为什么 std::vector 这么快（或者我的实现太慢）

前几天我在玩游戏，试图看看我能在多大程度上优化某些东西。我决定从一个简单的映射开始，它只进行线性搜索来查找元素是否存在，然后尝试对其大部分进行优化。另外，为了进行比较，我使用 std::find 对 std::map 和 std::vector 执行相同的操作。

地图的结果是预期的，创建和销毁比我的地图慢，但速度快得多（实际上，我无法测量它，它总是返回 0）。 问题出在 std::vector 上。我预计它会比我的实现慢，但事实并非如此，而且我真的不明白它怎么可能相同或更快，因为我的实现正在跳过最坏的情况（该值不在向量中）并且是使用结果缓存。

任何人都可以在这里阐明一些情况吗？我知道stl背后的人都是半神，但这仍然没有意义。

基准测试结果（i3、Windows 8.1 Pro 64、Visual Studio 2013）：

std::vector :
    Build : 85.0042 ms
    Loop : 37.0011 ms
    Find : 1.82259 ms  -> First : Found, Second : Found, Third : Not Found
    Release : 0 ms
--------------------
std::map :
    Build : 6929.41 ms
    Loop : 570.032 ms
    Find : 0 ms  -> First : Found, Second : Found, Third : Not Found
    Release : 1425.08
--------------------
Linear Map V0:
    Build : 194.012 ms
    Loop : 49.0052 ms
    Find : 1.88915 ms -> First : Found, Second : Found, Third : Not Found
    Release : 109.004

这是地图的代码：

template<typename T>
class LinearMap0
{
public:
LinearMap0()
{
    _end = _root = new Node;
    _prebuffer = nullptr;
    prebufferCapacity = 0;
    _alive = true;
    prebufferMarker = 0;
    _cache = _mm_set1_epi32(-1);
    for (auto& ptr : _cacheBuffer) ptr = nullptr;
    MinID = INT32_MAX - 1;
    MaxID = -1;
}
void PreAllocate(int Count)
{
    prebufferCapacity = Count;
    _prebuffer = new Node[Count];
}
~LinearMap0()
{
    if (_alive)
    {
        Release();
    }
}
void Release()
{
    Node* marker = _end;
    while (marker->Prev)
    {
        marker = marker->Prev;
        if (!marker->Next->IsPreAllocated) delete marker->Next;
    }

    if (!_root->IsPreAllocated) delete _root;
    delete[] _prebuffer;

    _alive = false;
}

void AddElement(int ID,T element)
{
    Node* tmp = nullptr;
    if (prebufferMarker < prebufferCapacity)
    {
        // Use a pre-allocated object
        tmp = &_prebuffer[prebufferMarker];
        prebufferMarker++;
        tmp->IsPreAllocated = true;
    }
    else
    {
        tmp = new Node;
    }

    tmp->ID = ID;
    tmp->Data = element;

    // Update list
    _end->Next = tmp;
    Node* prevEnd = _end;
    _end = tmp;
    _end->Prev = prevEnd;
    bool isMin = ID < MinID; MinID = ID * isMin + (1 - isMin) * MinID;
    bool isMax = ID > MaxID; MaxID = ID * isMax + (1 - isMax) * MaxID;
}
void DeleteLast()
{
    Node* tmp = _end;

    _end = _end->Prev;
    _end->Next = nullptr;

    delete tmp;
}

template<class Function>
void Loop(Function&& f, bool Forward = true)
{
    if (Forward)
    {
        Node* marker = _root;
        while (marker->Next)
        {
            marker = marker->Next;
            f(marker->Data);
        }
    }
    else
    {
        Node* marker = _end;
        while (marker->Prev)
        {
            marker = marker->Prev;
            f(marker->Data);
        }
    }
}

T* Find(int ID)
{
    // Bounds check
    if (ID < MinID || ID > MaxID) return nullptr;

    // Check it it's in the cache

    // Compare the value to every value in the cache
    __m128i idxSSE = _mm_set1_epi32(ID);
    __m128i C = _mm_cmpeq_epi32(_cache, idxSSE);

    // To change form -1 to 1
    C = _mm_mul_epi32(C, _mm_set1_epi32(-1));

    // Now C holds 1 if true, or 0 if false (in each of its 4 members). It should only be ONE set at 1
    __m128i tmp = _mm_set1_epi32(1);
    __m128i S = _mm_sub_epi32(tmp, C);

    // Now find the index
    int i = S.m128i_i32[0] * (C.m128i_i32[1] + S.m128i_i32[1] * (2 * C.m128i_i32[2] + S.m128i_i32[2] * (3 * C.m128i_i32[3] + S.m128i_i32[3] * -1)));

    if (i != -1)
        return _cacheBuffer[i];

    // Traverse the list
    Node* marker0 = _root;
    T* obj = nullptr;

    while (true)
    {
        if (marker0->ID == ID)
        {
            obj = &marker0->Data;
        }

        if (marker0->Next) marker0 = marker0->Next; else break;
    }

    // Cache value and return
    _cache.m128i_i32[cacheMarker] = ID;
    _cacheBuffer[cacheMarker] = obj;
    cacheMarker = (cacheMarker + 1) & 3; // x & 3 = x % 4

    return obj;
}
private:
struct Node
{
    Node()
    {
        Prev = nullptr;
        Next = nullptr;
        IsPreAllocated = false;
        ID = -1;
    }
    T Data;
    Node* Prev;
    Node* Next;
    bool IsPreAllocated;
    int ID;
};

