The following works. First it is a good idea to protect the main part of your code inside a main block in order to avoid weird side effects. The result of pool.map() is a list containing the evaluations for each value in the iterator list_start_vals, such that you don’t have to create array_2D before. import … Read more
Why does s1 and s2 point to the same object, whereas s1 and s3 doesn’t? (There is no usage of new keyword). Because the concatenation happens at compile time, and the completed string therefore goes in the constant pool the same as in the first example. It’s a special case “known” to the compiler. It’s … Read more
You cannot directly access the String intern pool. As per Javadocs String intern pool is: A pool of strings, initially empty, is maintained privately by the class String. However String objects can be added to this pool using String’s intern() method. java.lang.String.intern() returns an interned String, that is, one that has an entry in the … Read more
Note that I’m using pathos.multiprocessing instead of multiprocessing. It’s just a fork of multiprocessing that enables you to do map functions with multiple inputs, has much better serialization, and allows you to execute map calls anywhere (not just in __main__). You could use multiprocessing to do the below as well, however the code would be … Read more
Multiprocessing pools implement a queue for you. Just use a pool method that returns the worker return value to the caller. imap works well: import multiprocessing import re def mp_worker(filename): with open(filename) as f: text = f.read() m = re.findall(“x+”, text) count = len(max(m, key=len)) return filename, count def mp_handler(): p = multiprocessing.Pool(32) with open(‘infilenamess.txt’) … Read more
Use a function object: class Copier(object): def __init__(self, tgtdir): self.target_dir = tgtdir def __call__(self, src): copy_file(src, self.target_dir) To run your Pool.map: p.map(Copier(target_dir), file_list)
You can use callbacks from Pool.apply_async. Something like this should do the job for you. from multiprocessing import Pool def part_crack_helper(args): solution = do_job(args) if solution: return True else: return False class Worker(): def __init__(self, workers, initializer, initargs): self.pool = Pool(processes=workers, initializer=initializer, initargs=initargs) def callback(self, result): if result: print(“Solution found! Yay!”) self.pool.terminate() def do_job(self): for … Read more
You can use functools.partial for this (as you suspected): from functools import partial def target(lock, iterable_item): for item in iterable_item: # Do cool stuff if (… some condition here …): lock.acquire() # Write to stdout or logfile, etc. lock.release() def main(): iterable = [1, 2, 3, 4, 5] pool = multiprocessing.Pool() l = multiprocessing.Lock() func … Read more
The following code seems to work: import multiprocessing, time def task(args): count = args[0] queue = args[1] for i in xrange(count): queue.put(“%d mississippi” % i) return “Done” def main(): manager = multiprocessing.Manager() q = manager.Queue() pool = multiprocessing.Pool() result = pool.map_async(task, [(x, q) for x in range(10)]) time.sleep(1) while not q.empty(): print q.get() print result.get() … Read more
These problems usually boil down to the following: The function you are trying to parallelize doesn’t require enough CPU resources (i.e. CPU time) to rationalize parallelization! Sure, when you parallelize with multiprocessing.Pool(8), you theoretically (but not practically) could get a 8x speed up. However, keep in mind that this isn’t free – you gain this … Read more