bob wrote:I don't buy the "this hurts Rybka" idea, because the cluster rybka is a joke. And a poor joke at that. There have been some decent cluster-based programs. But Rybka is simply not one of them.
1)I think that it is impossible to know the algorithm rybka is using based on output from a single position.
It is possible that something similiar that is not exactly the same is used
when some illogical moves that lose the queen are analyzed but this is not all the story and the algorithm is based partly on "split only at the root" and partly on another idea.
2)I remember that Vas said 100 elo based on testing at fast time control and I suspect that at fast time control you get clearly more than 50 elo per doubling so practically 5 nodes do not give 4:1 speed improvement but clearly less than it(maybe 2.5:1).
Uri
The effective speedup is probably somewhere between 2.5:1 and 3:1 for 5 nodes, which is what Lukas had when he tested all of this.
Now he's up to 9 nodes BTW
Vas
Can we stay in the real world? Splitting at the root can not produce a 2.5x speedup, when the best move at the root takes _way_ over 50% of the total search time. There is theory. There is practice. And there is nonsense. For the event I am talking about, this claim is "nonsense". You might get the uninformed to buy this stuff, but not someone that has been doing it for 30+ years now (my first parallel search played its first ACM event in 1978....)
By effective speedup I mean the time handicap you could give to the original entity and score 50%. So, even if you do nothing other than split at the root and if the first move typically takes 50% of your search time, you could still get an effective speedup of >2. Not that that's what Rybka is doing
Vas
Here's what you need to make that happen.
(1) you need to change your mind at least once at the root during the last couple of iterations. More changes is better.
Sure. If you're not changing your mind, it doesn't matter what kind of speedup you have.
(2) you have to hope that the hash information from the first move does not affect any other move. Fine 70 is a good example of where this can be a problem.
That's just life without shared memory. Any cluster implementation is going to have problems in a position like that.
I think you'd be very lucky to get a speedup of 1.5x with any number of processors, which is not zero of course, but it is not something that will make me quake in my boots either.
With infinite # of processors and splitting only at the root, you will get a lot more than 1.5x.
Vas
No program today have search that is good enough but in theory
if you have a good search the speed up may be smaller from splitting at the root for the simple reason that the value of 1.5x speed improvement is bigger than the value of one ply with no pruning.
I think that
some type of bad evaluation also can cause the speed up to be smaller
or even negative.
Imagine that you search with no pruning and extensions and no qsearch and imagine that you have an evaluation that gives no bonus for the side to move so practically you often evaluate even depth as better than odd depths(maybe it should be the opposite and I did not think about it but the idea is the same)
If you split at the root you may get depth 7 for move A and depth 8 for move B and prefer move B not because it is better but only because you searched to even depth.
This problem does not happen without splitting at the root because without splitting at the root you always get the same depth for all moves.
bob wrote:
I _did_ an algorithm like that in 1983. I put it together in under 2 weeks and won the 1983 WCCC championship with it. And I _know_ how it works and how the PVs look.
Actually, I'd be curious to hear more about it. How did you split the root moves? Which moves got PV searches and which ones got scout (null-window) searches? What beta did you use for the scout searches?
I couldn't find anything useful in the public domain about splitting work without shared memory, except for the Cluster Toga algorithm, which wasn't really suitable for Rybka (among other things, it doesn't apply to analysis and would be useless for users).
Vas
What we did back then was more dynamic that that. I had the root move list sorted just as I do today (previous iteration node count). Processor 1 searched the first move with a normal aspiration window. Processor 2 did the same. When one of them reported back with a new best move, the alpha/beta value was shipped to the other processor, which would take a short "break", update all the bounds, and then check to see if anything will now cutoff where it didn't when we started. We then searched the root moves one at a time, whenever a processor finished a move it grabbed another (dynamic load balancing) until the list was gone.
We always did an aspiration-type search in CB, where the root window was very narrow, something like +/- 0.25 pawns, for the first move. At the time we were using PVS (this was something from the late 70's and was used by belle and ourselves as well as others) which is (I assume) what you mean by "scout" (which is not quite the same as PVS but close enough for discussion).
I should add that both the univac 1100 and Cray were both shared-memory. I'm looking at cluster-based search right now because of this 70 node 8-core cluster we have. I can search about 20M nodes per sec on one node, which gives a potential for something faster than deep blue (we could possibly hit 1400M nodes per second although there are great issues to overcome. But Hsu and I did talk a lot about his parallel search and I like his two-level approach (split at early plies among the nodes, each node splits at late plies among the local processors)...
