How about parallel computing with finance
Terrence E. Brown
tbrown at lector.kth.se
Wed Dec 6 23:13:47 PST 2000
Probably , not much discussion should take place here. But I will have a
another venue for it in a few days.
"Robert_G._Brown_ (Robert G. Brown )" wrote:
> On Wed, 6 Dec 2000, Terrence E. Brown wrote:
> > I am also interested in the business and managerial application as well
> as other
> > industrial apps.
> > I would certainly like to talk with another with similar thoughts. I
> have even
> > started an org dedicated to that objective.
> > Terrence
> > "Horatio_B._Bogbindero_ (Horatio B. Bogbindero )" wrote:
> > > i would just like to know about building neural networks in clusters.
> > > i am not into neural network but some people here in the university
> > > maybe interested. however, we do not know where to start. i would to
> > > where i can get some sample NN code. maybe for something trivial.
> Hmmm, I don't know how much such of such a discussion should occur on
> this list. The following (up to <shameless marketing> is probably
> Neural networks (and the genetic algorithms that underlie a really good
> one in a problem with high dimensionality) are certainly fascinating
> things. They are even in some sense a fundamentally parallel thing, as
> their processing capabilities result from a tiered composition of
> relatively simple (but nonlinear) transfer functions. A general
> discussion of NN's and how they work is clearly not appropriate for this
> list though. There are some particular issues that are.
> In practice, most the parallelization issues of NN's are a small part of
> the overall problem UNLESS you are interested in constructing custom
> hardware or building NN ASIC's or the like. This is because computers
> generally run neural network SIMULATORS and use what amounts to
> relatively small-scale linear algebra (transmogrified through an e.g.
> logistic function) to do a net evaluation. Since this is so small that
> it will often fit into even L1 (and almost certainly into L2) there is
> no possible way that it can be distributed in parallel except via
> (embarrassingly parallel) task division in a profitable way.
> Evaluation of networks' values applied to training/trial set data makes
> up the bulk of the numerical effort in building a network and is at the
> heart of the other tasks (e.g. regression or conjugate gradient
> improvement of the weights). For large training/trial sets and "big"
> networks, this can be split up (and my experiences splitting it up are
> recorded in one of the talks available on the brahma website). For
> small ones, the ratio of the time spent doing parallel work to the time
> spent doing parallel communication isn't favorable and one's parallel
> scaling sucks. As in even two nodes may complete in more time than one
> working alone.
> I'm working on an improved algorithm that splits up NN
> construction/training in a way that is more functionally coherent. That
> way one or two of the distinct tasks can be parallelized very
> efficiently and thoroughly and the results fed back into a mostly serial
> or entirely serial step further down the pipeline. I expect that this
> will permit a very nice master/slave implementation of a neural network
> constructor where nodes are slaves that can be working on any of a
> number of parallelized tasks according to the directions of the master
> (quite possibly with internode IPC's, though), and all the serial work
> can be done on the master.
> <shameless marketing>
> NN's (parallelized or not) are, as one might expect, incredibly useful
> and potentially profitable. After all, a successful predictive model
> "tells the future", at least probabilistically, by construction, and
> does even better than a delphic oracle ever did in that they can often
> provide a quantitative (although probabilistic) answer to "what if"
> questions as well. In ancient times the words of the oracle were just
> fate and nothing you could do would change them. In business, one would
> like to predict what is likely to happen if you follow plan A instead of
> plan B. Just about any business manager has a list of questions about
> the future (what if or otherwise) they would love to have the answers
> to. That's one of Market Driven's foci -- providing answers and
> expertise in business optimization.
> </shameless marketing>
> Anyway, let me know if you're interested in more discussion of this (or
> how NN's work or how they and predictive modeling in general can be
> applied in business and managerial situations) offline.
> Robert G. Brown http://www.phy.duke.edu/~rgb/
> Duke University Dept. of Physics, Box 90305
> Durham, N.C. 27708-0305
> Phone: 1-919-660-2567 Fax: 919-660-2525 email:rgb at phy.duke.edu
> To: "Terrence E. Brown" <tbrown at lector.kth.se>
> From: "Robert G. Brown" <rgb at phy.duke.edu>
> Subject: Re: How about parallel computing with finance
> Date: Wed, 6 Dec 2000 12:56:00 -0500 (EST)
> cc: "Horatio_B._Bogbindero_ (Horatio B. Bogbindero
> <wyy at cersa.admu.edu.ph>)" <wyy at cersa.admu.edu.ph>, liuxg_
> <beowulf at eyou.com>, "beowulf at beowulf.org_" <beowulf at beowulf.org>
> Message-ID: <Pine.LNX.4.30.0012061029560.15373-100000 at ganesh.phy.duke.edu>
> Received: from ganesh.phy.duke.edu (ganesh.phy.duke.edu [184.108.40.206]) by
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Terrence E. Brown, Ph.D.
Stockholm School of Entrepreneurship
Royal Technical Institute
Drottning Kristina Väg 35D
S- 100 44 Stockholm
Tel: +46(0)8-7906174 (work)
Fax: +46(0)8-7906741 (work)
Email: tbrown at lector.kth.se
or terrence.brown at sses.se
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