Ty Linas! -> Am Dienstag, 2. August 2016 18:05:28 UTC+2 schrieb linas: > > 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 > > What I already understood is that it is related to PROLOG-trees but with real-truth values and attention values.
> 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. ) > > Ty for this precise technical information it will help me much by thinking about performance. > > 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. > I had visions like that. Its a problem of mapping. this will be solved more in praxis than in theorie. We will see what can be done... > > 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... > In the momend I am not so much familiar with caching technics. In my answer to Ben I wrote that maybe a SPARK-architecture could do it. As fare as I know and understand there is some room for cachecontrol... I think that after the current goals are achived - robots are doing well and good demos are there- than the project focus will come back to performance again. Maybe this will happen within about one year. If I get some help, from you and Ben , like I got here, from time to time - I hopefully will be there just in time! --Andi > > --linas > > > On Mon, Aug 1, 2016 at 3:26 PM, Andi <[email protected] <javascript:>> > 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] <javascript:>. >> To post to this group, send email to [email protected] >> <javascript:>. >> 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]. 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/83e143e6-be3e-4957-a7c4-0d8c7942faff%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
