Super Fast Python
@SuperFastPython
Making python developers awesome at concurrency. #python #threading #multiprocessing #asyncio
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Discover how to execute Python loops using all CPU cores. Book: "Parallel Loops in Python" marvelous-writer-6152.ck.page/99ee689b9b
ThreadPool vs ThreadPoolExecutor in Python superfastpython.com/threadpool-vs-… #Python #Concurrency
Tip: Use exponential backoff in busy-wait loops to gradually increase wait times, minimizing resource consumption in Python programs. #Python #Concurrency
How to Get ThreadPool Worker Names in Python superfastpython.com/threadpool-wor… #Python #Concurrency
Discover how to develop parallel for-loops with the multiprocessing Pool class. Book: "Python Multiprocessing Pool Jump-Start" superfastpython.com/python-multipr…
Tip: Consider time.sleep() or asyncio.sleep() in Python loops to reduce CPU usage during busy waiting when waiting for external conditions. #Python #Concurrency
How to Use ThreadPool imap() in Python superfastpython.com/threadpool-ima… #Python #Concurrency
superfastpython.com
How to Use ThreadPool imap() in Python - Super Fast Python
You can issue tasks to the ThreadPool pool one-by-one and execute them in threads via the imap() method. In this tutorial you will discover how to use the imap() method to issue tasks to the Thread...
Tip: Use spin locks only for short critical sections in Python, as they continuously consume CPU cycles while waiting. #Python #Concurrency
ThreadPool Best Practices in Python superfastpython.com/threadpool-bes… #Python #Concurrency
Tip: Avoid busy waiting in Python by using synchronization primitives like threading.Event or Condition to block efficiently until a signal is received. #Python #Concurrency
ThreadPool and the Global Interpreter Lock (GIL) superfastpython.com/threadpool-gil/ #Python #Concurrency
Tip: Optimize task granularity in Python to ensure each parallel job is large enough to offset the overhead of process management. #Python #Concurrency
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