From: Shelby Moore <shelby@coolpage.com>

Date: Thu, 21 Oct 2010 15:35:10 -0400

Message-ID: <1c7e311891996a756f942f297081b84c.squirrel@sm.webmail.pair.com>

To: shelby@coolpage.com

Cc: "Boris Zbarsky" <bzbarsky@mit.edu>, "www-style list" <www-style@w3.org>

Date: Thu, 21 Oct 2010 15:35:10 -0400

Message-ID: <1c7e311891996a756f942f297081b84c.squirrel@sm.webmail.pair.com>

To: shelby@coolpage.com

Cc: "Boris Zbarsky" <bzbarsky@mit.edu>, "www-style list" <www-style@w3.org>

>> On 10/21/10 12:32 PM, Shelby Moore wrote: >>> Good point. I am glad we can get some initial thinking about >>> quantification. >>> >>> The key may be how multi-level hierarchy is handled. I was supposing >>> it >>> is necessary that the hierarchy places some restrictions on >>> combinations, >>> as is the case with the CSS box model. >> >> It's reasonably common to have thousands to tens of thousands of >> siblings in the CSS box model. > > But they probably have a repeating pattern, and I am supposing such > repeating patterns will present an opportunity for optimization. > > It is very unlikely to have maximum entropy (randomization) amongs 10,000 > elements in a page. I doubt any thing could be comprehended from the page, > because by definition, there is no Shannon-Entropy information in that > case of maximum entropy. > > What CSS currently does is model some of the most popular repeating > patterns, e.g. table cols and rows. > > I am supposing we can generalize the search for patterns and optimizing > them. Actually it is quite simple and obvious. Those 1000s of elems on page are mostly inline content. I showed the equivalence in the general model for display:inline: http://lists.w3.org/Archives/Public/www-style/2010Oct/0486.html And we see that the generalized CSS for display:inline has a very well defined and manageable constraint model. Really all we have to do is re-apply the existing known models from typeography (i.e. current CSS) to the generalized model, and this will remove nearly all of the N from the generalized constraint solver. We only have to define how those models (of repeating generalized constraints) interact with random generalized constraints of a much more limited quantity. We know they will be limited, because otherwise randomness will insure no mutual information (comprehension) for the reader. If there are any cases that introduce new repeating patterns of large N count, then we will recognize these as popular layout models that have to be optimized, e.g multi-columns. This is exactly what I meant by generalizing the model will allow us to accelerate new things and give us a better understanding of how to code our layout engines to enable such. > >>>> And these are constraints on variables with, typically, positive reals >>>> or reals as domains, correct? >>> >>> I have not yet contemplated how large the per table entry data >>> structure >>> is >> >> The question is not of data structure size, but of algorithmic >> complexity. Most constraint satisfaction algorithms I have been able to >> find seem to assume finite domains for the variables, and have >> complexity at least D^N where N is the number of variables and D is the >> domain size, at first read. Again, maybe I'm just totally >> misunderstanding them.... > > Ditto what I wrote above. That D^N complexity assumes the domain is set of > all random possibilities. > >> But in our case N is order of 1e2--1e5 and D is infinite in theory. In >> practice, D is order of 2^{30} in Gecko, say. > > Typo? Don't you mean in Gecko D^N is on order of 2^30? > >>> but if is less than 1024 bytes, then n = 1024 is less than 1 GB of >>> virtual memory (hopefully physical memory) >> >> Per page, yes? > > Yes but virtual memory, and that is before any such optimization for > repeating patterns. And 1024 was just pulled out of the air, it might be > 1/10 or 10 times that (but I lean towards it will be 1/10). > >>> and let's just ballpark guesstimate on the order of less than Gflops. >> >> Where is this estimate coming from? What are we measuring, even? flops >> are operations/second; what does it mean to measure problem complexity >> in flops? > > I was just making some wild guessestimate of complexity and typical use > case in my head, and extracting to ops/sec. > > We would really need to dig more into this. Afaics, you are raising > issues which are for the next stage things I wrote I want to contemplate. > So I will have to put that off for now, and come back to it later. > > >Received on Thursday, 21 October 2010 19:35:38 UTC

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