我写了一个程序来计算2乘2随机矩阵的特征值。我生成了50,000个2x2随机矩阵并计算了它们的特征值。
使用boost,我在getEigVal()
的成员函数myClass
中使用了多线程,但我发现CPU利用率只有35%。
如何通过多线程加快getEigVal()
的过程?
#define _USE_MATH_DEFINES
#define _USE_MATH_DEFINES
#define BOOST_THREAD_PROVIDES_FUTURE
#include <boost/thread.hpp>
#include <boost/thread/future.hpp>
#include <vector>
#include <cmath>
#include <random>
#include <complex>
#include <chrono>
using namespace std;
using namespace std::chrono;
class myClass {
private:
int numOfRun;
double var;
vector <vector<complex<double>>> eigVal;
vector<complex<double>> quad_root(double a, double b, double c) {//quadratic formula
vector<complex<double>> root(2, complex<double>(0, 0));
complex<double> delta = sqrt(complex<double>(pow(b, 2) - 4 * a*c, 0));
root[0] = (-b + delta) / 2.0 / a;
root[1] = (-b - delta) / 2.0 / a;
return root;
}
vector<complex<double>> eig(vector<vector<double>> A) {//compute eigenvalues
double a = 1.0;
double b = -A[0][0] - A[1][1];
double c = A[0][0] * A[1][1] - A[0][1] * A[1][0];
vector<complex<double>> r = quad_root(a, b, c);
return r;
}
public:
myClass(int run = 5e4, double v = 1) :
numOfRun(run), var(v), eigVal(numOfRun, vector<complex<double>>(2)){
}
vector <vector<complex<double>>> getEigVal() {
random_device rd;
mt19937 e2(rd());
normal_distribution<> a(0.0, var);
vector <vector<double>> A(2, vector<double>(2));
for (int i = 0; i < numOfRun; i++) {
A = { { a(e2), a(e2) }, { a(e2), a(e2) } };//generate a 2x2 random matrix
boost::packaged_task<vector<complex<double>>> task{ bind(&myClass::eig, this, A) };
boost::future<vector<complex<double>>> f = task.get_future();
boost::thread t{ std::move(task) };
eigVal[i] = f.get();
}
return eigVal;
}
};
int main() {
myClass Test;
auto start = steady_clock::now();
vector <vector<complex<double>>> result = Test.getEigVal();
auto end = steady_clock::now();
cout << "Time elapsed: " << (duration_cast<milliseconds>(end - start).count())/1e3 << " seconds\n";//13.826 s
return 0;
}