Pythonコードを35000倍に高速化したい
はじめに Pythonは世界的にも人気のあるプログラミング言語ですが、実行速度については課題があります。Pythonの実行速度を高速化したい、という要求は根強く、これまでにも様々な処理系が開発されています。 この記事はPythonで書かれたコードを35000倍に高速化するにはどのような方法があるかについてまとめたものです。 この記事は： Pythonで書かれたアルゴリズムを35000倍に高速化する 事前コンパイル、並列化、SIMD演算を駆使する 最終的に44000倍まで高速化できた なぜ35000倍？ 2023年5月2日にModular社よりPythonの使いやすさとC言語の性能を兼ね備える新しいプログラミング言語、Mojoの開発について発表がありました。低レベルのハードウェア向けにコンパイル可能なこと、文法的にはPythonを踏襲しており、既存のPythonライブラリを利用可能であること
Making Python 100x faster with less than 100 lines of Rust
Update: I gave a talk on this topic at P99 CONF 2023 and at PyCon IL 2024 (Hebrew). A while ago at $work, we had a performance issue with one of our core Python libraries. This particular library forms the backbone of our 3D processing pipeline. It’s a rather big and complex library which uses NumPy and other scientific Python packages to do a wide range of mathematical and geometrical operations.
Has your password been pwned? Or, how I almost failed to search a 37 GB text file in under 1 millisecond (in Python)
So, there's this website, Have I Been Pwned, where you can check if your email address has appeared in a data breach. There's also a Pwned Passwords section for passwords ... but, typing your password on a random website probably isn't such a great idea, right? Of course, you could read about how HIBP protects the privacy of searched passwords, and understand how k-Anonymity works, and check that
High Performance Python 1 from PyCon 2012 (slides, video, src)
High Performance Python 1 from PyCon 2012 (slides, video, src) This is the follow-on for my PyCon 2012 notes from the end post. I gave a 3.5 hour tutorial on High Performance Python 1, below I link to the slides, the video and the source code. UPDATE2 From October 2014 I’ll be training on High Performance Python and Data Science in London using Python – sign-up here to get on our announce list (no
Stand back... I'm going to try science! : Blog – @kgriffs
Slow Website? Check out Luhnar for an easy way to speed up your client's Django, WordPress, or other CMS-based or custom site. www.luhnar.com The majority of benchmarks posted on the web are derived from testing simple “hello world” apps. Although certainly better than nothing, these tests tell us little about real-world performance. Ideally, one would compare multiple implementations of a non-tri
The Performance Impact of Using dict() Instead of {} in CPython 2.7
Open Source Developer, Author, Editor. There’s always room for pie. I’ve been reviewing lot of code lately for various open source and internal projects written in Python. As part of those reviews, I have noticed what I think is a trend toward using dict() instead of {} to create dictionaries. I don’t know exactly why this trend has emerged. Perhaps the authors perceive dict() as more readable tha
A Guide to Analyzing Python Performance
Introduction Coarse grain timing with time Fine grain timing with a timing context manager Line-by-line timing and execution frequency with a profiler How much memory does it use? IPython shortcuts for line_profiler and memory_profiler Where’s the memory leak? Which objects are the most common? Which objects have been added or deleted? What is referencing this leaky object? Effort vs precision Ref
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Understanding CPUs can help speed up Numba and NumPy code
Reddit - Dive into anything
Speeding Up Your Python Code - Max Burstein's Blog
Python performance the easy(ish) way
GitHub - alonho/pytrace: pytrace is a fast python tracer. it records function calls, arguments and return values. can be used for debugging and profiling.