Aha, thanks. Like many things, this won't work without shared memory. Without shared memory, if 'processor A' (to use your terminology) takes moves x at some iteration, it needs to take the same move at the next iteration as well.
Basically, without shared memory, you want to have the same units processing the same parts of the tree throughout the entire search. I agree with Hsu's comment - without shared memory, work needs to be split at or near the root.
Vas
ps. I use the term "scout search" to refer to any search with a zero-width window.
That's what I thought (scout search).
The way I am looking at splitting on a cluster is to use a "split hash table". I store the hash signature and node ID when I split, so that the same node searches the same sub-tree each time to take advantage of the hash, killers, etc. There are issues with this as the next iteration can greatly alter the shape of the tree and give one node a set of moves that are much harder to search than others, producing an imbalance. I'm working on sorting out the details for this kind of stuff as the code gets written...
I'm working on the "two-level" approach, although I am also considering the more complicated issues encountered on a cluster that has NUMA (AMD) nodes. Because you also have issues on a single node dealing with trying to maximize local memory accesses...
bob wrote:I don't buy the "this hurts Rybka" idea, because the cluster rybka is a joke. And a poor joke at that. There have been some decent cluster-based programs. But Rybka is simply not one of them.
1)I think that it is impossible to know the algorithm rybka is using based on output from a single position.
It is possible that something similiar that is not exactly the same is used
when some illogical moves that lose the queen are analyzed but this is not all the story and the algorithm is based partly on "split only at the root" and partly on another idea.
2)I remember that Vas said 100 elo based on testing at fast time control and I suspect that at fast time control you get clearly more than 50 elo per doubling so practically 5 nodes do not give 4:1 speed improvement but clearly less than it(maybe 2.5:1).
Uri
The effective speedup is probably somewhere between 2.5:1 and 3:1 for 5 nodes, which is what Lukas had when he tested all of this.
Now he's up to 9 nodes BTW
Vas
Can we stay in the real world? Splitting at the root can not produce a 2.5x speedup, when the best move at the root takes _way_ over 50% of the total search time. There is theory. There is practice. And there is nonsense. For the event I am talking about, this claim is "nonsense". You might get the uninformed to buy this stuff, but not someone that has been doing it for 30+ years now (my first parallel search played its first ACM event in 1978....)
By effective speedup I mean the time handicap you could give to the original entity and score 50%. So, even if you do nothing other than split at the root and if the first move typically takes 50% of your search time, you could still get an effective speedup of >2. Not that that's what Rybka is doing
Vas
Here's what you need to make that happen.
(1) you need to change your mind at least once at the root during the last couple of iterations. More changes is better.
(2) you have to hope that the hash information from the first move does not affect any other move. Fine 70 is a good example of where this can be a problem.
If both hold, you can get a speedup of N, where N is the number of times you actually change the best move at the root. Typically that is none in roughly 80% of the searches according to analysis on the "deep projects" from years ago, "Crafty goes deep" and then Heinz's "DarkThought goes deeper" or whatever it was called. So with luck, 20% of time time (actually more like 15%) you will change your mind at the root once, and get a 2x speedup. 80% of the time you get no speedup to speak of.
The bad part of that is that the more nodes / processors you add, you get nothing for them unless you change your mind at least once for each node so that all those moves have to be searched completely with no early exits due to cutoffs.
In 1978 when we started this, our branching factor was closer to 10 than to 2. So we potentially got more then than now. Today with EBF hovering around 2.0 or so, the first move takes >50% of the time. Which means all you can speed up is that remaining 50% which is not going to accomplish much, performance-wise. For example, one processor takes 20 minutes to complete a search. Two will take 15 minutes. Four will take 12.5 minutes. An infinite number could complete the search in 10 minutes minimum, and that is a stretch to assume all of those branches would produce trees no larger than the first move, which is unlikely.
I think you'd be very lucky to get a speedup of 1.5x with any number of processors, which is not zero of course, but it is not something that will make me quake in my boots either.
What you can achieve with infinite number of processors by splitting at the root is one ply and one ply can be equivalent to more than speed improvement of 2:1
IF_AND_ONLY_IF the condition I gave is satisfied, that is you change your mind at the root. Otherwise you can never beat 2x in normal positions. This is basic alpha/beta math.
The branching factor is irrelevant because
one ply+ searching to depth n may be practically better than searching to depth n+1 because searching to depth n+1 may have more pruning.
