Concurrency is often misunderstood as parallelism. Concurrency implies scheduling independent code to be executed in a systematic manner. This chapter focuses on the execution of concurrency for an operating system using Python.

The following program helps in the execution of concurrency for an operating system −

import os
import time
import threading
import multiprocessing

NUM_WORKERS = 4

def only_sleep():
   print("PID: %s, Process Name: %s, Thread Name: %s" % (
      os.getpid(),
      multiprocessing.current_process().name,
      threading.current_thread().name)
   )
   time.sleep(1)

def crunch_numbers():
   print("PID: %s, Process Name: %s, Thread Name: %s" % (
      os.getpid(),
      multiprocessing.current_process().name,
      threading.current_thread().name)
   )
   x = 0
   while x < 10000000:
      x += 1
for _ in range(NUM_WORKERS):
   only_sleep()
end_time = time.time()
print("Serial time=", end_time - start_time)

# Run tasks using threads
start_time = time.time()
threads = [threading.Thread(target=only_sleep) for _ in range(NUM_WORKERS)]
[thread.start() for thread in threads]
[thread.join() for thread in threads]
end_time = time.time()

print("Threads time=", end_time - start_time)

# Run tasks using processes
start_time = time.time()
processes = [multiprocessing.Process(target=only_sleep()) for _ in range(NUM_WORKERS)]
[process.start() for process in processes]
[process.join() for process in processes]
end_time = time.time()

print("Parallel time=", end_time - start_time)

Output

The above program generates the following output −

Explanation

“multiprocessing” is a package similar to the threading module. This package supports local and remote concurrency. Due to this module, programmers get the advantage to use multiple processes on the given system.