boost :: flat_map及其与map和unordered_map相比的性能 [英] boost::flat_map and its performance compared to map and unordered_map

问题描述

编程中常见的知识是存储器局部性由于缓存命中而提高了性能。我最近发现了一个基于向量的地图实现的 boost :: flat_map 。它似乎并不像你的典型的地图 / unordered_map 那么流行，所以我还没有找到任何性能比较。如何比较，最好的用例是什么？

谢谢！

解决方案

我最近在我公司对不同的数据结构运行了一个基准测试，所以我觉得我需要一个字。

基准测试

在网络上我们很少找到（如果有的话）一个精心设计的基准。直到今天，我只找到了记者方式的基准（在地毯下快速扫过数十个变量）。

1）你需要考虑缓存升温

大多数运行基准测试的人都害怕定时器差异，因此他们运行他们的东西数千次，并且花了一整个时间，他们只是小心对于每个操作，都要花上一千次，然后考虑可比较。

事实是，在现实世界中没有任何意义，因为你的缓存不会是温暖的，您的操作可能会被调用一次。因此，您需要使用RDTSC进行基准测试，而只需调用一次即可。
英特尔已经写了一篇论文的性能进行测试，我测量的性能差距超过一些 std :: unordered_map 用例（
这使我想要向读者警告上述结果（他们被做了在Win7上）;所以你的里程可能有所不同。

最好的问候

It is common knowledge in programming that memory locality improves performance a lot due to cache hits. I recently found out about boost::flat_map which is a vector based implementation of a map. It doesn't seem to be nearly as popular as your typical map/unordered_map so I haven't been able to find any performance comparisons. How does it compare and what are the best use cases for it?

Thanks!

解决方案

I have run a benchmark on different data structures very recently at my company so I feel I need to drop a word. It is very complicated to benchmark something correctly.

Benchmarking

On the web we rarely find (if ever) a well engineered benchmark. Until today I only found benchmarks that were done the journalist way (pretty quickly and sweeping dozens of variables under the carpet).

1) You need to consider about cache warming

Most people running benchmarks are afraid of timer discrepancy, therefore they run their stuff thousands of times and take the whole time, they just are careful to take the same thousand of times for every operation, and then consider that comparable.

The truth is, in real world it makes little sense, because your cache will not be warm, and your operation will likely be called just once. Therefore you need to benchmark using RDTSC, and time stuff calling them once only. Intel has made a paper describing how to use RDTSC (using a cpuid instruction to flush the pipeline, and calling it at least 3 times at the beginning of the program to stabilize it).

2) RDTSC accuracy measure

I also recommend doing this:

u64 g_correctionFactor;  // number of clocks to offset after each measurement to remove the overhead of the measurer itself.
u64 g_accuracy;

static u64 const errormeasure = ~((u64)0);

#ifdef _MSC_VER
#pragma intrinsic(__rdtsc)
inline u64 GetRDTSC()
{
    int a[4];
    __cpuid(a, 0x80000000);  // flush OOO instruction pipeline
    return __rdtsc();
}

inline void WarmupRDTSC()
{
    int a[4];
    __cpuid(a, 0x80000000);  // warmup cpuid.
    __cpuid(a, 0x80000000);
    __cpuid(a, 0x80000000);

    // measure the measurer overhead with the measurer (crazy he..)
    u64 minDiff = LLONG_MAX;
    u64 maxDiff = 0;   // this is going to help calculate our PRECISION ERROR MARGIN
    for (int i = 0; i < 80; ++i)
    {
        u64 tick1 = GetRDTSC();
        u64 tick2 = GetRDTSC();
        minDiff = Aska::Min(minDiff, tick2 - tick1);   // make many takes, take the smallest that ever come.
        maxDiff = Aska::Max(maxDiff, tick2 - tick1);
    }
    g_correctionFactor = minDiff;

    printf("Correction factor %llu clocks\n", g_correctionFactor);

    g_accuracy = maxDiff - minDiff;
    printf("Measurement Accuracy (in clocks) : %llu\n", g_accuracy);
}
#endif

This is a discrepancy measurer, and it will take the minimum of all measured values, to avoid to get a -10**18 (64 bits first negatives values) from time to time.

Notice the use of intrinsics and not inline assembly. First inline assembly is rarely supported by compilers nowadays, but much worse of all, the compiler creates a full ordering barrier around inline assembly because it cannot static analyze the inside, so this is a problem to benchmark real world stuff, especially when calling stuff just once. So an intrinsic is suited here, because it doesn't break the compiler free-re-ordering of instructions.