Who cares? The basic idea is to continue to use the same search strategy you already use. Otherwise you are admitting that your current search paradigm is not optimal. And you ought to fix that _first_ before you try to implement a poor search algorithm as a parallel search algorithm.
practically the depth is not constant but you can try by yourself to test version A against version B when version A is using 2 seconds per move and version B is searching for 1 second after every root move and see who wins(version B should use something like 100 processors to be practically sure that the number of root moves is smaller than the number of processors but the interesting question is if version B can win the match or not).
If it wins then you can increase the time of version A to see
the effective speed up when version A score near 50% against version B.
Again, you can not average 2x faster splitting at the root. You might hit 2x, or on very rare occasions something beyond 2x. But you will _not_ average anywhere near 2x. And you will do well to average 1.5 x which is what we got back in the days where the effective branching factor was closer to 10, which makes parallel speedup easier to get.
bob wrote:I don't buy the "this hurts Rybka" idea, because the cluster rybka is a joke. And a poor joke at that. There have been some decent cluster-based programs. But Rybka is simply not one of them.
1)I think that it is impossible to know the algorithm rybka is using based on output from a single position.
It is possible that something similiar that is not exactly the same is used
when some illogical moves that lose the queen are analyzed but this is not all the story and the algorithm is based partly on "split only at the root" and partly on another idea.
2)I remember that Vas said 100 elo based on testing at fast time control and I suspect that at fast time control you get clearly more than 50 elo per doubling so practically 5 nodes do not give 4:1 speed improvement but clearly less than it(maybe 2.5:1).
Uri
The effective speedup is probably somewhere between 2.5:1 and 3:1 for 5 nodes, which is what Lukas had when he tested all of this.
Now he's up to 9 nodes BTW
Vas
Can we stay in the real world? Splitting at the root can not produce a 2.5x speedup, when the best move at the root takes _way_ over 50% of the total search time. There is theory. There is practice. And there is nonsense. For the event I am talking about, this claim is "nonsense". You might get the uninformed to buy this stuff, but not someone that has been doing it for 30+ years now (my first parallel search played its first ACM event in 1978....)
By effective speedup I mean the time handicap you could give to the original entity and score 50%. So, even if you do nothing other than split at the root and if the first move typically takes 50% of your search time, you could still get an effective speedup of >2. Not that that's what Rybka is doing
Vas
Here's what you need to make that happen.
(1) you need to change your mind at least once at the root during the last couple of iterations. More changes is better.
(2) you have to hope that the hash information from the first move does not affect any other move. Fine 70 is a good example of where this can be a problem.
If both hold, you can get a speedup of N, where N is the number of times you actually change the best move at the root. Typically that is none in roughly 80% of the searches according to analysis on the "deep projects" from years ago, "Crafty goes deep" and then Heinz's "DarkThought goes deeper" or whatever it was called. So with luck, 20% of time time (actually more like 15%) you will change your mind at the root once, and get a 2x speedup. 80% of the time you get no speedup to speak of.
The bad part of that is that the more nodes / processors you add, you get nothing for them unless you change your mind at least once for each node so that all those moves have to be searched completely with no early exits due to cutoffs.
In 1978 when we started this, our branching factor was closer to 10 than to 2. So we potentially got more then than now. Today with EBF hovering around 2.0 or so, the first move takes >50% of the time. Which means all you can speed up is that remaining 50% which is not going to accomplish much, performance-wise. For example, one processor takes 20 minutes to complete a search. Two will take 15 minutes. Four will take 12.5 minutes. An infinite number could complete the search in 10 minutes minimum, and that is a stretch to assume all of those branches would produce trees no larger than the first move, which is unlikely.
I think you'd be very lucky to get a speedup of 1.5x with any number of processors, which is not zero of course, but it is not something that will make me quake in my boots either.
What you can achieve with infinite number of processors by splitting at the root is one ply and one ply can be equivalent to more than speed improvement of 2:1
The branching factor is irrelevant because
one ply+ searching to depth n may be practically better than searching to depth n+1 because searching to depth n+1 may have more pruning.
The question is what is the effective speed difference between searching to depth n and searching to depth n+1 when you do not prune after root moves(not by null move and not by late move reductions).
practically the depth is not constant but you can try by yourself to test version A against version B when version A is using 2 seconds per move and version B is searching for 1 second after every root move and see who wins(version B should use something like 100 processors to be practically sure that the number of root moves is smaller than the number of processors but the interesting question is if version B can win the match or not).
If it wins then you can increase the time of version A to see
the effective speed up when version A score near 50% against version B.
Uri
That's right. The way to say this is that having to work without shared memory is going to have a big impact on the proper shape of your search tree.
