Why is my C++ code so much slower than R? - c++

Question

I have written the following codes in R and C++ which perform the same algorithm:
a) To simulate the random variable X 500 times. (X has value 0.9 with prob 0.5 and 1.1 with prob 0.5)
b) Multiply these 500 simulated values together to get a value. Save that value in a container
c) Repeat 10000000 times such that the container has 10000000 values
R:
ptm <- proc.time()
steps <- 500
MCsize <- 10000000
a <- rbinom(MCsize,steps,0.5)
b <- rep(500,times=MCsize) - a
result <- rep(1.1,times=MCsize)^a*rep(0.9,times=MCsize)^b
proc.time()-ptm
C++
#include <numeric>
#include <vector>
#include <iostream>
#include <random>
#include <thread>
#include <mutex>
#include <cmath>
#include <algorithm>
#include <chrono>
const size_t MCsize = 10000000;
std::mutex mutex1;
std::mutex mutex2;
unsigned seed_;
std::vector<double> cache;
void generatereturns(size_t steps, int RUNS){
mutex2.lock();
// setting seed
try{
std::mt19937 tmpgenerator(seed_);
seed_ = tmpgenerator();
std::cout << "SEED : " << seed_ << std::endl;
}catch(int exception){
mutex2.unlock();
}
mutex2.unlock();
// Creating generator
std::binomial_distribution<int> distribution(steps,0.5);
std::mt19937 generator(seed_);
for(int i = 0; i!= RUNS; ++i){
double power;
double returns;
power = distribution(generator);
returns = pow(0.9,power) * pow(1.1,(double)steps - power);
std::lock_guard<std::mutex> guard(mutex1);
cache.push_back(returns);
}
}
int main(){
std::chrono::steady_clock::time_point start = std::chrono::steady_clock::now();
size_t steps = 500;
seed_ = 777;
unsigned concurentThreadsSupported = std::max(std::thread::hardware_concurrency(),(unsigned)1);
int remainder = MCsize % concurentThreadsSupported;
std::vector<std::thread> threads;
// starting sub-thread simulations
if(concurentThreadsSupported != 1){
for(int i = 0 ; i != concurentThreadsSupported - 1; ++i){
if(remainder != 0){
threads.push_back(std::thread(generatereturns,steps,MCsize / concurentThreadsSupported + 1));
remainder--;
}else{
threads.push_back(std::thread(generatereturns,steps,MCsize / concurentThreadsSupported));
}
}
}
//starting main thread simulation
if(remainder != 0){
generatereturns(steps, MCsize / concurentThreadsSupported + 1);
remainder--;
}else{
generatereturns(steps, MCsize / concurentThreadsSupported);
}
for (auto& th : threads) th.join();
std::chrono::steady_clock::time_point end = std::chrono::steady_clock::now() ;
typedef std::chrono::duration<int,std::milli> millisecs_t ;
millisecs_t duration( std::chrono::duration_cast<millisecs_t>(end-start) ) ;
std::cout << "Time elapsed : " << duration.count() << " milliseconds.\n" ;
return 0;
}
I can't understand why my R code is so much faster than my C++ code (3.29s vs 12s) even though I have used four threads in the C++ code? Can anyone enlighten me please? How should I improve my C++ code to make it run faster?
EDIT:
Thanks for all the advice! I reserved capacity for my vectors and reduced the amount of locking in my code. The crucial update in the generatereturns() function is :
std::vector<double> cache(MCsize);
std::vector<double>::iterator currit = cache.begin();
//.....
// Creating generator
std::binomial_distribution<int> distribution(steps,0.5);
std::mt19937 generator(seed_);
std::vector<double> tmpvec(RUNS);
for(int i = 0; i!= RUNS; ++i){
double power;
double returns;
power = distribution(generator);
returns = pow(0.9,power) * pow(1.1,(double)steps - power);
tmpvec[i] = returns;
}
std::lock_guard<std::mutex> guard(mutex1);
std::move(tmpvec.begin(),tmpvec.end(),currit);
currit += RUNS;
Instead of locking every time, I created a temporary vector and then used std::move to shift the elements in that tempvec into cache. Now the elapsed time has reduced to 1.9seconds.

Answer 1

First of all, are you running it in release mode?
Switching from debug to release reduced the running time from ~15s to ~4.5s on my laptop (windows 7, i5 3210M).
Also, reducing the number of threads to 2 instead of 4 in my case (I just have 2 cores but with hyperthreading) further reduced the running time to ~2.4s.
Changing the variable power to int (as jimifiki also suggested) also offered a slight boost, reducing the time to ~2.3s.

Answer 2

I really enjoyed your question and I tried the code at home. I tried to change the random number generator, my implementation of std::binomial_distribution requires on average about 9.6 calls of generator().
I know the question is more about comparing R with C++ performances, but since you ask "How should I improve my C++ code to make it run faster?" I insist with pow optimization. You can easily avoid one half of the call by precomputing either 0.9^steps or 1.1^steps before the for loop. This makes your code run a bit faster:
double power1 = pow(0.9,steps);
double ratio = 1.1/0.9;
for(int i = 0; i!= RUNS; ++i){
...
returns = myF1 * pow(myF2, (double)power);
Analogously you can improve the R code:
...
ratio <-1.1/0.9
pow1 = 0.9^steps
result <- rep(ratio,times=MCsize)^rep(pow1,times=MCsize)
...

Answer 3

Probably doesn't help you that much, but
start by using pow(double,int) when your exponent is an int.
int power;
returns = pow(0.9,power) * pow(1.1,(int)steps - power);
Can you see any improvement?