Learn how to effectively parallelize workloads in Python to enhance performance and efficiency, especially in security-related applications.
Python, parallelization, workloads, security, multiprocessing, threading, performance, efficiency
import multiprocessing
def worker_function(data):
# Simulate a workload
print(f"Processing {data}")
if __name__ == "__main__":
data_list = ['task1', 'task2', 'task3', 'task4']
# Create a pool of worker processes
with multiprocessing.Pool(processes=4) as pool:
pool.map(worker_function, data_list)
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