Vas
However, that is contrary to what Uri is trying to claim. Distributed (cluster) search is, by its very nature, going to be far worse than a search on a shared memory machine, whether you split at the root or anywhere else, because hash hits are going way down, and the hash table will not be able to graft tree sections from one search to another since the hash is not globally visible.
In fact, most cluster approaches in the past have done a global hash table as well (see Feldman, etc) for this very reason, accepting the huge hash access latency this produces.
bob wrote:I don't buy the "this hurts Rybka" idea, because the cluster rybka is a joke. And a poor joke at that. There have been some decent cluster-based programs. But Rybka is simply not one of them.
1)I think that it is impossible to know the algorithm rybka is using based on output from a single position.
It is possible that something similiar that is not exactly the same is used
when some illogical moves that lose the queen are analyzed but this is not all the story and the algorithm is based partly on "split only at the root" and partly on another idea.
2)I remember that Vas said 100 elo based on testing at fast time control and I suspect that at fast time control you get clearly more than 50 elo per doubling so practically 5 nodes do not give 4:1 speed improvement but clearly less than it(maybe 2.5:1).
Uri
The effective speedup is probably somewhere between 2.5:1 and 3:1 for 5 nodes, which is what Lukas had when he tested all of this.
Now he's up to 9 nodes BTW
Vas
Can we stay in the real world? Splitting at the root can not produce a 2.5x speedup, when the best move at the root takes _way_ over 50% of the total search time. There is theory. There is practice. And there is nonsense. For the event I am talking about, this claim is "nonsense". You might get the uninformed to buy this stuff, but not someone that has been doing it for 30+ years now (my first parallel search played its first ACM event in 1978....)
By effective speedup I mean the time handicap you could give to the original entity and score 50%. So, even if you do nothing other than split at the root and if the first move typically takes 50% of your search time, you could still get an effective speedup of >2. Not that that's what Rybka is doing
Vas
Here's what you need to make that happen.
(1) you need to change your mind at least once at the root during the last couple of iterations. More changes is better.
Sure. If you're not changing your mind, it doesn't matter what kind of speedup you have.
(2) you have to hope that the hash information from the first move does not affect any other move. Fine 70 is a good example of where this can be a problem.
That's just life without shared memory. Any cluster implementation is going to have problems in a position like that.
I think you'd be very lucky to get a speedup of 1.5x with any number of processors, which is not zero of course, but it is not something that will make me quake in my boots either.
With infinite # of processors and splitting only at the root, you will get a lot more than 1.5x.
Vas
Sorry, but no you won't. The math is simple and comes directly from Amdahl's law and the original alpha/beta analysis by knuth and moore, followed by the parallel analysis I did in the Journal of parallel computing. The first move takes 50% of the time. And that is going to be done on one processor. if you can somehow solve the "window" problem, so that you can search the remainder of the ply-1 moves in parallel with a correct alpha/beta window, then you can shrink those to the time required to search one of them. But you have no alpha/beta window, so all moves searched in parallel (ignoring the first move) are going to take about as long as the first move (each one will take that long) because you do _not_ yet have the alpha bound from the first move to let you get the quick cutoffs on the rest of the move.
Best case is 2x faster, since you could assume that any root move takes as long as any other when you do not have a good alpha bound. And what you can get "peak" is not going to be what you get "on average". >2x is just not going to happen except for rare cases. I have a position somewhere where you can get a 20x speedup like that. First move takes forever to find a very deep mate. Second move is a shorter mate in 1/20th the total nodes searched. Searching both at the same time finds the second mate before the first has even been "sniffed". But that is simply an exception. For the general case, 2x is the very best you can hope for, and it is not going to happen often...
I can give you a citation for the paper I wrote that uses the math from Knuth / Moore and extends it to cover alpha/beta in parallel. It is easy enough to understand and explains exactly why >2x is a rare case, not the norm...
bob wrote:I don't buy the "this hurts Rybka" idea, because the cluster rybka is a joke. And a poor joke at that. There have been some decent cluster-based programs. But Rybka is simply not one of them.
1)I think that it is impossible to know the algorithm rybka is using based on output from a single position.
It is possible that something similiar that is not exactly the same is used
when some illogical moves that lose the queen are analyzed but this is not all the story and the algorithm is based partly on "split only at the root" and partly on another idea.
2)I remember that Vas said 100 elo based on testing at fast time control and I suspect that at fast time control you get clearly more than 50 elo per doubling so practically 5 nodes do not give 4:1 speed improvement but clearly less than it(maybe 2.5:1).
