On May 7, 2018 9:15:32 PM Steve Dower email@example.com wrote:
“the data shows that a focused change to address file system inefficiencies has the potential to broadly and transparently deliver benefit to users without affecting existing code or workflows.”
This is consistent with a Node.js experiment I heard about where they compiled an entire application in a single (HUGE!) .js file. Reading a single large file from disk is quicker than many small files on every significant file system I’m aware of. Is there benefit to supporting import of .tar files as we currently do .zip? Or perhaps having a special fast-path for uncompressed .zip files?
I kind of built something like this, though I haven't really put in the effort to make it overly usable yet:
(Bonus points to anyone who gets the character reference in the name, though I seriously doubt it.)
Main thing I noticed was that reading compiled .pyc files is far faster than uncompiled Python code, even if you eliminate the disk access. Kind of obvious in retrospect, but still something to note
Top-posted from my Windows phone
From: Carl Shapiro Sent: Monday, May 7, 2018 14:36 To: Nathaniel Smith Cc: Nick Coghlan; Python Dev Subject: Re: [Python-Dev] A fast startup patch (was: Python startup time)
On Fri, May 4, 2018 at 6:58 PM, Nathaniel Smith firstname.lastname@example.org wrote: What are the obstacles to including "preloaded" objects in regular .pyc files, so that everyone can take advantage of this without rebuilding the interpreter?
The system we have developed can create a shared object file for each compiled Python file. However, such a representation is not directly usable. First, certain shared constants, such as interned strings, must be kept globally unique across object code files. Second, some marshaled objects, such as the hashed collections, must be initialized with randomization state that is not available until after the hosting runtime has been initialized.
We are able to work around the first issue by generating a heap image with the transitive closure of all modules that will be loaded which allows us to easily maintain uniqueness guarantees. We are able to work around the second issue with some unobservable changes to the affected data structures. Based on our numbers, it appears there should be some hesitancy--at this time--to changing the format of compiled Python file for the sake of load-time performance. In contrast, the data shows that a focused change to address file system inefficiencies has the potential to broadly and transparently deliver benefit to users without affecting existing code or workflows.
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