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PyWeek - The numbersSystem capabilities poll is closed, here's what I can see since this time last year:
To reach the biggest target, you should write for OpenGL (89% supported; I'm guessing most of the other 11% have it but don't realise it) and Linux (85% primary and "also-haves" combined). However, write for the lowest common denominator: only 52% of respondants claim to have any hardware shader support. — alex on 2007/08/31 04:52 of Lazy Susan Comments: (log in to comment) |
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By richard on 2007/08/31 05:47:
Thanks, alex!BTW, for some other numbers... we now have 158 people signed up in 91 teams (the final count for last comp saw 191 people in 102 teams)
By Deathworks on 2007/08/31 09:06:
Hi!
Personally, my Windows XP was on the machine because we bought it from the shelf - which at least here in Germany means, current version of Windows pre-installed.
However, I don't trust M$ enough to go with my own machine online.
DeathworksIf I ever need to get a new computer or want to go privately online, I am determined to go with a Linux-only machine (second, dedicated computer?). I definitely won't let Vista get into my home. But that is all hypothetical at the moment.
By Neppord on 2007/08/31 18:18:
oupps where ware that pool?By ironfroggy on 2007/09/02 05:26:
Of course, when you say multithread you mean multiprocess, right?By alex on 2007/09/02 06:01:
ironfroggy: er, yes. Or lobby for a GIL-free Python ;-)By Treeform on 2007/09/02 06:13:
how can people not have shaders now day?By alex on 2007/09/02 06:26:
Based on the bug reports I got from my last Pyweek entry, it seems a lot of people (in this community) use integrated video chips -- ones that do not even provide a complete h/w GL 1.1 implementation, let alone shaders. I'd guess a lot of these are laptops, and also a number of Windows and Linux users never get around to installing real video drivers.By Tee on 2007/09/02 12:01:
ironfroggy: Threads are also run in parallel in dual-core or other parallel machines, aren't they?By malcolmt on 2007/09/02 12:38:
Tee: because of Python's global interpreter lock (GIL), all threads run on a single core or CPU. If you want parallelism, you have to go multi-process.