Uri
The effective speedup is probably somewhere between 2.5:1 and 3:1 for 5 nodes, which is what Lukas had when he tested all of this.
Now he's up to 9 nodes BTW
Vas
Can we stay in the real world? Splitting at the root can not produce a 2.5x speedup, when the best move at the root takes _way_ over 50% of the total search time. There is theory. There is practice. And there is nonsense. For the event I am talking about, this claim is "nonsense". You might get the uninformed to buy this stuff, but not someone that has been doing it for 30+ years now (my first parallel search played its first ACM event in 1978....)
By effective speedup I mean the time handicap you could give to the original entity and score 50%. So, even if you do nothing other than split at the root and if the first move typically takes 50% of your search time, you could still get an effective speedup of >2. Not that that's what Rybka is doing
Vas
Here's what you need to make that happen.
(1) you need to change your mind at least once at the root during the last couple of iterations. More changes is better.
Sure. If you're not changing your mind, it doesn't matter what kind of speedup you have.
(2) you have to hope that the hash information from the first move does not affect any other move. Fine 70 is a good example of where this can be a problem.
That's just life without shared memory. Any cluster implementation is going to have problems in a position like that.
I think you'd be very lucky to get a speedup of 1.5x with any number of processors, which is not zero of course, but it is not something that will make me quake in my boots either.
With infinite # of processors and splitting only at the root, you will get a lot more than 1.5x.
Vas
No program today have search that is good enough but in theory
if you have a good search the speed up may be smaller from splitting at the root for the simple reason that the value of 1.5x speed improvement is bigger than the value of one ply with no pruning.
I think that
some type of bad evaluation also can cause the speed up to be smaller
or even negative.
Imagine that you search with no pruning and extensions and no qsearch and imagine that you have an evaluation that gives no bonus for the side to move so practically you often evaluate even depth as better than odd depths(maybe it should be the opposite and I did not think about it but the idea is the same)
If you split at the root you may get depth 7 for move A and depth 8 for move B and prefer move B not because it is better but only because you searched to even depth.
This problem does not happen without splitting at the root because without splitting at the root you always get the same depth for all moves.
Uri
You are badly misusing terminology.
1. "splitting at the root" does not mean each root move gets searched to different depth. I split at the root in current crafty. that only means that I do a parallel search on the root moves as well as deeper moves, because this is a far more efficient way to search.
2. You are using what is called "unsynchronized search" where each node searched a different move (or set of moves) at the root, and when iteration N is done, N+1 is started without regard for how the other moves are progressing on other nodes.
this is useless.
How can you choose between depth 21, eval=+1.3, and depth 19, eval +2.5?? You can't. This has been tried in the past, by Newborn, by Schaeffer, and by others. There is no way to compute any sort of equivalence function so that you can decide which of the above is better. The depth 19 move might be even higher at depth 21. Or it might be way lower. The only way to discover this is to search both moves to the same depth. Anything else is beyond hopeless and is a coin flip. You can't even choose between depth 21, +1.5, and depth 19, -2.0, because by depth 21 the -2.0 score might be +5.0...
bob wrote:I don't buy the "this hurts Rybka" idea, because the cluster rybka is a joke. And a poor joke at that. There have been some decent cluster-based programs. But Rybka is simply not one of them.
1)I think that it is impossible to know the algorithm rybka is using based on output from a single position.
It is possible that something similiar that is not exactly the same is used
when some illogical moves that lose the queen are analyzed but this is not all the story and the algorithm is based partly on "split only at the root" and partly on another idea.
2)I remember that Vas said 100 elo based on testing at fast time control and I suspect that at fast time control you get clearly more than 50 elo per doubling so practically 5 nodes do not give 4:1 speed improvement but clearly less than it(maybe 2.5:1).
Uri
The effective speedup is probably somewhere between 2.5:1 and 3:1 for 5 nodes, which is what Lukas had when he tested all of this.
Now he's up to 9 nodes BTW
Vas
Can we stay in the real world? Splitting at the root can not produce a 2.5x speedup, when the best move at the root takes _way_ over 50% of the total search time. There is theory. There is practice. And there is nonsense. For the event I am talking about, this claim is "nonsense". You might get the uninformed to buy this stuff, but not someone that has been doing it for 30+ years now (my first parallel search played its first ACM event in 1978....)
By effective speedup I mean the time handicap you could give to the original entity and score 50%. So, even if you do nothing other than split at the root and if the first move typically takes 50% of your search time, you could still get an effective speedup of >2. Not that that's what Rybka is doing
Vas
Here's what you need to make that happen.
(1) you need to change your mind at least once at the root during the last couple of iterations. More changes is better.
