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Title: GitHub - watson-cycle/python-performance: Repository for the book Fast Python - published by Manning

Open Graph Title: GitHub - watson-cycle/python-performance: Repository for the book Fast Python - published by Manning

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Description: Repository for the book Fast Python - published by Manning - watson-cycle/python-performance

Open Graph Description: Repository for the book Fast Python - published by Manning - watson-cycle/python-performance

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Fast Pythonhttps://www.manning.com/books/fast-python
https://patch-diff.githubusercontent.com/watson-cycle/python-performance/blob/master/cover.png
https://patch-diff.githubusercontent.com/watson-cycle/python-performance#introduction
https://patch-diff.githubusercontent.com/watson-cycle/python-performance#extracting-maximum-performance-from-built-in-features
Profiling applications with both IO and computing workloadshttps://patch-diff.githubusercontent.com/watson-cycle/python-performance/blob/master/02-python/sec1-io-cpu
Profiling code to detect performance bottleneckshttps://patch-diff.githubusercontent.com/watson-cycle/python-performance/blob/master/02-python/sec2-cpu
Optimizing basic data structures for speed: lists, sets, dictionarieshttps://patch-diff.githubusercontent.com/watson-cycle/python-performance/blob/master/02-python/sec3-basic-ds
Finding excessive memory allocationhttps://patch-diff.githubusercontent.com/watson-cycle/python-performance/blob/master/02-python/sec4-memory
Using laziness and generators for big-data pipelininghttps://patch-diff.githubusercontent.com/watson-cycle/python-performance/blob/master/02-python/sec5-lazy
https://patch-diff.githubusercontent.com/watson-cycle/python-performance#concurrency-parallelism-and-asynchronous-processing
Writing the scaffold of an asynchronous serverhttps://patch-diff.githubusercontent.com/watson-cycle/python-performance/blob/master/03-concurrency/sec1-async
Implementing the first MapReduce enginehttps://patch-diff.githubusercontent.com/watson-cycle/python-performance/blob/master/03-concurrency/sec2-naive
Implementing a concurrent version of a MapReduce enginehttps://patch-diff.githubusercontent.com/watson-cycle/python-performance/blob/master/03-concurrency/sec3-thread
Using multi-processing to implement MapReducehttps://patch-diff.githubusercontent.com/watson-cycle/python-performance/blob/master/03-concurrency/sec4-multiprocess
Tying it all together: an asynchronous multi-threaded and multi-processing MapReduce serverhttps://patch-diff.githubusercontent.com/watson-cycle/python-performance/blob/master/03-concurrency/sec5-all
https://patch-diff.githubusercontent.com/watson-cycle/python-performance#using-numpy-more-efficiently
Understanding NumPy from a performance perspectivehttps://patch-diff.githubusercontent.com/watson-cycle/python-performance/blob/master/04-numpy/sec1-basics
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Tuning NumPy's internal architecture for performancehttps://patch-diff.githubusercontent.com/watson-cycle/python-performance/blob/master/04-numpy/sec3-vectorize
https://patch-diff.githubusercontent.com/watson-cycle/python-performance#extracting-maximum-efficiency-of-hardware-and-networks
https://patch-diff.githubusercontent.com/watson-cycle/python-performance#re-implementing-critical-code-with-cython
A whirlwind tour of Cythonhttps://patch-diff.githubusercontent.com/watson-cycle/python-performance/blob/master/05-cython/sec2-intro
Profiling Cython codehttps://patch-diff.githubusercontent.com/watson-cycle/python-performance/blob/master/05-cython/sec3-profiling
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How modern hardware architectures impact Python performancehttps://patch-diff.githubusercontent.com/watson-cycle/python-performance/blob/master/06-hardware/sec1-arch
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https://patch-diff.githubusercontent.com/watson-cycle/python-performance#optimizing-modern-data-processing-libraries
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Parquet: an efficient format to store columnar datahttps://patch-diff.githubusercontent.com/watson-cycle/python-performance/blob/master/08-persistence/sec2-parquet
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Zarr for large array persistencehttps://patch-diff.githubusercontent.com/watson-cycle/python-performance/blob/master/08-persistence/sec4-zarr
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Using Numba to generate CPU codehttps://patch-diff.githubusercontent.com/watson-cycle/python-performance/blob/master/09-gpu/sec2-numba
Performance analysis of GPU code: the case of a CuPy applicationhttps://patch-diff.githubusercontent.com/watson-cycle/python-performance/blob/master/09-gpu/sec4-cupy
https://patch-diff.githubusercontent.com/watson-cycle/python-performance#analyzing-big-data-with-dask
Understanding the execution model of Daskhttps://patch-diff.githubusercontent.com/watson-cycle/python-performance/blob/master/10-dask/sec1-exec
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https://patch-diff.githubusercontent.com/watson-cycle/python-performance#appendixes
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Using Numba to generate efficient low level codehttps://patch-diff.githubusercontent.com/watson-cycle/python-performance/blob/master/A02-numba
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