title: Python multiprocessing: how to limit the number of waiting processes?
body: <p>When running a large number of tasks (with large parameters) using Pool.apply_async, the processes are allocated and go to a waiting state, and there is no limit for the number of waiting processes. This can end up by eating all memory, as in the example below:</p>
  
  <pre><code>import multiprocessing
  import numpy as np
  
  def f(a,b):
      return np.linalg.solve(a,b)
  
  def test():
  
      p = multiprocessing.Pool()
      for _ in range(1000):
          p.apply_async(f, (np.random.rand(1000,1000),np.random.rand(1000)))
      p.close()
      p.join()
  
  if __name__ == '__main__':
      test()
  </code></pre>
  
  <p>I'm searching for a way to limit the waiting queue, in such a way that there is only a limited number of waiting processes, and Pool.apply_async is blocked while the waiting queue is full.</p>