diff --git a/single_process_vs_multi_process/main.py b/single_process_vs_multi_process/main.py
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+++ b/single_process_vs_multi_process/main.py
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+from multiprocessing import Process
+from random import randint
+from time import time
+
+# This example compare the speed of CPU-bound operations
+# when using a sequential method and also when using a 
+# multi-process method with the 'multiprocessing' Python
+# package
+
+
+def calculatePrimeFactors(n):
+    primfac = []
+    d = 2
+    while d*d <= n:
+        while (n % d) == 0:
+            primfac.append(d)  # supposing you want multiple factors repeated
+            n //= d
+        d += 1
+        if n > 1:
+            primfac.append(n)
+    return primfac
+
+
+# Sequential Example
+
+
+def seq_crunch():
+    print("Starting sequential number crunching")
+    t0 = time()
+    for i in range(10000):
+        rand = randint(20000, 100000000)
+        calculatePrimeFactors(rand)
+    t1 = time()
+    return f'{t1-t0:.2f}'
+
+
+
+# Multi-processing Example
+
+
+def executeProc():
+ for i in range(1000):
+    rand = randint(20000, 100000000)
+    calculatePrimeFactors(rand)
+
+
+def multi_proc_crunch():
+    print("Starting multi-process number crunching")
+    t0 = time()
+    processes = []
+    # Here we create our processes and kick them off
+    for i in range(10):
+        proc = Process(target=executeProc, args=())
+        processes.append(proc)
+        proc.start()
+    # Again we use the .join() method in order to wait for
+    # execution to finish for all of our processes
+    for process in processes:
+        process.join()
+    t1 = time()
+    return f'{t1-t0:.2f}'
+
+if __name__ == '__main__':
+    seq_time = seq_crunch()
+    multi_proc_time = multi_proc_crunch()
+    print('\n==================================')
+    print(f'Time to crunch prime factors sequentially: {seq_time}s')
+    print(f'Time to crunch prime factors using multi-processing: {multi_proc_time}s')
+    print('==================================')
+