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Ongoing Optimization: A Data Race with CppMem

But we can improve and further improve the acquire-release semantics of the last post. Why should x be atomic? There is no reason. That was my first but incorrect assumption. See why?

A typical misunderstanding in applying the acquire-release semantic is to assume that the acquire operation is waiting for the release operation. So based on this assumption, you may think that x has not be an atomic variable. So we can further optimize the program.

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// ongoingOptimizationAcquireReleaseBroken.cpp

#include <atomic>
#include <iostream>
#include <thread>

int x= 0;
std::atomic<int> y{0};

void writing(){  
  x= 2000;  
  y.store(11,std::memory_order_release);
}

void reading(){  
  std::cout << y.load(std::memory_order_acquire) << " ";  
  std::cout << x << std::endl;
}

int main(){
  std::thread thread1(writing);
  std::thread thread2(reading);
  thread1.join();
  thread2.join();
};

 

The program has a data race on x and has, therefore, undefined behavior. If y.store(11,std::memory_order_release) (line 12) is executed before  y.load(std::memory_order_acquire) (line 16), the acquire-release semantic guarantees that x= 2000 (line 11) is executed before the reading of x in line 17. But if not. In this case, the reading of x will be executed simultaneously as the writing of x. So we have concurrent access to a shared variable, one of them is a write. That’s, per definition, a data race. 

 

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    The table puts it in a nutshell.

    undefinedEng

     

    I made this mistake in my presentation “Multithreading done right?” in Berlin. In Moscow, I did it right. I never claimed that the C++ memory model is a piece of cake.

    Now it’s time for CppMem. Let’s see what CppMem finds out.

    CppMem

     

    int main(){
      int x= 0;
      atomic_int y= 0;
      {{{ { 
          x= 2000;
          y.store(11,memory_order_release);
          }
      ||| {
          y.load(memory_order_acquire);
          x;
          }
      }}}
    }
    

    The data race occurs if one thread writes x= 2000 and the other reads x. The graph shows a dr symbol (data race) on the arrow.

    raceAcquireRelease

    What’s next?

    The ultimate step in the process of ongoing optimization is still missing. In the next post, I will use the relaxed semantic.

     

     

     

     

     

     

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