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GPU

As explained in Starting Data Analytics with Generative AI and Python

A GPU is a massively parallel processor originally designed for graphics that is also used to accelerate general-purpose numerical and machine-learning workloads.

A GPU is parallel hardware that can run many numerical calculations concurrently. It offers another route for scaling code after optimizing a single-processor implementation, but actual gains require measurement. section 7.2.2

GPU-accelerated bootstrap confidence intervals

A bootstrap confidence-interval function can move arrays and random sampling to a GPU with CuPy, then return the calculated bounds to ordinary Python. Execution-time comparisons show whether GPU processing beats the optimized baseline as the DataFrame grows.

How Starting Data Analytics with Generative AI and Python covers it

How deep it goes
Demonstrates a CuPy implementation and execution-time comparison, without detailing GPU architecture or kernel internals.
Examples
Uses optimized and GPU-utilizing bootstrap functions, CuPy installation, and execution-time figures across different DataFrame sizes.
What it assumes
Assumes an already optimized numerical function and familiarity with Python arrays, while demonstrating GPU adaptation through CuPy.
What you can do after
Apply it via CuPy to move bootstrap sampling and statistics onto GPU-backed arrays, then compare execution time with an optimized baseline.
How it connects
Connects GPUs with parallelization, CPU cores, Python numerical arrays, CuPy, CUDA, and bootstrap confidence intervals.
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This explainer was generated by Manning's knowledge graph from the text of Starting Data Analytics with Generative AI and Python. The section links point to the parts of the book it draws on.