Author: Robert Hyatt
Date: 06:58:44 05/02/00
Go up one level in this thread
On May 02, 2000 at 01:41:23, Christophe Theron wrote: >On May 02, 2000 at 00:15:44, Christophe Theron wrote: > >>On May 01, 2000 at 23:36:30, Robert Hyatt wrote: >> >>>On May 01, 2000 at 22:50:50, Christophe Theron wrote: >>> >>>>On May 01, 2000 at 21:59:05, Robert Hyatt wrote: >>>> >>>>>On May 01, 2000 at 20:39:13, Christophe Theron wrote: >>>>> >>>>>>On May 01, 2000 at 18:50:24, Robert Hyatt wrote: >>>>>> >>>>>>>On April 30, 2000 at 22:34:17, Christophe Theron wrote: >>>>>>> >>>>>>>>On April 30, 2000 at 18:42:57, Amir Ban wrote: >>>>>>>> >>>>>>>>>On April 30, 2000 at 16:39:04, Christophe Theron wrote: >>>>>>>>> >>>>>>>>>>On April 30, 2000 at 06:47:29, Amir Ban wrote: >>>>>>>>>> >>>>>>>>>>>On April 29, 2000 at 11:31:09, Eran wrote: >>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>>I am sorry if I said it. Okay I believe you that Junior6 has underpromotion code >>>>>>>>>>>>and that's wonderful. Maybe I will consider buying it. Does Junior6 consume >>>>>>>>>>>>hashtable memory as large as fritz does? Is having large hashtable memory >>>>>>>>>>>>important for Junior6? Is 40 MB hashtable enough for 40/120 games? >>>>>>>>>>>> >>>>>>>>>>>>Eran >>>>>>>>>>> >>>>>>>>>>>More memory for hash is good, but Junior is not very sensitive to it and you can >>>>>>>>>>>change memory size by order of magnitude without obvious effect on playing >>>>>>>>>>>strength. >>>>>>>>>>> >>>>>>>>>>>The comparison to Fritz is interesting and backward: I believe that Fritz 6 >>>>>>>>>>>(new) consumes less memory than previous versions and the reason may be a >>>>>>>>>>>conversation I had with Frans about this in Paderborn, from which he may have >>>>>>>>>>>decided that he doesn't need so much memory. >>>>>>>>>>> >>>>>>>>>>>Amir >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>>I must admit I don't understand what you say... >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> Christophe >>>>>>>>> >>>>>>>>> >>>>>>>>>Then why don't you ask :) >>>>>>>>> >>>>>>>>>I understood from Frans that he's hashing quiescence nodes. I told him I don't, >>>>>>>>>and that I consider it a waste of time. >>>>>>>>> >>>>>>>>>Amir >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>>I hash quies nodes. I tried both methods (hashing them and not hashing them) and >>>>>>>>found that hashing QSearch nodes was definitely better, but not by much. I did >>>>>>>>hours of experiments and drew a lot of curves with my spreadsheet in order to >>>>>>>>find this. >>>>>>>> >>>>>>>>It works better even if the hash table is highly saturated. >>>>>>>> >>>>>>>>That's how it works for me. I don't think that I have a better hashing/replacing >>>>>>>>strategy, actually it is rather simplistic. Maybe it's because I do more in >>>>>>>>QSearch than both of you do, although I cannot know for sure. >>>>>>>> >>>>>>>>Maybe I should check again... :) >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> Christophe >>>>>>> >>>>>>> >>>>>>>I used to hash q-search nodes. I found it reduced the size of the tree by about >>>>>>>10%. >>>>>> >>>>>> >>>>>> >>>>>>Yes, that's approximately what I measured. >>>>>> >>>>>> >>>>>> >>>>>> >>>>>>> I also found that by taking it out, I reduced the total search time by >>>>>>>15 %. A net gain of 5%. >>>>>> >>>>>> >>>>>> >>>>>>That's where I differ. Taking hash probe/updating out of QSearch results in a >>>>>>very marginal speed gain (in NPS) for me. >>>>>> >>>>>> >>>>>> >>>>>> >>>>>>> More importantly, I don't need nearly the hash memory >>>>>>>now since over half of the search is not hashed. >>>>>> >>>>>> >>>>>> >>>>>>I think there is no saving in memory. In QSearch, if the hash table slot is >>>>>>available, I use it and will maybe gain something, and if it's not available >>>>>>(because a more important information is stored) I end up not using the hash >>>>>>info in QSearch, as you do. >>>>>> >>>>>>Not hashing in QSearch means you are not taking advantage of all the memory you >>>>>>have to help you to decrease the size of your tree. It does not mean that you >>>>>>will need less memory. >>>>>> >>>>>>You do not take advantage of a resource you have, as your result of a 10% >>>>>>smaller tree when using HT in QSearch shows. >>>>>> >>>>>>Of course, the increased NPS in your case justifies your choice. >>>>>> >>>>>> >>>>>> >>>>>> >>>>>>>My next task is to save some time by getting rid of the hashing/unhashing code >>>>>>>in the q-search as well, since it isn't used... >>>>>> >>>>>> >>>>>>Yes, I thought of this possible speed gain. But then you have to write a special >>>>>>version of make/unmake that you will use exclusively in QSearch, and design >>>>>>another system to check for repetitions for example (as I assume that you use >>>>>>the hash key to detect repetitions). >>>>>> >>>>> >>>>> >>>>>Not possible in my q-search. I don't do checks. Only captures. As a result, >>>>>I don't do repetition checks at all since they can't possibly happen. >>>> >>>> >>>> >>>>I understand. So obviously you'll earn some extra % of speed by taking out the >>>>hashing code. >>>> >>>> >>>> >>>> >>>>>>This is maybe worth the effort for you, because you just generate captures in >>>>>>QSearch (that means no repetition can happen in QSearch). But my QSearch does >>>>>>more than that, so for me it's a real problem. My QSearch catches a lot of draws >>>>>>by repetition. >>>>> >>>>>I did this is CB. I am not sure it is the right thing to do, as several good >>>>>programs are using a restricted (or non-existant) capture search. Ferret is >>>>>one example, Junior is another. The benefit right now is that I use all the >>>>>extra time to extend in the basic search, where I generally encounter fewer >>>>>errors than in the q-search. >>>> >>>> >>>> >>>>The benefit of the kind of QSearch I do is not big. I do it mainly because I >>>>find it more elegant. >>>> >>>>If it was worse than doing a simplistic QSearch, I would not do it. But as it is >>>>not worse, I tend to use the most "elegant" approach (which is a matter of taste >>>>in this case). >>>> >>>> >>>> >>>> >>>>>>You also need to keep the hashing of pawn structures anyway, so all hashing will >>>>>>not be taken out of the QSearch make/unmake. >>>>> >>>>> >>>>>right... although that is (in my code) a 32 bit hash, rather than a 64 bit >>>>>one. >>>>> >>>>>> >>>>>> >>>>>>But I understand that not hashing in QSearch can be a good decision for some >>>>>>programs. >>>>>> >>>>>> >>>>>> >>>>>> Christophe >>>>> >>>>> >>>>>Bruce and I flipped and flopped on this one. I started off hashing, while I >>>>>don't think he did. Then I took it out but he added it. It is very close >>>>>to 'equal' in my code. And if it is equal, I prefer not doing it as it doesn't >>>>>stress memory nearly as much, and lets me do long searches without needing huge >>>>>hash tables... >>>> >>>> >>>> >>>>I understand the "stress memory" point. >>>> >>>>But I don't buy the "need less hash tables" argument. This is just not true. >>>> >>>>The hash table slots you fill during QSearch will never compete with the slots >>>>you fill during the "normal" search (I do not know how you call it). >>>> >>>>If you use the standard replacing scheme which uses the depth of computed >>>>subtree as the priority factor of a given slot, then it's obvious that a node >>>>computed in QSearch will never replace a node computed during the normal search, >>>>nor prevent a node computed in the normal search to replace it. >>>> >>>>By not sending QSearch nodes into the hash table, you are NOT allowing more >>>>space for the other nodes. You are just wasting memory space. I understand that >>>>the time saved in the process can justify it, but it's wrong to think that you >>>>are saving