Python
Multiprocessing
Achieve true parallelism with multiprocessing.
By EZ4Code Team
multiprocessingmulti-process
Code
from multiprocessing import Pool, cpu_count
def heavy_task(n):
return sum(i * i for i in range(n))
if __name__ == "__main__":
with Pool(processes=cpu_count()) as pool:
results = pool.map(heavy_task, [10**6, 10**6, 10**6])
print(results)
# Inter-process communication
from multiprocessing import Queue
q = Queue()
q.put("hello")
print(q.get())Explanation
Multiprocessing bypasses the GIL limitation, suitable for CPU-intensive tasks.
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