使用C ++ 11可变参数模板在编译时快速排序 [英] Quick sort at compilation time using C++11 variadic templates

问题描述

我刚刚实现了快速排序算法，使用C ++ 11可变参数模板在编译时评估它。但是，当数据集太大时，我遇到性能问题。

I just implemented the quick sort algorithm by using C++11 variadic templates to evaluate it at compilation time. However, I encounter a performance issue when the data set is too large.

#include <iostream>

using namespace std;

template<int... vs>
struct Seq
{}; 
template<int v1, int...vs>
struct Seq<v1, vs...>{
};


template<typename newT, typename srcT>
struct PushFront{
};
template<int vadded, int...vs>
struct PushFront<Seq<vadded>, Seq<vs...>>{
  typedef Seq<vadded, vs...> ResultType;
};

template<typename T>
struct PopFront{
};
template<int v1, int...vs>
struct PopFront<Seq<v1, vs...>>{
  typedef Seq<vs...> RemaindType;
  typedef Seq<v1>    ResultType;
};

template<typename T1, typename T2>
struct CatSeq{};
template<int...v, int...us>
struct CatSeq<Seq<v...>, Seq<us...>>{
  typedef Seq< v..., us... >  ResultType;
};


template<bool c, typename NewT, typename TrueClsT, typename FalseClsT>
struct Classify{
};
template<typename NewT, typename TrueClsT, typename FalseClsT>
struct Classify<true, NewT, TrueClsT, FalseClsT>{
  typedef typename PushFront<NewT, TrueClsT>::ResultType NewTrueClsT;
  typedef FalseClsT  NewFalseClsT;
};
template<typename NewT, typename TrueClsT, typename FalseClsT>
struct Classify<false, NewT, TrueClsT, FalseClsT>{
  typedef TrueClsT  NewTrueClsT;
  typedef typename PushFront<NewT, FalseClsT>::ResultType NewFalseClsT;
};

template<typename T1, typename T2>
struct Compare{};
template<int v1, int v2>
struct Compare<Seq<v1>, Seq<v2>>{
  static const bool result=(v1>=v2); 
};


template<typename AnchorT, typename SeqT, typename GESet, typename LSet>
struct PartitionImpl{};
template<typename GESet, typename LSet, int anchorv, int v1>
struct PartitionImpl<Seq<anchorv>, Seq<v1>, GESet, LSet>{
  static const bool isge=Compare<typename PopFront<Seq<v1>>::ResultType, Seq<anchorv>>::result;
  typedef typename Classify<isge, Seq<v1>, GESet, LSet>::NewTrueClsT  RstGESet;
  typedef typename Classify<isge, Seq<v1>, GESet, LSet>::NewFalseClsT  RstLSet;  
};
template<typename GESet, typename LSet, int anchorv, int v1, int...vs>
struct PartitionImpl<Seq<anchorv>, Seq<v1, vs...>, GESet, LSet>{
  static const bool isge=Compare<typename PopFront<Seq<v1, vs...>>::ResultType, Seq<anchorv>>::result;
  typedef typename Classify<isge, Seq<v1>, GESet, LSet>::NewTrueClsT  TmpRstGESet;
  typedef typename Classify<isge, Seq<v1>, GESet, LSet>::NewFalseClsT  TmpRstLSet;

  typedef typename PartitionImpl<Seq<anchorv>, Seq<vs...>, TmpRstGESet, TmpRstLSet>::RstGESet RstGESet;
  typedef typename PartitionImpl<Seq<anchorv>, Seq<vs...>, TmpRstGESet, TmpRstLSet>::RstLSet  RstLSet;
};


template<typename T>
struct Partition{
};
template<int v1, int v2, int...vs>
struct Partition<Seq<v1, v2, vs...>>{
  typedef Seq<v1> AnchorType;
  typedef Seq<> GESet;
  typedef Seq<> LSet;
  typedef typename PartitionImpl<AnchorType, Seq<v1, v2, vs...>, GESet, LSet>::RstGESet  RstGESet;
  typedef typename PartitionImpl<AnchorType, Seq<v1, v2, vs...>, GESet, LSet>::RstLSet   RstLSet;
};

//why introduce this? refer to Sort
template<typename SrcT, typename GESet, typename LSet, template<typename > class SortOp>
struct SortSub{  
  typedef typename SortOp<GESet>::ResultType  TmpGESet2;
  typedef typename SortOp<LSet>::ResultType   TmpLSet2;
};
template<typename SrcT, typename LSet, template<typename> class SortOp>
struct SortSub<SrcT, SrcT, LSet, SortOp>{
  typedef SrcT  TmpGESet2;
  typedef typename SortOp<LSet>::ResultType   TmpLSet2;
};
template<typename SrcT, typename GESet, template<typename> class SortOp>
struct SortSub<SrcT, GESet, SrcT, SortOp>{
  typedef typename SortOp<GESet>::ResultType  TmpGESet2;
  typedef SrcT   TmpLSet2;
};

template<typename T>
struct Sort;
template<>
struct Sort<Seq<>>{
  typedef Seq<> ResultType;
};
template<int v>
struct Sort< Seq<v> >{
  typedef Seq<v> ResultType;
};
template<int v1, int...vs>
struct Sort< Seq<v1, vs...> >{
  typedef Seq<v1, vs...> SrcType;
  typedef typename Partition< Seq<v1, vs...> >::RstGESet TmpGESet;
  typedef typename Partition< Seq<v1, vs...> >::RstLSet TmpLSet;