(2) you have to hope that the hash information from the first move does not affect any other move. Fine 70 is a good example of where this can be a problem.
If both hold, you can get a speedup of N, where N is the number of times you actually change the best move at the root. Typically that is none in roughly 80% of the searches according to analysis on the "deep projects" from years ago, "Crafty goes deep" and then Heinz's "DarkThought goes deeper" or whatever it was called. So with luck, 20% of time time (actually more like 15%) you will change your mind at the root once, and get a 2x speedup. 80% of the time you get no speedup to speak of.
The bad part of that is that the more nodes / processors you add, you get nothing for them unless you change your mind at least once for each node so that all those moves have to be searched completely with no early exits due to cutoffs.
In 1978 when we started this, our branching factor was closer to 10 than to 2. So we potentially got more then than now. Today with EBF hovering around 2.0 or so, the first move takes >50% of the time. Which means all you can speed up is that remaining 50% which is not going to accomplish much, performance-wise. For example, one processor takes 20 minutes to complete a search. Two will take 15 minutes. Four will take 12.5 minutes. An infinite number could complete the search in 10 minutes minimum, and that is a stretch to assume all of those branches would produce trees no larger than the first move, which is unlikely.
I think you'd be very lucky to get a speedup of 1.5x with any number of processors, which is not zero of course, but it is not something that will make me quake in my boots either.
What you can achieve with infinite number of processors by splitting at the root is one ply and one ply can be equivalent to more than speed improvement of 2:1
IF_AND_ONLY_IF the condition I gave is satisfied, that is you change your mind at the root. Otherwise you can never beat 2x in normal positions. This is basic alpha/beta math.
The branching factor is irrelevant because
one ply+ searching to depth n may be practically better than searching to depth n+1 because searching to depth n+1 may have more pruning.
Who cares? The basic idea is to continue to use the same search strategy you already use. Otherwise you are admitting that your current search paradigm is not optimal. And you ought to fix that _first_ before you try to implement a poor search algorithm as a parallel search algorithm.
practically the depth is not constant but you can try by yourself to test version A against version B when version A is using 2 seconds per move and version B is searching for 1 second after every root move and see who wins(version B should use something like 100 processors to be practically sure that the number of root moves is smaller than the number of processors but the interesting question is if version B can win the match or not).
If it wins then you can increase the time of version A to see
the effective speed up when version A score near 50% against version B.
Again, you can not average 2x faster splitting at the root. You might hit 2x, or on very rare occasions something beyond 2x. But you will _not_ average anywhere near 2x. And you will do well to average 1.5 x which is what we got back in the days where the effective branching factor was closer to 10, which makes parallel speedup easier to get.
Uri
I see no reason to continue to use the same search with a cluster when I admit that my algorithm with a cluster is not optimal but I have no time to design optimal algorithm with a cluster.
Not using the same search is not admitting that my search is not optimal without a cluster because the conditions are different.
Without a cluster not pruning after the first ply may cause me to be too slow and get smaller depth.
When I use a cluster with many nodes(let say 100 nodes to allow me to do full search of the root moves in all practical cases) I get the extra ply for free.
You can say that it is possible to do better with a cluster and you are right but I do not claim that speed improvement that you get by splitting at the root is the best that you can get by a cluster but only
that I see no reason to claim that speed improvement of more than 2:1 by splitting at the root is theoretically impossible.
I also see no reason that the hash cannot be saved after splitting at the root(if I understand correctly this was suggested in this thread).
one node analyzed the right move and this node has hash information.
I guess that it may be possible to copy the hash information of this node to all the nodes and use it in the next search(there is obviously connection between the nodes otherwise they cannot work together).
bob wrote:I don't buy the "this hurts Rybka" idea, because the cluster rybka is a joke. And a poor joke at that. There have been some decent cluster-based programs. But Rybka is simply not one of them.
1)I think that it is impossible to know the algorithm rybka is using based on output from a single position.
It is possible that something similiar that is not exactly the same is used
when some illogical moves that lose the queen are analyzed but this is not all the story and the algorithm is based partly on "split only at the root" and partly on another idea.
2)I remember that Vas said 100 elo based on testing at fast time control and I suspect that at fast time control you get clearly more than 50 elo per doubling so practically 5 nodes do not give 4:1 speed improvement but clearly less than it(maybe 2.5:1).
Uri
The effective speedup is probably somewhere between 2.5:1 and 3:1 for 5 nodes, which is what Lukas had when he tested all of this.
