如何在循环中使用std :: condition_variable

Question

I'm trying to implement some algorithm using threads that must be synchronized at some moment. More or less the sequence for each thread should be:

1. Try to find a solution with current settings.
2. Synchronize solution with other threads.
3. If any of the threads found solution end work.
4. (empty - to be inline with example below)
5. Modify parameters for algorithm and jump to 1.

Here is a toy example with algorithm changed to just random number generation - all threads should end if at least one of them will find 0.

#include <iostream>
#include <condition_variable>
#include <thread>
#include <vector>

const int numOfThreads = 8;

std::condition_variable cv1, cv2;
std::mutex m1, m2;
int lockCnt1 = 0;
int lockCnt2 = 0;

int solutionCnt = 0;

void workerThread()
{
    while(true) {
        // 1. do some important work
        int r = rand() % 1000;

        // 2. synchronize and get results from all threads
        {
            std::unique_lock<std::mutex> l1(m1);
            ++lockCnt1;
            if (r == 0) ++solutionCnt; // gather solutions
            if (lockCnt1 == numOfThreads) {
                // last thread ends here
                lockCnt2 = 0;
                cv1.notify_all();
            }
            else {
                cv1.wait(l1, [&] { return lockCnt1 == numOfThreads; });
            }
        }

        // 3. if solution found then quit all threads
        if (solutionCnt > 0) return;

        // 4. if not, then set lockCnt1 to 0 to have section 2. working again
        {
            std::unique_lock<std::mutex> l2(m2);
            ++lockCnt2;
            if (lockCnt2 == numOfThreads) {
                // last thread ends here
                lockCnt1 = 0;
                cv2.notify_all();
            }
            else {
                cv2.wait(l2, [&] { return lockCnt2 == numOfThreads; });
            }
        }

        // 5. Setup new algorithm parameters and repeat.
    }
}

int main()
{
    srand(time(NULL));

    std::vector<std::thread> v;
    for (int i = 0; i < numOfThreads ; ++i) v.emplace_back(std::thread(workerThread));
    for (int i = 0; i < numOfThreads ; ++i) v[i].join();

    return 0;
}

The questions I have are about sections 2. and 4. from code above.

A) In a section 2 there is synchronization of all threads and gathering solutions (if found). All is done using lockCnt1 variable. Comparing to single use of condition_variable I found it hard how to set lockCnt1 to zero safely, to be able to reuse this section (2.) next time. Because of that I introduced section 4. Is there better way to do that (without introducing section 4.)?

B) It seems that all examples shows using condition_variable rather in context of 'producer-consumer' scenario. Is there better way to synchronization all threads in case where all are 'producers'?

Edit: Just to be clear, I didn't want to describe algorithm details since this is not important here - anyway this is necessary to have all solution(s) or none from given loop execution and mixing them is not allowed. Described sequence of execution must be followed and the question is how to have such synchronization between threads.

Answer 1

A) You could just not reset the lockCnt1 to 0, just keep incrementing it further. The condition lockCnt2 == numOfThreads then changes to lockCnt2 % numOfThreads == 0 . You can then drop the block #4. In future you could also use std::experimental::barrier to get the threads to meet.

B) I would suggest using std::atomic for solutionCnt and then you can drop all other counters, the mutex and the condition variable. Just atomically increase it by one in the thread that found solution and then return. In all threads after every iteration check if the value is bigger than zero. If it is, then return. The advantage is that the threads do not have to meet regularly, but can try to solve it at their own pace.

Answer 2

Out of curiosity, I tried to solve your problem using std::async . For every attempt to find a solution, we call async . Once all parallel attempts have finished, we process feedback, adjust parameters, and repeat. An important difference with your implementation is that feedback is processed in the calling (main) thread. If processing feedback takes too long — or if we don't want to block the main thread at all — then the code in main() can be adjusted to also call std::async .

The code is supposed to be quite efficient, provided that the implementation of async uses a thread pool (eg Microsoft's implementation does that).

#include <chrono>
#include <future>
#include <iostream>
#include <vector>

const int numOfThreads = 8;

struct Parameters{};
struct Feedback {
    int result;
};

Feedback doTheWork(const Parameters &){
    // do the work and provide result and feedback for future runs
    return Feedback{rand() % 1000};
}

bool isSolution(const Feedback &f){
    return f.result == 0;
}

// Runs doTheWork in parallel. Number of parallel tasks is same as size of params vector
std::vector<Feedback> findSolutions(const std::vector<Parameters> &params){

    // 1. Run async tasks to find solutions. Normally threads are not created each time but re-used from a pool
    std::vector<std::future<Feedback>> futures;
    for (auto &p: params){
        futures.push_back(std::async(std::launch::async,
                                    [&p](){ return doTheWork(p); }));
    }

    // 2. Syncrhonize: wait for all tasks
    std::vector<Feedback> feedback(futures.size());
    for (auto nofRunning = futures.size(), iFuture = size_t{0}; nofRunning > 0; ){

        // Check if the task has finished (future is invalid if we already handled it during an earlier iteration)
        auto &future = futures[iFuture];
        if (future.valid() && future.wait_for(std::chrono::milliseconds(1)) != std::future_status::timeout){
            // Collect feedback for next attempt
            // Alternatively, we could already check if solution has been found and cancel other tasks [if our algorithm supports cancellation]
            feedback[iFuture] = std::move(future.get());
            --nofRunning;
        }

        if (++iFuture == futures.size())
            iFuture = 0;
    }
    return feedback;
}

int main()
{
    srand(time(NULL));

    std::vector<Parameters> params(numOfThreads);
    // 0. Set inital parameter values here

    // If we don't want to block the main thread while the algorithm is running, we can use std::async here too
    while (true){
        auto feedbackVector = findSolutions(params);
        auto itSolution = std::find_if(std::begin(feedbackVector), std::end(feedbackVector), isSolution);

        // 3. If any of the threads has found a solution, we stop
        if (itSolution != feedbackVector.end())
            break;

        // 5. Use feedback to re-configure parameters for next iteration
    }

    return 0;
}