PyPyの論文つぶやき
Ryotaro Ikeda @chlere 今回はlanguage-specificな特徴量をいかにしてMeta-Tracingで扱うかについて述べられています (via : http://t.co/miqpeba ) #pypy 2011-08-15 11:26:20 Ryotaro Ikeda @chlere Pythonで簡単にVMを作りたい！->RPythonで作れるPyPyというFWを作りました！->JITは言語の実装に寄らないようにRPythonそのものを対象にしちゃうよ！->あれ、データモデルの部分にもJITが入り込むからすげー早くなっちゃった （いまここ） #pypy 2011-08-15 15:59:05
PyPy Status Blog JA
Pyrlangでは、BEAMの約２５％の命令を実装しました。現在Pyrlangがサポートしている機能は、整数の計算、クロージャ、例外処理や、アトム（atom）、リスト、組みへのいくつかの操作、ユーザーモジュール、及びシングルコア上のマルチプロセスです。Pyrlangはまだ開発している最中です。
PyPy 1.6 - kickass panda
We're pleased to announce the 1.6 release of PyPy. This release brings a lot of bugfixes and performance improvements over 1.5, and improves support for Windows 32bit and OS X 64bit. This version fully implements Python 2.7.1 and has beta level support for loading CPython C extensions. You can download it here: http://pypy.org/download.html What is PyPy? PyPy is a very compliant Python interpreter
#3 PyPyについての講演、ハンズオン、スプリント | gihyo.jp
前回に続き、Pythonそのものの講演について紹介したいと思います。今回は、PyPyというPythonで実装されたPythonの講演を紹介します。 本カンファレンスに一緒に参加した池さん（@rokujyouhitoma）がPyPyというプロジェクトにとても興味をもたれたという話を受け、以下のPyPyの講演のレポートは池さんに執筆していただきました。 PyPy（執筆：池さん） PyPy IN PRODUCTION PyPyのセッションは、メインコミッター、設計者であるAntonio Cuni氏とArmin Rigo氏が講演されました。 Armin Rigo氏 大学にてプログラミング言語および言語実装を研究。PyPyプロジェクトのリーダー。CPythonのJITコンパイラであるPsycoの作者。Antonio Cuni氏らとPyPyに関する論文を共著。 Antonio Cuni氏 2006年よ
Realtime image processing in Python
Image processing is notoriously a CPU intensive task. To do it in realtime, you need to implement your algorithm in a fast language, hence trying to do it in Python is foolish: Python is clearly not fast enough for this task. Is it? :-) Actually, it turns out that the PyPy JIT compiler produces code which is fast enough to do realtime video processing using two simple algorithms implemented by Håk
PyPy 1.5 Released: Catching Up
We're pleased to announce the 1.5 release of PyPy. This release updates PyPy with the features of CPython 2.7.1, including the standard library. Thus all the features of CPython 2.6 and CPython 2.7 are now supported. It also contains additional performance improvements. You can download it here: http://pypy.org/download.html What is PyPy? PyPy is a very compliant Python interpreter, almost a drop-
PyPy faster than C on a carefully crafted example
Good day everyone. Recent round of optimizations, especially loop invariant code motion has been very good for small to medium examples. There is work ongoing to make them scale to larger ones, however there are few examples worth showing how well they perform. This one following example, besides getting benefits from loop invariants, also shows a difference between static and dynamic compilation.
A JIT for Regular Expression Matching
This is part 2 of a series, see Part 1 for an introduction. In this post I want to describe how the JIT generator of the PyPy project can be used to turn the elegant but not particularly fast regular expression matcher from the first part into a rather fast implementation. In addition, I will show some speed measurements against various regular expression implementations. Again, note the disclaime
PyPy Speed
Plot 1: The above plot represents PyPy3.11 (pypy3.11-jit-64) benchmark times normalized to cpython. Smaller is better. It depends greatly on the type of task being performed. The geometric average of all benchmarks is or times faster than cpython How has PyPy performance evolved over time? Plot 2: Speedup compared to cpython, using the inverse of the geometric average of normalized times, out of b
Benchmarking twisted
To reproduce, run each benchmark with: benchname.py -n 12 -d 5 WARNING: running tcp-based benchmarks that open new connection for each request (web & accept) can exhaust number of some kernel structures, limit n or wait until next run if you see drops in request per second. The first obvious thing is that various benchmarks are more or less amenable to speedups by JIT compilation. Accept and tcp g
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言語のJITコンパイラをその言語自身で作る事の意義とは