Now he's up to 9 nodes BTW
Vas
Can we stay in the real world? Splitting at the root can not produce a 2.5x speedup, when the best move at the root takes _way_ over 50% of the total search time. There is theory. There is practice. And there is nonsense. For the event I am talking about, this claim is "nonsense". You might get the uninformed to buy this stuff, but not someone that has been doing it for 30+ years now (my first parallel search played its first ACM event in 1978....)
By effective speedup I mean the time handicap you could give to the original entity and score 50%. So, even if you do nothing other than split at the root and if the first move typically takes 50% of your search time, you could still get an effective speedup of >2. Not that that's what Rybka is doing
Vas
Here's what you need to make that happen.
(1) you need to change your mind at least once at the root during the last couple of iterations. More changes is better.
Sure. If you're not changing your mind, it doesn't matter what kind of speedup you have.
(2) you have to hope that the hash information from the first move does not affect any other move. Fine 70 is a good example of where this can be a problem.
That's just life without shared memory. Any cluster implementation is going to have problems in a position like that.
I think you'd be very lucky to get a speedup of 1.5x with any number of processors, which is not zero of course, but it is not something that will make me quake in my boots either.
With infinite # of processors and splitting only at the root, you will get a lot more than 1.5x.
Vas
No program today have search that is good enough but in theory
if you have a good search the speed up may be smaller from splitting at the root for the simple reason that the value of 1.5x speed improvement is bigger than the value of one ply with no pruning.
I think that
some type of bad evaluation also can cause the speed up to be smaller
or even negative.
Imagine that you search with no pruning and extensions and no qsearch and imagine that you have an evaluation that gives no bonus for the side to move so practically you often evaluate even depth as better than odd depths(maybe it should be the opposite and I did not think about it but the idea is the same)
If you split at the root you may get depth 7 for move A and depth 8 for move B and prefer move B not because it is better but only because you searched to even depth.
This problem does not happen without splitting at the root because without splitting at the root you always get the same depth for all moves.
Uri
You are badly misusing terminology.
1. "splitting at the root" does not mean each root move gets searched to different depth. I split at the root in current crafty. that only means that I do a parallel search on the root moves as well as deeper moves, because this is a far more efficient way to search.
2. You are using what is called "unsynchronized search" where each node searched a different move (or set of moves) at the root, and when iteration N is done, N+1 is started without regard for how the other moves are progressing on other nodes.
this is useless.
How can you choose between depth 21, eval=+1.3, and depth 19, eval +2.5?? You can't. This has been tried in the past, by Newborn, by Schaeffer, and by others. There is no way to compute any sort of equivalence function so that you can decide which of the above is better. The depth 19 move might be even higher at depth 21. Or it might be way lower. The only way to discover this is to search both moves to the same depth. Anything else is beyond hopeless and is a coin flip. You can't even choose between depth 21, +1.5, and depth 19, -2.0, because by depth 21 the -2.0 score might be +5.0...
I can certainly choose and the simplest choice is to choose higher score is better.
depth 19 eval=+2.5 is better than depth 21 eval=+1.3 for the simple reason that 2.5>1.3
I believe that with relatively good evaluation it is not so bad choice(not that splitting at the root is good but it may give effective speed improvement of 2 or more than 2 assuming that all root moves are searched).
I may be wrong and the only way to know is by testing.
How can you choose between depth 21, eval=+1.3, and depth 19, eval +2.5?? You can't. This has been tried in the past, by Newborn, by Schaeffer, and by others. There is no way to compute any sort of equivalence function so that you can decide which of the above is better. The depth 19 move might be even higher at depth 21. Or it might be way lower. The only way to discover this is to search both moves to the same depth. Anything else is beyond hopeless and is a coin flip. You can't even choose between depth 21, +1.5, and depth 19, -2.0, because by depth 21 the -2.0 score might be +5.0...
By the same logic, you can't choose between depth 20, eval=+1.3, and depth 20, eval +2.5. If there is not a very high correlation between scores at ply n and ply n+1, standard computer searches could not work at all. I parsed some log files and came up with a correlation coefficient of 0.995.
bob wrote:I don't buy the "this hurts Rybka" idea, because the cluster rybka is a joke. And a poor joke at that. There have been some decent cluster-based programs. But Rybka is simply not one of them.
1)I think that it is impossible to know the algorithm rybka is using based on output from a single position.
It is possible that something similiar that is not exactly the same is used
when some illogical moves that lose the queen are analyzed but this is not all the story and the algorithm is based partly on "split only at the root" and partly on another idea.
