How about parallel computing with finance

Brian G. Powell bpowell at uucom.com
Thu Dec 7 11:05:38 EST 2000


Go to REITER's in D.C.

The "nearest bookstore" could be overloaded with "Your a Dummy" books.

Horatio B. Bogbindero writes:
 > 
 > 
 > i went to closes bookstore here in our area and i could not find a single
 > book about neural network here. this is the drawback for being in a third
 > world country like the philippines. hehehe. anyway, i have been surfing
 > the net and am beginning to find it interesting. anyway, thanks for the
 > information.
 > 
 > On Wed, 6 Dec 2000, 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 know
 > > > > 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
 > > reasonable.
 > > 
 > > 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.
 > > 
 > >    rgb
 > > 
 > > -- 
 > > 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
 > > 
 > > 
 > > 
 > 
 >  
 > ---------------------
 > william.s.yu at ieee.org
 >  
 > "I am, therefore I am."
 > -- Akira
 >  
 > 
 > 
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