  //to by pass the case SrcType <==> TmpGESet or  SrcType <==> TmpLSet
  typedef typename SortSub<SrcType, TmpGESet, TmpLSet, Sort>::TmpGESet2  TmpGESet2;
  typedef typename SortSub<SrcType, TmpGESet, TmpLSet, Sort>::TmpLSet2   TmpLSet2;

  typedef typename CatSeq<TmpGESet2, TmpLSet2>::ResultType ResultType;
};


void dumpSeqTypeImpl(Seq<> ){
}
template<int v1>
void dumpSeqTypeImpl(Seq<v1> ){
  cout<<v1<<" ";
}
template<int v1, int...vs>
void dumpSeqTypeImpl(Seq<v1, vs...> ){
  cout<<v1<<" ";
  dumpSeqTypeImpl( Seq<vs...>() );
}
template<int...vs>
void dumpSeqType(Seq<vs...> ){
  cout<<"Seq type < ";
  dumpSeqTypeImpl( Seq<vs...>() );
  cout<<" >"<<endl;
}

    //test data
#include "qsort_input.txt"

int main(){
  //Seq<>  s0;// aggregate ‘Seq<> s0’ has incomplete type and cannot be defined
  Seq<1> s1;
  Seq<1, 2> s2;

  typedef Seq<5, 5, 5> TestType_SAME;
  TestType_SAME same;
  dumpSeqType( same );
  typename Partition< TestType_SAME >::RstGESet _ts1;
  typename Partition< TestType_SAME >::RstLSet _ts2;
  dumpSeqType( _ts1 );
  dumpSeqType( _ts2 );

#if 1
  typedef Seq<4, 7, 3, 9, 1, 2, 5, 5, 19, 5> TestType;
  TestType s3;
  dumpSeqType( s3 );
  typename Partition< TestType >::RstGESet ts1;
  typename Partition< TestType >::RstLSet ts2;
  dumpSeqType( ts1 );
  dumpSeqType( ts2 );

  typename Sort<TestType>::ResultType so1;
  dumpSeqType( so1 );
#endif 

#if 1
  typedef Seq<TEST_DATA_100> TAdvanceType;
  typename Sort<TAdvanceType>::ResultType soadvance;
  dumpSeqType(soadvance);
#endif

  return 0;
}

当数据集为TEST_DATA_100时，需要1.7s编译。 >
当数据集为TEST_DATA_1000时，编译器似乎暂停....

When the data set is TEST_DATA_100, it takes 1.7s to compile.
When the data set is TEST_DATA_1000, the compiler seems to halt....

我使用gcc 4.6.0。

I'm using gcc 4.6.0.

推荐答案

你还看过它的内存消耗吗？请注意，快速排序本身比线性更差，运行时的情况相当糟糕。这与模板编译和实例化的某些步骤（有时是指数）的线性运行时行为相比更糟糕。你应该可以绘制你的compiletime为各种数据集观察真正的复杂性类的代码。通常模板元编程与这样大的数据集是不可行的。

Have you also looked at its memory consumption? Note that quicksort itself is worse than linear, with a quite bad worse case runtime. This multiplies with the worse than linear runtime behaviour of certain steps of template compilation and instantiation (sometimes those are exponentional). You should maybe graph your compiletime for various datasets to observe the real complexity class for your code. Usually template metaprogramming with such large datasets is not feasible.

编辑：
出于好奇我尝试了代码，直到〜500，它大致遵循公式pow（N * log（N），1.47）* 0.0004 + 0.6，但开始得到令人难以置信的慢得多，700个项目155秒。此外，它开始吃非常多的ram（3GiB为600），这导致我的结论是，对于1000个元素，它将需要更多的ram比大多数人都有，需要几个小时来编译。

Out of curiosity I tried the code out and found that up until ~500 it follows roughly the formula pow(N*log(N),1.47)*0.0004+0.6 but then starts to get incredibly much slower, with 155 seconds for 700 items. Also at around that it starts eating very much ram (3GiB for 600) which leads me to the conclusion that for 1000 elements it will need more ram than most people have and will take hours to compile.

进一步注意，当不是每个元素都是唯一的，代码不工作。

Further note that the code does not work when not every element is unique.

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