2)I remember that Vas said 100 elo based on testing at fast time control and I suspect that at fast time control you get clearly more than 50 elo per doubling so practically 5 nodes do not give 4:1 speed improvement but clearly less than it(maybe 2.5:1).
Uri
The effective speedup is probably somewhere between 2.5:1 and 3:1 for 5 nodes, which is what Lukas had when he tested all of this.
Now he's up to 9 nodes BTW
Vas
Can we stay in the real world? Splitting at the root can not produce a 2.5x speedup, when the best move at the root takes _way_ over 50% of the total search time. There is theory. There is practice. And there is nonsense. For the event I am talking about, this claim is "nonsense". You might get the uninformed to buy this stuff, but not someone that has been doing it for 30+ years now (my first parallel search played its first ACM event in 1978....)
By effective speedup I mean the time handicap you could give to the original entity and score 50%. So, even if you do nothing other than split at the root and if the first move typically takes 50% of your search time, you could still get an effective speedup of >2. Not that that's what Rybka is doing
Vas
Here's what you need to make that happen.
(1) you need to change your mind at least once at the root during the last couple of iterations. More changes is better.
Sure. If you're not changing your mind, it doesn't matter what kind of speedup you have.
(2) you have to hope that the hash information from the first move does not affect any other move. Fine 70 is a good example of where this can be a problem.
That's just life without shared memory. Any cluster implementation is going to have problems in a position like that.
I think you'd be very lucky to get a speedup of 1.5x with any number of processors, which is not zero of course, but it is not something that will make me quake in my boots either.
With infinite # of processors and splitting only at the root, you will get a lot more than 1.5x.
Vas
No program today have search that is good enough but in theory
if you have a good search the speed up may be smaller from splitting at the root for the simple reason that the value of 1.5x speed improvement is bigger than the value of one ply with no pruning.
I think that
some type of bad evaluation also can cause the speed up to be smaller
or even negative.
Imagine that you search with no pruning and extensions and no qsearch and imagine that you have an evaluation that gives no bonus for the side to move so practically you often evaluate even depth as better than odd depths(maybe it should be the opposite and I did not think about it but the idea is the same)
If you split at the root you may get depth 7 for move A and depth 8 for move B and prefer move B not because it is better but only because you searched to even depth.
This problem does not happen without splitting at the root because without splitting at the root you always get the same depth for all moves.
Uri
You are badly misusing terminology.
1. "splitting at the root" does not mean each root move gets searched to different depth. I split at the root in current crafty. that only means that I do a parallel search on the root moves as well as deeper moves, because this is a far more efficient way to search.
2. You are using what is called "unsynchronized search" where each node searched a different move (or set of moves) at the root, and when iteration N is done, N+1 is started without regard for how the other moves are progressing on other nodes.
this is useless.
How can you choose between depth 21, eval=+1.3, and depth 19, eval +2.5?? You can't. This has been tried in the past, by Newborn, by Schaeffer, and by others. There is no way to compute any sort of equivalence function so that you can decide which of the above is better. The depth 19 move might be even higher at depth 21. Or it might be way lower. The only way to discover this is to search both moves to the same depth. Anything else is beyond hopeless and is a coin flip. You can't even choose between depth 21, +1.5, and depth 19, -2.0, because by depth 21 the -2.0 score might be +5.0...
I can certainly choose and the simplest choice is to choose higher score is better.
depth 19 eval=+2.5 is better than depth 21 eval=+1.3 for the simple reason that 2.5>1.3
I believe that with relatively good evaluation it is not so bad choice(not that splitting at the root is good but it may give effective speed improvement of 2 or more than 2 assuming that all root moves are searched).
I may be wrong and the only way to know is by testing.
Uri
There is absolutely no doubt you are wrong. How many times have you seen a program search a move to depth N with a + score, then at depth n+1 the score drops off the map and it switches to a new move? If you believe what you wrote, why not stop your search when the eval gets to a good point? Why continue to search deeper?
As far as a speedup of > 2, it simply will not/can not happen. Amdahl's law says that the overall speedup for an algorithm is:
speedup = time(N) / T(P)
T(P) = sequential_processing_time + parallel_processing_time / N
where N = number of processors.
If the first move takes 50% of the time, and that is the usual case, then you absolutely can not get a speedup > 2.0. It is impossible. Except for those cases where the first move is not best. Now you search the second move. And it takes the same amount of time. So you do get a bigger boost there. But that only happens about 15% of the time. 85% of the time the speedup is going to be way below 2.0 no matter how many CPUs you use. And there is simply no way to escape the basic sequential property of the alpha/beta algorithm.
