Learn how to effectively slice sets in Python across multiple processes. This guide covers how to use Python's multiprocessing module to split sets for parallel processing, enhancing performance for large datasets.
Python, Slicing Sets, Multiprocessing, Parallel Processing, Performance Optimization
# Example Python code to slice sets across multiple processes
import multiprocessing
def worker(sliced_set):
# Process the sliced set
return {x * 2 for x in sliced_set}
if __name__ == "__main__":
# Original set
original_set = set(range(100))
# Determine the number of processes
num_processes = multiprocessing.cpu_count()
# Split the set into slices
set_slices = [set(list(original_set)[i::num_processes]) for i in range(num_processes)]
# Create a pool of workers
with multiprocessing.Pool(processes=num_processes) as pool:
results = pool.map(worker, set_slices)
# Combine results from all processes
combined_results = set.union(*results)
print(combined_results)
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