您的位置：首页 > 其它

boost::flat_map性能测试

2015-06-30 10:11 1266 查看

文章转自：boost::flat_map and its performance compared to map and unordered_map

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:

C++

u64 g_correctionFactor; // number of clocks to offset after each measurment 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

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39

u64 g_correctionFactor; // number of clocks to offset after each measurment 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:

Allocator

size of contained type

cost of implementation of copy operation, assignment operation, move operation, construction operation, of the contained type.

number of elements in the container (size of the problem)

type has trivial 3.-operations

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…

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 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 asymptoptic complexity can start to be felt.

Point 5 is about compilers being able to ellude 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 to 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 presize 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) asymptoptic 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:

C++

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

1
2

typeid=__int64 . sizeof=8 . ispod=yes

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

Insertion

EDIT:

Ok, because 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 thereunder 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_map

s here is buggy and actually testsordered 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 optimiality of back-insertion in a vector. Which is 3Million cycles, we observe 4.8M here for boost (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)

best regards

内容来自用户分享和网络整理，不保证内容的准确性，如有侵权内容，可联系管理员处理

标签： boost flat_map 性能

相关文章推荐

新的分享

章节导航