My recent efforts to implement a thread/ mutex manager ended up in an 75% CPU load (4 core), while all four running threads were either in sleep or waiting for a mutex beeing unlocked.
The specific class is far too large for being posted here entirely, but I could narrow down the cause to the deadlock-safe acquiring of two mutexes
std::unique_lock<std::mutex> lock1( mutex1, std::defer_lock );
std::unique_lock<std::mutex> lock2( mutex2, std::defer_lock );
std::lock( lock1, lock2 );
Another part of the class uses a std::condition_variable
with wait()
and notify_one()
on mutex1
for some code to be executed selectively at the same time.
The simple change to
std::unique_lock<std::mutex> lock1( mutex1 );
std::unique_lock<std::mutex> lock2( mutex2 );
brought the CPU usage down to normal 1-2%.
I Cant believe, the std::lock()
function is that inefficient. Could this be a bug in g++ 4.6.3?
edit: ( example )
#include <atomic>
#include <chrono>
#include <condition_variable>
#include <iostream>
#include <mutex>
#include <thread>
std::mutex mutex1, mutex2;
std::condition_variable cond_var;
bool cond = false;
std::atomic<bool>done{false};
using namespace std::chrono_literals;
void Take_Locks()
{
while( !done )
{
std::this_thread::sleep_for( 1s );
std::unique_lock<std::mutex> lock1( mutex1, std::defer_lock );
std::unique_lock<std::mutex> lock2( mutex2, std::defer_lock );
std::lock( lock1, lock2 );
std::this_thread::sleep_for( 1s );
lock1.unlock();
lock2.unlock();
}
}
void Conditional_Code()
{
std::unique_lock<std::mutex> lock1( mutex1, std::defer_lock );
std::unique_lock<std::mutex> lock2( mutex2, std::defer_lock );
std::lock( lock1, lock2 );
std::cout << "t4: waiting \n";
while( !cond )
cond_var.wait( lock1 );
std::cout << "t4: condition met \n";
}
int main()
{
std::thread t1( Take_Locks ), t2( Take_Locks ), t3( Take_Locks );
std::thread t4( Conditional_Code );
std::cout << "threads started \n";
std::this_thread::sleep_for( 10s );
std::unique_lock<std::mutex> lock1( mutex1 );
std::cout << "mutex1 locked \n" ;
std::this_thread::sleep_for( 5s );
std::cout << "setting condition/notify \n";
cond = true;
cond_var.notify_one();
std::this_thread::sleep_for( 5s );
lock1.unlock();
std::cout << "mutex1 unlocked \n";
std::this_thread::sleep_for( 6s );
done = true;
t4.join(); t3.join(); t2.join(); t1.join();
}
On my machine, the following code prints out 10 times a second and consumes almost 0 cpu because most of the time the thread is either sleeping or blocked on a locked mutex:
#include <chrono>
#include <thread>
#include <mutex>
#include <iostream>
using namespace std::chrono_literals;
std::mutex m1;
std::mutex m2;
void
f1()
{
while (true)
{
std::unique_lock<std::mutex> l1(m1, std::defer_lock);
std::unique_lock<std::mutex> l2(m2, std::defer_lock);
std::lock(l1, l2);
std::cout << "f1 has the two locks\n";
std::this_thread::sleep_for(100ms);
}
}
void
f2()
{
while (true)
{
std::unique_lock<std::mutex> l2(m2, std::defer_lock);
std::unique_lock<std::mutex> l1(m1, std::defer_lock);
std::lock(l2, l1);
std::cout << "f2 has the two locks\n";
std::this_thread::sleep_for(100ms);
}
}
int main()
{
std::thread t1(f1);
std::thread t2(f2);
t1.join();
t2.join();
}
Sample output:
f1 has the two locks
f2 has the two locks
f1 has the two locks
...
I'm running this on OS X and the Activity Monitor application says that this process is using 0.1% cpu. The machine is a Intel Core i5 (4 core).
I'm happy to adjust this experiment in any way to attempt to create live-lock or excessive cpu usage.
Update
If this program is using excessive CPU on your platform, try changing it to call ::lock()
instead, where that is defined with:
template <class L0, class L1>
void
lock(L0& l0, L1& l1)
{
while (true)
{
{
std::unique_lock<L0> u0(l0);
if (l1.try_lock())
{
u0.release();
break;
}
}
std::this_thread::yield();
{
std::unique_lock<L1> u1(l1);
if (l0.try_lock())
{
u1.release();
break;
}
}
std::this_thread::yield();
}
}
I would be interested to know if that made any difference for you, thanks.
Update 2
After a long delay, I have written a first draft of a paper on this subject. The paper compares 4 different ways of getting this job done. It contains software you can copy and paste into your own code and test yourself (and please report back what you find!):