| 379 | |
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| 380 | // Special versions of complete reductions: minIndex and |
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| 381 | // maxIndex |
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| 382 | |
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| 383 | #define BZ_DECL_ARRAY_FULL_REDUCE_INDEXVECTOR(fn,reduction) \ |
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| 384 | template<typename T_expr> \ |
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| 385 | _bz_inline_et \ |
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| 386 | _bz_typename reduction<_bz_typename T_expr::T_numtype, \ |
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| 387 | T_expr::rank_>::T_resulttype \ |
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| 388 | fn(const BZ_BLITZ_SCOPE(ETBase)<T_expr>& expr) \ |
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| 389 | { \ |
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| 390 | return _bz_reduceWithIndexVectorTraversal \ |
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| 391 | (BZ_BLITZ_SCOPE(asExpr)<T_expr>::getExpr(expr.unwrap()), \ |
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| 392 | reduction<_bz_typename T_expr::T_numtype, T_expr::rank_>()); \ |
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| 393 | } |
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| 394 | |
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| 395 | BZ_DECL_ARRAY_FULL_REDUCE_INDEXVECTOR(minIndex, ReduceMinIndexVector) |
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| 396 | BZ_DECL_ARRAY_FULL_REDUCE_INDEXVECTOR(maxIndex, ReduceMaxIndexVector) |
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| 397 | |
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| 398 | BZ_NAMESPACE_END |
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| 399 | |
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| 400 | #include <blitz/array/reduce.cc> |
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| 401 | |
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| 402 | #endif // BZ_ARRAYREDUCE_H |
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