3) parameters

The last problem is people usually test for too few variations of the scenario. A container performance is affected by:

1. Allocator
2. size of contained type
3. cost of implementation of copy operation, assignment operation, move operation, construction operation, of the contained type.
4. number of elements in the container (size of the problem)
5. type has trivial 3.-operations
6. type is POD

Point 1 is important because containers do allocate from time to time, and it matters a lot if they allocate using the CRT "new" or some user defined operation, like pool allocation or freelist or other...

(for people interested about pt 1, join the mystery thread on gamedev about system allocator performance impact)

Point 2 is because some containers (say A) will loose time copying stuff around, and the bigger the type the bigger the overhead. The problem is that when comparing to another container B, A may win over B for small types, and loose for larger types.

Point 3 is the same than point 2, except it multiplies the cost by some weighting factor.

Point 4 is a question of big O mixed with cache issues. Some bad complexities containers can largely outperform low complexity containers for small number of types (like map vs. vector, because their cache locality is good, but map fragments the memory). And then at some crossing point, they will lose, because the contained overall size starts to "leak" to main memory and cause cache misses, that plus the fact that the asymptotic complexity can start to be felt.

Point 5 is about compilers being able to elude stuff that are empty or trivial at compile time. This can optimize greatly some operations, because the containers are template, therefore each type will have its own performance profile.

Point 6 same as point 5, PODS can benefit from the fact that copy construction is just a memcpy, and some containers can have a specific implementation for these cases, using partial template specializations, or SFINAE to select algorithms according to traits of T.

About the flat map

Apparently the flat map is a sorted vector wrapper, like Loki AssocVector, but with some supplementary modernizations coming with C++11, exploiting move semantics to accelerate insert and delete of single elements.

This is still an ordered container. Most people usually don't need the ordering part, therefore the existence of unordered...

Have you considered that maybe you need a flat_unorderedmap? which would be something like google::sparse_map or something like that - an open address hash map.

The problem of open address hash maps, is that at the time of rehash they have to copy all around to the new extended flat land. When a standard unordered map just have to recreate the hash index, but the allocated data stays where it is. The disadvantage of course is that the memory is fragmented like hell.

The criterion of a rehash in an open address hash map is when the capacity overpasses the size of the bucket vector multiplied by the load factor.

A typical load factor is 0.8; therefore, you need to care about that, if you can pre-size your hash map before filling it, always pre-size to: intended_filling * (1/0.8) + epsilon this will give you a guarantee of never having to spuriously rehash and recopy everything during filling.

The advantage of closed address maps (std::unordered..) is that you don't have to care about those parameters.

But the boost::flat_map is an ordered vector; therefore, it will always have a log(N) asymptotic complexity, which is less good than the open address hash map (amortized constant time). You should consider that as well.

Benchmark results

This is a test involving different maps (with int key and __int64/somestruct as value) and std::vector.

tested types information:

typeid=__int64 .  sizeof=8 . ispod=yes
typeid=struct MediumTypePod .  sizeof=184 . ispod=yes

Insertion

EDIT:

My previous results included a bug: they actually tested ordered insertion, which exhibited a very fast behavior for the flat maps.
I left those results later down this page because they are interesting.
This is the correct test:

I have checked the implementation, there is no such thing as a deferred sort implemented in the flat maps here. Each insertion sorts on the fly, therefore this benchmark exhibits the asymptotic tendencies:

map : N * log(N)
hashmaps : amortized N
vector and flatmaps : N * N

Warning: hereafter the test for std::map and both flat_maps is buggy and actually tests ordered insertion:

We can see that ordered insertion, results in back pushing, and is extremely fast. However, from non-charted results of my benchmark, I can also say that this is not near the absolute optimality for a back-insertion. Perfect back-insertion optimality is obtained on a pre-reserved vector. Which gives us 3Million cycles; we observe 4.8M here for boost (therefore 160% of the optimal).

Random search of 3 elements (clocks renormalized to 1)

in size = 100

in size = 10000

Iteration

over size 100 (only MediumPod type)

over size 10000 (only MediumPod type)

Final grain of salt

In the end I wanted to come back on "Benchmarking §3 Pt1" (the system allocator). In a recent experiment I am doing around the performance of an open address hash map I develop, I measured a performance gap of more than 3000% between Windows 7 and Windows 8 on some std::unordered_map use cases (discussed here).
Which makes me want to warn the reader about the above results (they were made on Win7); so, your mileage may vary.

best regards

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