# orderedItems[].object.content
Published my first PyPI package today, called lafleur.

#lafleur is a specialized CPython JIT fuzzer that uses a coverage-guided, evolutionary approach. It executes test cases, observes their effect on the JIT's state by analyzing verbose trace logs, and uses that feedback to guide its mutations, becoming smarter at finding interesting code paths over time.

Let me know if you use it or have any questions.

https://pypi.org/project/lafleur/
https://github.com/devdanzin/lafleur

#Python #CPython #fuzzer #fuzzing #JIT

# orderedItems[].object.ephemeral.metadata[].twitterTitle
GitHub - devdanzin/lafleur: A feedback-driven, evolutionary fuzzer for the CPython JIT compiler.

# orderedItems[].object.ephemeral.metadata[].description
A feedback-driven, evolutionary fuzzer for the CPython JIT compiler. - devdanzin/lafleur

# orderedItems[].object.ephemeral.metadata[].ogDescription
A feedback-driven, evolutionary fuzzer for the CPython JIT compiler. - devdanzin/lafleur

# orderedItems[].object.ephemeral.metadata[].ogTitle
GitHub - devdanzin/lafleur: A feedback-driven, evolutionary fuzzer for the CPython JIT compiler.

# orderedItems[].object.ephemeral.metadata[].twitterDescription
A feedback-driven, evolutionary fuzzer for the CPython JIT compiler. - devdanzin/lafleur

# orderedItems[].object.content
Some new preliminary numbers from Ken Jin for the JIT in Python 3.15:

"On AArch64 macOS, the JIT has a 7-8% speedup over the tail calling interpreter with all optimizations enabled."

https://docs.python.org/3.15/whatsnew/3.15.html#whatsnew315-jit

https://github.com/python/cpython/comm it/d9c1235db44100b16355a347ca9e304df48411a7

This is in addition to 3-4% over the standard CPython interpreter on x86-64 Linux.

See also https://doesjitgobrrr.com

#Python #Python315 #JIT #CPython

# orderedItems[].object.attachment[].name
Top half of a chart showing dozens of benchmarks, most of them more than 1x faster.

# orderedItems[].object.attachment[].name
Bottom half of the chart. About half of these are also most of them more than 1x faster, about a quarter are around 0.9 faster, and a quarter are insignificant.

# orderedItems[].object.ephemeral.metadata[].description
Editor, Hugo van Kemenade,. This article explains the new features in Python 3.15, compared to 3.14. For full details, see the changelog. Summary – Release highlights: PEP 799: A dedicated profilin...

# orderedItems[].object.ephemeral.metadata[].ogDescription
Editor, Hugo van Kemenade,. This article explains the new features in Python 3.15, compared to 3.14. For full details, see the changelog. Summary – Release highlights: PEP 799: A dedicated profilin...

# orderedItems[].object.ephemeral.metadata[].twitterTitle
gh-139038: Add macOS performance numbers for the JIT (GH-143479) · python/cpython@d9c1235

# orderedItems[].object.ephemeral.metadata[].description
The Python programming language. Contribute to python/cpython development by creating an account on GitHub.

# orderedItems[].object.ephemeral.metadata[].ogTitle
gh-139038: Add macOS performance numbers for the JIT (GH-143479) · python/cpython@d9c1235

# orderedItems[].object.ephemeral.metadata[].twitterTitle
does JIT go brrr? | Python JIT Performance Tracker

# orderedItems[].object.ephemeral.metadata[].ogDescription
Track Python's experimental JIT compiler performance vs the standard interpreter over time. Daily benchmarks across multiple machines using pyperformance.

# orderedItems[].object.ephemeral.metadata[].ogTitle
does JIT go brrr? | Python JIT Performance Tracker

# orderedItems[].object.ephemeral.metadata[].twitterDescription
Track Python's experimental JIT compiler performance vs the standard interpreter over time. Daily benchmarks across multiple machines using pyperformance.

