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Parallelization overhead

As explained in Starting Data Analytics with Generative AI and Python

Parallelization overhead is the extra computation and coordination cost incurred when dividing, scheduling, communicating, and recombining concurrent work.

Parallelization overhead is the extra cost of dividing, scheduling, coordinating, and recombining work; these costs can make a parallel function slower than its optimized single-threaded version. section 7.2.2

When parallel work loses

A bootstrap confidence-interval function was parallelized with ThreadPoolExecutor across 16 threads, yet execution became considerably slower. Its low 1.5% processor use made it a poor parallelization candidate. section 7.2.2

How Starting Data Analytics with Generative AI and Python covers it

How deep it goes
Demonstrates threaded implementation and timing evidence, but defers detailed overhead mechanics.
Examples
Compares optimized and parallelized bootstrap confidence intervals using execution-time figures and threaded Python code.
What it assumes
Assumes an optimized working function and distinguishes CPU-bound from I/O-bound work before changing its execution architecture.
What you can do after
Diagnose performance by measuring parallel and optimized implementations rather than assuming more threads are faster.
How it connects
Connects parallelization overhead with CPU-bound and I/O-bound tasks, the Python GIL, multicore processors, and GPUs.
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