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Super Fast Python

@SuperFastPython

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Making python developers awesome at concurrency. #python #threading #multiprocessing #asyncio

Joined October 2021
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@SuperFastPython
Super Fast Python
@SuperFastPython
7 months
Discover how to execute Python loops using all CPU cores. Book: "Parallel Loops in Python".
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@SuperFastPython
Super Fast Python
@SuperFastPython
10 hours
Parallel Numpy Array Fill (up to 3x faster). #Python #Concurrency.
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superfastpython.com
You can fill a Numpy array in parallel using Python threads. Numpy will release the global interpreter lock (GIL) when calling a fill function, allowing Python threads to run in parallel and populate...
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@SuperFastPython
Super Fast Python
@SuperFastPython
13 hours
Use callback in multiprocessing.ThreadPool to trigger dependent actions.#Python #Concurrency
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@SuperFastPython
Super Fast Python
@SuperFastPython
15 hours
Tip: Ensure tasks back off or retry with a delay to avoid continuous state changes that cause livelocks. #Python #Concurrency
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@SuperFastPython
Super Fast Python
@SuperFastPython
16 hours
Why Numpy Parallelism is Important. #Python #Concurrency.
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superfastpython.com
Parallelism is an important consideration when using numpy. Numpy is perhaps the most common Python library for working with arrays of numbers. It is popular when used directly, such as when perfor...
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@SuperFastPython
Super Fast Python
@SuperFastPython
19 hours
Use callback in multiprocessing.ThreadPool for logging task completion.#Python #Concurrency
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@SuperFastPython
Super Fast Python
@SuperFastPython
20 hours
multiprocessing.BoundedSemaphore API.#Python #Concurrency
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@SuperFastPython
Super Fast Python
@SuperFastPython
21 hours
Tip: Use "threading.RLock" if a thread needs to re-acquire the same lock, avoiding self-deadlock scenarios. #Python #Concurrency
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@SuperFastPython
Super Fast Python
@SuperFastPython
22 hours
Benchmark Python with time.monotonic(). #Python #Concurrency.
superfastpython.com
You can benchmark Python code using the time.monotonic() function. In this tutorial, you will discover how to benchmark Python code using the time.monotonic() function. Let’s get started. Need to...
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@SuperFastPython
Super Fast Python
@SuperFastPython
1 day
Use callback in multiprocessing.ThreadPool to aggregate results.#Python #Concurrency
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@SuperFastPython
Super Fast Python
@SuperFastPython
1 day
Tip: Prefer higher-level concurrency primitives like "Semaphore" or "Condition" when they fit your use case, as they reduce the need for manual lock management. #Python #Concurrency
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@SuperFastPython
Super Fast Python
@SuperFastPython
1 day
Parallel NumPy Vector Math with Multiprocessing. #Python #Concurrency.
superfastpython.com
You can use multiprocessing to apply a math function to each item in a numpy vector. Although this is straightforward to implement, it is likely to result in worse performance compared to the...
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@SuperFastPython
Super Fast Python
@SuperFastPython
1 day
Use callback with multiprocessing.ThreadPool to process results.#Python #Concurrency
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@SuperFastPython
Super Fast Python
@SuperFastPython
1 day
Using Multiprocessing With Numpy Results in Worse Performance. #Python #Concurrency.
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superfastpython.com
You can parallelize numpy programs using multiprocessing. It is likely that using process-based concurrency via multiprocessing to parallelize a numpy program will result in worse overall performan...
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@SuperFastPython
Super Fast Python
@SuperFastPython
2 days
Demonstrate difference between close() and terminate() in the ThreadPool.#Python #Concurrency
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@SuperFastPython
Super Fast Python
@SuperFastPython
2 days
Tip: Limit the scope of locks to only critical sections, reducing the chance of blocking other threads unnecessarily. #Python #Concurrency
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@SuperFastPython
Super Fast Python
@SuperFastPython
2 days
Performance Cost of Naive Parallelism in NumPy. #Python #Concurrency.
superfastpython.com
You can add parallelism to a numpy program to improve its performance. Nevertheless, there is a danger in naively adding parallelism to a program that uses numpy. This is for many reasons, such as...
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@SuperFastPython
Super Fast Python
@SuperFastPython
2 days
Mix close() and terminate() based on task conditions in the ThreadPool.#Python #Concurrency
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@SuperFastPython
Super Fast Python
@SuperFastPython
2 days
multiprocessing.Barrier API.#Python #Concurrency
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@SuperFastPython
Super Fast Python
@SuperFastPython
2 days
Tip: Use Python's "timeout" parameter when acquiring locks to detect and recover from potential deadlocks. #Python #Concurrency
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@SuperFastPython
Super Fast Python
@SuperFastPython
2 days
Benchmark Python with time.process_time(). #Python #Concurrency.
superfastpython.com
You can benchmark Python code using the time.process_time() function. In this tutorial, you will discover how to benchmark Python code using the time.process_time() function. Let’s get started. Need...
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