Using C++11's random module, I encountered an odd performance drop when using std::mt19937 (32 and 64bit versions) in combination with a uniform_real_distribution (float or double, doesn't matter). Compared to a g++ compile, it's more than an order of magnitude slower!
The culprit isn't just the mt generator, as it's fast with a uniform_int_distribution. And it isn't a general flaw in the uniform_real_distribution since that's fast with other generators like default_random_engine. Just that specific combination is oddly slow.
I'm not very familiar with the intrinsics, but the Mersenne Twister algorithm is more or less strictly defined, so a difference in implementation couldn't account for this difference I guess? measure Program is following, but here are my results for clang 3.4 and gcc 4.8.1 on a 64bit linux machine:
gcc 4.8.1
runtime_int_default: 185.6
runtime_int_mt: 179.198
runtime_int_mt_64: 175.195
runtime_float_default: 45.375
runtime_float_mt: 58.144
runtime_float_mt_64: 94.188
clang 3.4
runtime_int_default: 215.096
runtime_int_mt: 201.064
runtime_int_mt_64: 199.836
runtime_float_default: 55.143
runtime_float_mt: 744.072 <--- this and
runtime_float_mt_64: 783.293 <- this is slow
Program to generate this and try out yourself:
#include <iostream>
#include <vector>
#include <chrono>
#include <random>
template< typename T_rng, typename T_dist>
double time_rngs(T_rng& rng, T_dist& dist, int n){
std::vector< typename T_dist::result_type > vec(n, 0);
auto t1 = std::chrono::high_resolution_clock::now();
for (int i = 0; i < n; ++i)
vec[i] = dist(rng);
auto t2 = std::chrono::high_resolution_clock::now();
auto runtime = std::chrono::duration_cast<std::chrono::microseconds>(t2-t1).count()/1000.0;
auto sum = vec[0]; //access to avoid compiler skipping
return runtime;
}
int main(){
const int n = 10000000;
unsigned seed = std::chrono::system_clock::now().time_since_epoch().count();
std::default_random_engine rng_default(seed);
std::mt19937 rng_mt (seed);
std::mt19937_64 rng_mt_64 (seed);
std::uniform_int_distribution<int> dist_int(0,1000);
std::uniform_real_distribution<float> dist_float(0.0, 1.0);
// print max values
std::cout << "rng_default_random.max(): " << rng_default.max() << std::endl;
std::cout << "rng_mt.max(): " << rng_mt.max() << std::endl;
std::cout << "rng_mt_64.max(): " << rng_mt_64.max() << std::endl << std::endl;
std::cout << "runtime_int_default: " << time_rngs(rng_default, dist_int, n) << std::endl;
std::cout << "runtime_int_mt: " << time_rngs(rng_mt_64, dist_int, n) << std::endl;
std::cout << "runtime_int_mt_64: " << time_rngs(rng_mt_64, dist_int, n) << std::endl;
std::cout << "runtime_float_default: " << time_rngs(rng_default, dist_float, n) << std::endl;
std::cout << "runtime_float_mt: " << time_rngs(rng_mt, dist_float, n) << std::endl;
std::cout << "runtime_float_mt_64: " << time_rngs(rng_mt_64, dist_float, n) << std::endl;
}
compile via clang++ -O3 -std=c++11 random.cpp or g++ respectively. Any ideas?
edit: Finally, Matthieu M. had a great idea: The culprit is inlining, or rather a lack thereof. Increasing the clang inlining limit eliminated the performance penalty. That actually solved a number of performance oddities I encountered. Thanks, I learned something new.