Node* _root;
Node* _end;

Node* _prebuffer;
int prebufferCapacity;
int prebufferMarker;

bool _alive;

__m128i _cache;
T* _cacheBuffer[4];
int cacheMarker;
int MinID, MaxID;
};

这是基准：

// Initialize seeds
const __int64 ecount = 5 * 1000*1000;
vector<__int64> seed(ecount);
for (__int64 i = 0; i < ecount; i++)
{
    seed[i] = i;
}
random_shuffle(seed.begin(), seed.end());

///////////// std::vector

vector<__int64> v;

cout << "--------------------" << endl;
cout << "std::vector :" << endl;
cout << "   Build : " << time_call([&]()
{
    v.resize(ecount/2);
    for (__int64 i = 0; i < ecount; i++)
    {
        if (i < (ecount / 2))
            v[i] = seed[i];
        else
            v.push_back(seed[i]);
    }
}) << " ms" << endl;

cout << "   Loop : " << time_call([&]()
{
    for (auto& n : v)
        n /= 2;
}) << " ms" << endl;

bool found1, found2, found3;
cout << "   Find : " << (((float)time_call([&]()
{
    for (int i = 0; i < 15; i++)
    {
        // Should exist
        found1 = find(v.begin(), v.end(), seed[5] / 2) != v.end();//find(seed[5]) != m.end();
        found2 = find(v.begin(), v.end(), seed[1000] / 2) != v.end();

        // Shouldn't exist
        found3 = find(v.begin(), v.end(), -1234) != v.end();
    }
})) / 15.0) / 3.0;
cout << " ms " << " -> First : " << ((found1) ? "Found" : "Not Found") << ", Second : " << ((found2) ? "Found" : "Not Found") << ", Third : " << ((found3) ? "Found" : "Not Found") << endl;

cout << "   Release : " << time_call([&]()
{
    v.clear();
}) << " ms" << endl;

///////////// std::map

map<__int64, __int64> m;

cout << "--------------------" << endl;
cout << "std::map :" << endl;
cout << "   Build : " << time_call([&]()
{
    for (__int64 i = 0; i < ecount; i++)
    {
        m[seed[i]] = seed[i];
    }
}) << " ms" << endl;

cout << "   Loop : " << time_call([&]()
{
    for (auto& n : m)
        n.second /= 2;
}) << " ms" << endl;

cout << "   Find : " << (((float)time_call([&]()
{
    for (int i = 0; i < 15; i++)
    {
        // Should exist
        found1 = m.find(seed[5]) != m.end();
        found2 = m.find(seed[1000]) != m.end();

        // Shouldn't exist
        found3 = m.find(-1234) != m.end();
    }
})) / 15.0) / 3.0;
cout << " ms " << " -> First : " << ((found1) ? "Found" : "Not Found") << ", Second : " << ((found2) ? "Found" : "Not Found") << ", Third : " << ((found3) ? "Found" : "Not Found") << endl;

cout << "   Release : " << time_call([&]()
{
    m.clear();
}) << endl;

///////////// Linear Map V0

LinearMap0<__int64> c;

cout << "--------------------" << endl;
cout << "Linear Map V0:" << endl;
cout << "   Build : " << time_call([&]()
{
    c.PreAllocate(ecount / 2);
    for (__int64 i = 0; i < ecount; i++)
    {
        c.AddElement(seed[i],seed[i]);
    }
}) << " ms" << endl;

cout << "   Loop : " << time_call([&]()
{
    c.Loop([](__int64& Data)
    {
        Data /= 2;
    });
}) << " ms" << endl;

cout << "   Find : " << (((float)time_call([&]()
{
    for (int i = 0; i < 15; i++)
    {
        // Should exist
        found1 = c.Find(seed[5]);
        found2 = c.Find(seed[1000]);

        // Shouldn't exist
        found3 = c.Find(-1234);
    }
})) / 15.0) / 3.0;
cout << " ms -> First : " << ((found1) ? "Found" : "Not Found") << ", Second : " << ((found2) ? "Found" : "Not Found") << ", Third : " << ((found3) ? "Found" : "Not Found") << endl;

cout << "   Release : " << time_call([&]()
{
    c.Release();
}) << endl;

编辑： time_call 是：

template <class Function>
double time_call(Function&& f)
{
    chrono::time_point<chrono::high_resolution_clock> start, end;
    start = chrono::high_resolution_clock::now();
        f();
    end = chrono::high_resolution_clock::now();

    return ((double)(chrono::duration_cast<chrono::nanoseconds>(end - start).count())) / 1000000.0;
}

你的容器是一个链表，而std::vector是一个动态大小的数组。

链表方法有很多好处，例如能够插入元素而无需重新分配。

然而，数组方法有一些显着的优点：

• 线性搜索只是扫描内存，这正是缓存和预取器的用途。链表的扫描效率较低，因为每次跳转到未缓存的内存都意味着昂贵的缓存未命中。
• 线性阵列扫描很容易矢量化。如果你编译-O3那么编译器可能会使用向量化版本std::find。由于内存依赖性，不可能对链表扫描进行矢量化。
• 使用的内存量。你的链表必须维护一个next指针可以有效地使你的元素变大。此外，每个非预分配Node必须支付分配器的开销（即会计数据new and delete）。这意味着您会更快达到内存带宽限制，并且可以在缓存中容纳更少的元素。