a single byte of memory. >>>> >>>> >>>> >>>> Christophe >>> >>> >>> >>>This is a well-known bug. If you use totally depth-prefered replacement, you >>>run into a big problem, in that the table (in a deep search) gets filled with >>>'deep positions' and the positions near the tips don't get stored. >> >> >> >>I guess that now you know which replacement strategy I'm using! :) >> >> >> >> >>> Yet they >>>are _critical_. My approach is the one used by Ken Thompson, that of using >>>two tables, one depth-prefered, one always-store. >> >> >> >>OK, so in this case you are right, storing QSearch nodes does reduce the space >>for the other nodes, in the "always replace" table at least. >> >> >> >> >>>Probably the best way to compare two programs is to do the following: Pick a >>>position, and search (using the same hardware) for 300 seconds, while varying >>>the hash table for both from something very small, to something very big. For >>>each program, you will find a point where the improvement slope drops sharply >>>and levels off. We need to test a q-search prober vs a non-q-search prober. >>> >>>I can run the test for Crafty if you want... and can run from something tiny >>>up to 384M max... >> >> >> >>I can do the same, but I'll not be able to use more than 16Mb of RAM, as my >>computer has only 32Mb. >> >>I have already done this kind of experiment and plotted the curves with a >>spreadsheet. Actually I have done the experiment with 2 versions of my own >>program: one using HT during QSearch, the other one not using HT during QSearch. >>The programs are otherwise totally identical. >> >>The experiment was not done on only one position, but on 40 positions. The >>positions were taken from 2 actual games (20 positions of the first game, 20 >>positions of the other game) played by an old version of Chess Tiger against >>Rebel Decade, one with white the other one with black. The positions are >>consecutive ones, and range from early middlegame to early endgame. >> >>The positions have all been searched to a fixed depth of 8 plies. The size of >>the hash table ranged from 32Kb to 8Mb. I have checked that the point where the >>slope of efficiency drops exists in this range of hash table sizes. >> >>The curves are interesting and show that, for my program, using the HT during >>QSearch is always better, and surprisingly is much more interesting when... the >>HT begins to be saturated!! > > > >Oops... Sorry, I'm tired. > >The above is complete bullshit. The curve of course shows the opposite: using HT >during QSearch is better when the HT is not saturated. > >Which is not surprising. > > > Christophe > > > > >>If you are interested, I can send you the small Excel4 file (or export it to >>text file). >> >>I would be interested in the curve that could be drawn from the experiment you >>are describing. >> >> >> Christophe I will run this test. I did it a couple of years ago and posted in r.g.c.c in response to a request by "Komputer Korner". The thing I worry about is this: for a hash table of size N, there is obviously a specific number of nodes that will fit in it at that size. Suppose you fill it by depth=12. What happens when you go to depth=20? The last 8 plies can't get in, as their depth is too low. And you will see the search die. And die badly, performance-wise. That is the purpose of the 'always-store' part. I have 1/3 of my table holding deep search results. And 2/3 of the table is always store so it holds the "local search" results. It is those way-deep searches that make this a problem... IE overnight, or at todays hardware speeds, even 10 minutes. I can easily hit 1M nps on my xeon. Using 384M of hash, I get about 24M hash entries. I can overrun that so fast it would be a joke. Even not hashing q-search sees this fill up in searches of < 1 minute...
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