线程没有提高代码性能

Question

I am trying to convert a basic long loop into thread to improve the loop performance.

Here is the threaded version:

#include <iostream>
#include <thread>
#include <chrono>
using namespace std;
using namespace std::chrono;

void funcSum(long long int start, long long int end, long long int *sum)
{
    for(auto i = start; i <= end; ++i)
    {
        *sum += i;
    }
}

int main()
{
    long long int start = 10, end = 1900000000;
    long long int sum = 0;
    auto startTime = high_resolution_clock::now();
    thread t1(funcSum, start, end / 2, &sum);
    thread t2(funcSum, end / 2 + 1 , end, &sum);
    t1.join();
    t2.join();
    auto stopTime = high_resolution_clock::now();
    auto duration = duration_cast<seconds>(stopTime - startTime);
    cout << "Sum: " << sum << endl;
    cout << duration.count() << " Seconds";
    return 0;
}

And here is the normal code (Without threads):

#include <iostream>
#include <thread>
#include <chrono>
using namespace std;
using namespace std::chrono;

void funcSum(long long int start, long long int end, long long int *sum)
{
    for(auto i = start; i <= end; ++i)
    {
        *sum += i;
    }
}

int main()
{
    long long int start = 10, end = 1900000000;
    long long int sum = 0;
    auto startTime = high_resolution_clock::now();
    funcSum(start, end, &sum);
    auto stopTime = high_resolution_clock::now();
    auto duration = duration_cast<seconds>(stopTime - startTime);
    cout << "Sum: " << sum << endl;
    cout << duration.count() << " Seconds";
    return 0;
}

Sum: 1805000000949999955 5 Seconds Process finished with exit code 0

In both the cases, time spent is 5 seconds.

Why the first threaded version does not improve the performance? How do I decrease the time using threads for this sum of range?

Answer 1

Fixed version of threaded code:

// Compute the sum of start ... end
class Summer {
public:
    long long int start;
    long long int end;
    long long int sum = 0;

    Summer(long long int aStart, long long int aEnd)
        : start(aStart),
        end(aEnd)
    {
    }

    void funcSum()
    {
        sum = 0;
        for (auto i = start; i <= end; ++i)
        {
            sum += i;
        }
    }
};

class SummerFunctor {
    Summer& mSummer;
public:
    SummerFunctor(Summer& aSummer)
    : mSummer(aSummer)
    {
    }

    void operator()()
    {
        mSummer.funcSum();
    }
};

// Version with n thread objects reports 
// 1 threads, sum = 1805000000949999955, 1587 ms
// 2 threads, sum = 1805000000949999955, 2547 ms
// 4 threads, sum = 1805000000949999955, 1251 ms
// 6 threads, sum = 1805000000949999955, 916 ms
int main()
{
    long long int start = 10, end = 1900000000;
    long long int sum = 0;
    auto startTime = high_resolution_clock::now();
    const size_t threadCount = 6;

    if (threadCount < 2) {
        funcSum(start, end, &sum);
    } else {
        Summer* summers[threadCount];
        std::thread* threads[threadCount];

        // Start threads
        auto val = start;
        auto partitionSize = (end-start) / threadCount;
        for (size_t i = 0; i < threadCount; ++i) {
            auto partitionEnd = std::min(start + partitionSize, end);
            summers[i] = new Summer(start, partitionEnd);
            start = partitionEnd + 1;
            SummerFunctor functor (*summers[i]);
            threads[i] = new std::thread(functor);
        }

        // Join threads
        for (size_t i = 0; i < threadCount; ++i) {
            threads[i]->join();
            sum += summers[i]->sum;
            delete threads[i];
            delete summers[i];
        }
    }

    auto stopTime = high_resolution_clock::now();
    auto duration = duration_cast<milliseconds>(stopTime - startTime);
    cout << threadCount << " threads, sum = " << sum << ", " << duration.count() << " ms" << std::endl;
    return 0;
}

I had to wrap the Summer object with a functor because std::thread insists on making a copy of a functor handed to it, that we can't access later. The execution gets better when more threads are used (running times see comments). Possible reasons for this:

1. The CPU has to synchronize access to the memory pages even though the threads use separate variables here because the variables likely lie in the same page
2. If there is only one thread running on a CPU, that thread may run at higher CPU frequency, but several threads may run only at normal CPU frequency
3. CPU cores often share arithmetic units
4. Without threads, the compiler can make optimizations that are not possible with threads. In theory, the compiler could unroll the loop and directly print the result.