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Embedded Programming with Modern C++

- Exercise

Explore how to parallelize scalar product calculations with Modern C++ by launching asynchronous functions and using standard library tools like std::inner_product. Understand performance comparisons between single and multithreaded programs and how to optimize core utilization during multithreading tasks.

We'll cover the following...

Task 1

The calculation of the scalar product in the program below is quite easy to parallelize. Take a look at the explanation below to better understand the code.

Launch four asynchronous functions to calculate the scalar product.

C++
// dotProduct.cpp
#include <chrono>
#include <iostream>
#include <numeric>
#include <random>
#include <vector>
static const int NUM= 100000000;
long long getDotProduct(std::vector<int>& v, std::vector<int>& w){
  return std::inner_product(v.begin(), v.end(), 
                             w.begin(), 
                             0LL);
}
int main(){
  std::cout << std::endl;
  // get NUM random numbers from 0 .. 100
  std::random_device seed;
  // generator
  std::mt19937 engine(seed());
  // distribution
  std::uniform_int_distribution<int> dist(0, 100);
  // fill the vectors
  std::vector<int> v,  w;
  v.reserve(NUM);
  w.reserve(NUM);
  for (int i=0; i< NUM; ++i){
    v.push_back(dist(engine));
    w.push_back(dist(engine));
  }
  // measure the execution time
  std::chrono::system_clock::time_point start = std::chrono::system_clock::now();
  std::cout << "getDotProduct(v, w): " << getDotProduct(v, w) << std::endl;
  std::chrono::duration<double> dur  = std::chrono::system_clock::now() - start;
  std::cout << "Sequential Execution: "<< dur.count() << std::endl;
  std::cout << std::endl;
}
...