C++ – How to use a condition_variable to really wait_for no longer than a certain duration

c++c++11condition-variablemultithreading

As it turns out, condition_variable::wait_for should really be called condition_variable::wait_for_or_possibly_indefinitely_longer_than, because it needs to reacquire the lock before really timing out and returning.

See this program for a demonstration.

Is there a way to express, "Look, I really only have two seconds. If myPredicate() is still false at that time and/or the lock is still locked, I don't care, just carry on regardless and give me a way to detect that."

Something like:

bool myPredicate();
auto sec = std::chrono::seconds(1);

bool pred;
std::condition_variable::cv_status timedOut;

std::tie( pred, timedOut ) =
    cv.really_wait_for_no_longer_than( lck, 2*sec, myPredicate );

if( lck.owns_lock() ) {
    // Can use mutexed resource.
    // ...
    lck.unlock();
} else {
    // Cannot use mutexed resource. Deal with it.
};

Best Solution

I think that you misuse the condition_variable's lock. It's for protecting condition only, not for protecting a time-consuming work.

Your example can be fixed easily by splitting the mutex into two - one is for critical section, another is for protecting modifications of ready condition. Here is the modified fragment:

typedef std::unique_lock<std::mutex> lock_type;
auto sec = std::chrono::seconds(1);
std::mutex mtx_work;
std::mutex mtx_ready;
std::condition_variable cv;
bool ready = false;

void task1() {
    log("Starting task 1. Waiting on cv for 2 secs.");
    lock_type lck(mtx_ready);
    bool done = cv.wait_for(lck, 2*sec, []{log("Checking condition..."); return ready;});
    std::stringstream ss;
    ss << "Task 1 finished, done==" << (done?"true":"false") << ", " << (lck.owns_lock()?"lock owned":"lock not owned");
    log(ss.str());
}

void task2() {
    // Allow task1 to go first
    std::this_thread::sleep_for(1*sec);
    log("Starting task 2. Locking and sleeping 2 secs.");
    lock_type lck1(mtx_work);
    std::this_thread::sleep_for(2*sec);
    lock_type lck2(mtx_ready);
    ready = true; // This happens around 3s into the program
    log("OK, task 2 unlocking...");
    lck2.unlock();
    cv.notify_one();
}

It's output:

@2 ms: Starting task 1. Waiting on cv for 2 secs.
@2 ms: Checking condition...
@1002 ms: Starting task 2. Locking and sleeping 2 secs.
@2002 ms: Checking condition...
@2002 ms: Task 1 finished, done==false, lock owned
@3002 ms: OK, task 2 unlocking...

Related Solutions

Java – When and how should I use a ThreadLocal variable

One possible (and common) use is when you have some object that is not thread-safe, but you want to avoid synchronizing access to that object (I'm looking at you, SimpleDateFormat). Instead, give each thread its own instance of the object.

For example:

public class Foo
{
    // SimpleDateFormat is not thread-safe, so give one to each thread
    private static final ThreadLocal<SimpleDateFormat> formatter = new ThreadLocal<SimpleDateFormat>(){
        @Override
        protected SimpleDateFormat initialValue()
        {
            return new SimpleDateFormat("yyyyMMdd HHmm");
        }
    };

    public String formatIt(Date date)
    {
        return formatter.get().format(date);
    }
}

Documentation.

Python – How to use threading in Python

Since this question was asked in 2010, there has been real simplification in how to do simple multithreading with Python with map and pool.

The code below comes from an article/blog post that you should definitely check out (no affiliation) - Parallelism in one line: A Better Model for Day to Day Threading Tasks. I'll summarize below - it ends up being just a few lines of code:

from multiprocessing.dummy import Pool as ThreadPool
pool = ThreadPool(4)
results = pool.map(my_function, my_array)

Which is the multithreaded version of:

results = []
for item in my_array:
    results.append(my_function(item))

Description

Map is a cool little function, and the key to easily injecting parallelism into your Python code. For those unfamiliar, map is something lifted from functional languages like Lisp. It is a function which maps another function over a sequence.

Map handles the iteration over the sequence for us, applies the function, and stores all of the results in a handy list at the end.

Enter image description here

Implementation

Parallel versions of the map function are provided by two libraries:multiprocessing, and also its little known, but equally fantastic step child:multiprocessing.dummy.

multiprocessing.dummy is exactly the same as multiprocessing module, but uses threads instead (an important distinction - use multiple processes for CPU-intensive tasks; threads for (and during) I/O):

multiprocessing.dummy replicates the API of multiprocessing, but is no more than a wrapper around the threading module.

import urllib2
from multiprocessing.dummy import Pool as ThreadPool

urls = [
  'http://www.python.org',
  'http://www.python.org/about/',
  'http://www.onlamp.com/pub/a/python/2003/04/17/metaclasses.html',
  'http://www.python.org/doc/',
  'http://www.python.org/download/',
  'http://www.python.org/getit/',
  'http://www.python.org/community/',
  'https://wiki.python.org/moin/',
]

# Make the Pool of workers
pool = ThreadPool(4)

# Open the URLs in their own threads
# and return the results
results = pool.map(urllib2.urlopen, urls)

# Close the pool and wait for the work to finish
pool.close()
pool.join()

And the timing results:

Single thread:   14.4 seconds
       4 Pool:   3.1 seconds
       8 Pool:   1.4 seconds
      13 Pool:   1.3 seconds

Passing multiple arguments (works like this only in Python 3.3 and later):

To pass multiple arrays:

results = pool.starmap(function, zip(list_a, list_b))

Or to pass a constant and an array:

results = pool.starmap(function, zip(itertools.repeat(constant), list_a))

If you are using an earlier version of Python, you can pass multiple arguments via this workaround).

(Thanks to user136036 for the helpful comment.)

Best Solution

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