# orderedItems[].object.content
Disabling #JIT compilation on #PostgreSQL has been a revelation. One of my queries (against my closure table for threading) went from 4 seconds to about 100ms. And that query gets hit a lot because it's loaded opportunistically as I scroll in the UI.

It made a huge difference there but also just in general because my queries tend to be non-trivial and the system was making the wrong choice to invoke the JIT often.

# orderedItems[].object.source.content
Disabling #JIT compilation on #PostgreSQL has been a revelation. One of my queries (against my closure table for threading) went from 4 seconds to about 100ms. And that query gets hit _a lot_ because it's loaded opportunistically as I scroll in the UI.

It made a huge difference there but also just in general because my queries tend to be non-trivial and the system was making the wrong choice to invoke the JIT often.

# orderedItems[].object.content
ZJIT is now available in Ruby 4.0

https://railsatscale.com/2025-12-24-la unch-zjit/

#ruby #jit

# orderedItems[].object.ephemeral.metadata[].description
ZJIT is now available with the release of Ruby 4.0. Here’s an update of our progress.

# orderedItems[].object.ephemeral.metadata[].ogDescription
ZJIT is now available with the release of Ruby 4.0. Here’s an update of our progress.

# orderedItems[].object.content
Ever wondered what really happens when your Java code runs? In the latest deep dive, we explore how the JVM translates bytecode into rapid machine instructions through interpreters, JIT compilers, and multiple tiers (C1, C2, and beyond).

Read the full story:
https://softwaremill.com/the-long-jour ney-how-byte-code-gets-interpreted-by-the-machine/

#Java #JVM #JIT #Performance #SoftwareEngineering #Programming

# orderedItems[].object.ephemeral.metadata[].twitterTitle
The Long Journey - How Byte Code Gets Interpreted by the Machine

# orderedItems[].object.ephemeral.metadata[].description
Explore how Java bytecode is transformed into executable machine code through JVM internals, JIT compilation tiers, and optimization processes.

# orderedItems[].object.ephemeral.metadata[].ogDescription
Explore how Java bytecode is transformed into executable machine code through JVM internals, JIT compilation tiers, and optimization processes.

# orderedItems[].object.ephemeral.metadata[].ogTitle
The Long Journey - How Byte Code Gets Interpreted by the Machine

# orderedItems[].object.ephemeral.metadata[].twitterDescription
Explore how Java bytecode is transformed into executable machine code through JVM internals, JIT compilation tiers, and optimization processes.

# orderedItems[].object.content
lafleur, the CPython JIT fuzzer, now can compare timings for running a piece of code with JIT on and off.

The idea is that if the run with JIT on is much slower that with JIT off, we have found a performance bug.

Brandt Bucher suggested this mode. It took a while to get started on it, but it was simple to implement on top of Differential Mode.

So many modes, so little compute available...

#lafleur #JIT #fuzzing #fuzzer #CPython #Python

# orderedItems[].object.content
Differential Mode has just landed in lafleur, the CPython JIT fuzzer. It runs the same code with and without the JIT, compares the result, and flags any discrepancies.

It does this while mutating the code in a feedback-guided loop, so it evolves the fuzzing scripts trying to find one where the JIT gives wrong results.

This is actually the 2nd time this feature is implemented (now better and more robust): it used to work, broke, and now is back.

#lafleur #JIT #fuzzing #fuzzer #CPython #Python

# orderedItems[].object.content
via @dotnet : Performance Improvements in .NET 10

https://ift.tt/BroyIWM
#DotNet10 #PerformanceImprovements #DotNet #Programming #SoftwareDevelopment #Coding #Developer #Technology #Microbenchmarks #Optimization #JIT #GarbageCollection #IOT #CSharp #LINQ #JsonSe…

# orderedItems[].object.ephemeral.metadata[].ogDescription
Take a tour through hundreds of performance improvements in .NET 10.

