Hi Andi, Ben has a good answer, and to emphasize, let me add this: Think of the atomspace as being a collection of trees. The atoms are the nodes in the tree. Any one atom can appear in many trees, and so the whole thing is in fact tangled into a big matt, like a rhizome https://www.google.com/search?q=rhizome&tbm=isch
The pattern matcher starts at one atom, and walks the rhizome, exploring nearest neighbors, until all the entire neighborhood is explored (and a match is found, or some other (local) computation is performed). The problem is that the atoms are scattered randomly through RAM, so when the nearest neighbor walk happens, random locations in RAM get visited. I'm guessing that there is a lot of cache-miss going on two: If you have, say, a CPU cache that is 8-way, 4-associative, then you could have maybe 32 atoms in the cache, but the chance that the 33rd atom will accidentally be in one of the existing cache lines is just about zero, and so the graph walk will have a 99.9% cache-miss rate. (most graphs that get searched have more that 32 atoms in them. ) Hmm, I have an idea -- I guess the atomsapce *could* keep track of individual connected components (create a bag of trees, which are connected by one or more atoms) -- any given search is guaranteed to stay in just one bag, and so maybe one could download the entire bag to the gpu before starting a search. Could work if the bags are small enough to fit in GPU ram. Maybe allocation could be changed to improve cache locality: allocate atoms so that they are more likely to be on the same cache line if they are also connected. But this becomes a hard, fiddly computer-science problem... --linas On Mon, Aug 1, 2016 at 3:26 PM, Andi <[email protected]> wrote: > Hello All, > I do not want to disturb the ongoing work so an answer to this question is > not urgent, > but it will help me during my investigations within the next month. > > *What in the hell could prevent me to look at the Atomspace as a certain > kind of Neuronal Network?* > > Please don't tell me:"Because it is a hypergraph" haha.... > > One of my aims is, try to port the whole thing, or some parts, to > hardware. Maybe a bunch of some GPU's, PLD's and a CPU can do it. It seems > that they are designing some interesting machines for Deep Learning - so > maybe even nothing new has to be invented.. > > For first steps I think it should at least be possible to use some > GPU-power to do some work in parallel or is there really a theoretical > barrier for paralleling some work that I cannot see in the moment? > > Please don't be afraid, I know what kind of challenging task this is and > would carry it on my own back. But maybe it is not so much work if the > right approach is found... > At least I want to investigate this - so any red lights blinking? > > --Andi > > > Am Sonntag, 24. April 2016 07:07:25 UTC+2 schrieb Ben Goertzel: >> >> Indeed this is not an OpenCoggy question, but some of us may be able >> to help... is this dynamic data or instantaneous data you're trying to >> classify? >> >> >> >> On Sat, Apr 23, 2016 at 1:46 PM, <[email protected]> wrote: >> > Hi >> > >> > I have a dataset of mocap (motion caption) positions as vectors which I >> am >> > going to train a DNN for this dataset. >> > the sample data would be like a 140-D dimension vector. >> > Is it possible to train a CNN for this kind of data? I have >> > how to use convolution layers for this kind of data as kernels are >> e.g.5x5 >> > while the data is a vector? >> > >> > >> > If I make the data in a form of matrix, is it possible to train a >> pretrained >> > CNN e.g. alexnet for this dataset? >> > >> > Best >> > Majid >> > >> > -- >> > You received this message because you are subscribed to the Google >> Groups >> > "opencog" group. >> > To unsubscribe from this group and stop receiving emails from it, send >> an >> > email to [email protected]. >> > To post to this group, send email to [email protected]. >> > Visit this group at https://groups.google.com/group/opencog. >> > To view this discussion on the web visit >> > >> https://groups.google.com/d/msgid/opencog/1a04d763-3dfa-473f-a240-a0e452f6faba%40googlegroups.com. >> >> > For more options, visit https://groups.google.com/d/optout. >> >> >> >> -- >> Ben Goertzel, PhD >> http://goertzel.org >> >> "I am Ubik. Before the universe was, I am. I made the suns. I made the >> worlds. I created the lives and the places they inhabit; I move them >> here, I put them there. They go as I say, then do as I tell them. I am >> the word and my name is never spoken, the name which no one knows. I >> am called Ubik, but that is not my name. I am. I shall always be.” -- >> Ubik >> > -- > You received this message because you are subscribed to the Google Groups > "opencog" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to [email protected]. > To post to this group, send email to [email protected]. > Visit this group at https://groups.google.com/group/opencog. > To view this discussion on the web visit > https://groups.google.com/d/msgid/opencog/b3ca200e-c039-4417-96dc-5ef3f37f38ea%40googlegroups.com > <https://groups.google.com/d/msgid/opencog/b3ca200e-c039-4417-96dc-5ef3f37f38ea%40googlegroups.com?utm_medium=email&utm_source=footer> > . > > For more options, visit https://groups.google.com/d/optout. > -- You received this message because you are subscribed to the Google Groups "opencog" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. 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