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From: Mild Shock <janburse@fastmail.fm>
Newsgroups: comp.lang.python
Subject: Which Python System is affected? (Was: What does the Async Detour usually cost)
Date: Tue, 24 Jun 2025 00:48:20 +0200
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In-Reply-To: <103cl86$16hvn$1@solani.org>
Xref: csiph.com comp.lang.python:197525

Hi,

I tested this one:

Python 3.11.11 (0253c85bf5f8, Feb 26 2025, 10:43:25)
[PyPy 7.3.19 with MSC v.1941 64 bit (AMD64)] on win32

I didn't test yet this one, because it is usually slower:

ython 3.14.0b2 (tags/v3.14.0b2:12d3f88, May 26 2025, 13:55:44)
[MSC v.1943 64 bit (AMD64)] on win32

Bye

Mild Shock schrieb:
> Hi,
> 
> I have some data what the Async Detour usually
> costs. I just compared with another Java Prolog
> that didn't do the thread thingy.
> 
> Reported measurement with the async Java Prolog:
> 
>  > JDK 24: 50 ms (using Threads, not yet VirtualThreads)
> 
> New additional measurement with an alternative Java Prolog:
> 
> JDK 24: 30 ms (no Threads)
> 
> But already the using Threads version is quite optimized,
> it basically reuse its own thread and uses a mutex
> somewhere, so it doesn't really create a new secondary
> 
> thread, unless a new task is spawn. Creating a 2nd thread
> is silly if task have their own thread. This is the
> main potential of virtual threads in upcoming Java,
> 
> just run tasks inside virtual threads.
> 
> Bye
> 
> P.S.: But I should measure with more files, since
> the 50 ms and 30 ms are quite small. Also I am using a
> warm run, so the files and their meta information is already
> 
> cached in operating system memory. I am trying to only
> measure the async overhead, but maybe Python doesn't trust
> the operating system memory, and calls some disk
> 
> sync somewhere. I don't know. I don't open and close the
> files, and don't call some disk syncing. Only reading
> stats to get mtime and doing some comparisons.