# orderedItems[].object.ephemeral.metadata[].ogTitle
Performance Improvements in .NET 10 - .NET Blog

# orderedItems[].object.content
Posted a topic about lafleur, the CPython JIT fuzzer I'm developing: https://discuss.python.org/t/introduci ng-lafleur-a-cpython-jit-fuzzer/103452

It has many details about the project, the results (4 JIT crashes so far), calls for help and ideas for the future.

If any of this interests you, please give a read. Boosts welcome :)

#lafleur #fusil #Python #fuzzer #fuzzing #CPython #JIT

# orderedItems[].object.ephemeral.metadata[].twitterTitle
Introducing `lafleur`, a CPython JIT fuzzer

# orderedItems[].object.ephemeral.metadata[].description
Greetings all, I’d like to present lafleur, a CPython JIT fuzzer. We’d love and welcome any kind of collaborations, from questions to vague ideas to working code to being part of a research project. Please start discussi…

# orderedItems[].object.ephemeral.metadata[].ogDescription
Greetings all, I’d like to present lafleur, a CPython JIT fuzzer. We’d love and welcome any kind of collaborations, from questions to vague ideas to working code to being part of a research project. Please start discussions, file issues or propose pull requests if you want to contribute.  Summary What lafleur is a specialized fuzzer designed to find crashes in CPython’s experimental JIT, having found 4 JIT crashes so far.  How It works by mutating code samples and reading JIT debug information w...

# orderedItems[].object.ephemeral.metadata[].ogTitle
Introducing `lafleur`, a CPython JIT fuzzer

# orderedItems[].object.ephemeral.metadata[].twitterDescription
Greetings all, I’d like to present lafleur, a CPython JIT fuzzer. We’d love and welcome any kind of collaborations, from questions to vague ideas to working code to being part of a research project. Please start discussions, file issues or propose pull requests if you want to contribute.  Summary What lafleur is a specialized fuzzer designed to find crashes in CPython’s experimental JIT, having found 4 JIT crashes so far.  How It works by mutating code samples and reading JIT debug information w...

# orderedItems[].object.content
Certa vez chegando nos EUA o agente de fronteira perguntou qual era o propósito da minha viagem [...]. Falei que ia palestrar em um evento sobre Python e ele disparou:

— Python é interpretada ou compilada?

(esse causo fala de #JIT e #Python 3.14)

https://ramalho.org/posts/interpretada-compilada/

# orderedItems[].object.content
There are scenarios in which Python code beats C code. Python code can be JIT compiled/specialized to handle the type of data on hand, along with the most common code paths. Meanwhile the C code is fixed, and can't adapt to changes in the input data, it always takes the same amount of time

#python #jit #adaptive

# orderedItems[].object.content
Decorator JITs - Python as a DSL - Eli Bendersky's website

Link
📌 Summary: 本文探討 Python 中 JIT (即時編譯) 裝飾器的應用，尤其在機器學習函式庫中如 JAX 和 Triton 的實作方式。文中介紹了不同的 JIT 裝飾器，包括 AST、字節碼和追蹤方式的設計理念及運作原理。透過簡化的範例，說明如何將 Python 函式轉換為中間表示並編譯成 LLVM IR，最終實現即時執行。這些方法的設計均旨在提升 Python 函式的執行效率，並提供靈活的程式碼表達方式。

🎯 Key Points:
- JIT 裝飾器在 Python 中可用於提升機器學習相關函式的執行效率。
- 介紹三種不同的 JIT 實作方法：AST 驅動的 JIT、字節碼驅動的 JIT，以及基於追蹤的 JIT。
- 透過 AST 驅動的 JIT，可以從 Python 代碼生成中間表示，進而編譯至 LLVM IR。
- Triton 語言示範了類似的方法，將 Python 函式轉換為可在 GPU 上執行的代碼。
- 追蹤技術能夠在運行時期動態生成 IR，支援複雜的 Python 結構。

🔖 Keywords: #JIT #Python #LLVM #機器學習 #即時編譯

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