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Groups > comp.lang.prolog > #14753 > unrolled thread
| Started by | Mild Shock <janburse@fastmail.fm> |
|---|---|
| First post | 2025-08-04 02:52 +0200 |
| Last post | 2025-11-28 15:12 +0100 |
| Articles | 2 on this page of 22 — 1 participant |
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Mercio’s Algorithm for Rational Tree Compare in Prolog Mild Shock <janburse@fastmail.fm> - 2025-08-04 02:52 +0200
The Original Ganster (OG) of Gameification: IEEE 1044.1-1995 (Was: Mercio’s Algorithm for Rational Tree Compare in Prolog) Mild Shock <janburse@fastmail.fm> - 2025-08-04 13:48 +0200
The Bitrot called Math Stack Exchange (Re: The Original Ganster (OG) of Gameification: IEEE 1044.1-1995) Mild Shock <janburse@fastmail.fm> - 2025-08-04 14:00 +0200
I guess its back to Hopcroft and Karp.Re: The Bitrot called Math Stack Exchange) Mild Shock <janburse@fastmail.fm> - 2025-08-04 14:11 +0200
Szpilrajn Theorem and Suzumura Consistency (Was: Mercio’s Algorithm for Rational Tree Compare in Prolog) Mild Shock <janburse@fastmail.fm> - 2025-08-06 01:50 +0200
The good thing is we have at least Mercio’s Algorithm (Re: Szpilrajn Theorem and Suzumura Consistency) Mild Shock <janburse@fastmail.fm> - 2025-08-06 08:10 +0200
Hopcroft and Karp’s is just Contraction (Was: The good thing is we have at least Mercio’s Algorithm) Mild Shock <janburse@fastmail.fm> - 2025-08-06 08:13 +0200
Re: Hopcroft and Karp’s is just Contraction (Was: The good thing is we have at least Mercio’s Algorithm) Mild Shock <janburse@fastmail.fm> - 2025-08-06 08:23 +0200
Should we use minimum_coa/3 ? (Was: The good thing is we have at least Mercio’s Algorithm) Mild Shock <janburse@fastmail.fm> - 2025-08-08 23:45 +0200
Perfectly balanced , as all things should be! (Was: Should we use minimum_coa/3 ?) Mild Shock <janburse@fastmail.fm> - 2025-08-08 23:49 +0200
Mercios decidability was already attested in 2012 (Was: Mercio’s Algorithm for Rational Tree Compare in Prolog) Mild Shock <janburse@fastmail.fm> - 2025-08-14 20:26 +0200
Performance of Mercio’s Total Order (Was: Mercios decidability was already attested in 2012) Mild Shock <janburse@fastmail.fm> - 2025-08-15 23:49 +0200
Fuzzy Testing is your Swiss Knife (Re: Performance of Mercio’s Total Order) Mild Shock <janburse@fastmail.fm> - 2025-08-15 23:55 +0200
Yeah, we have another name! (Was: Fuzzy Testing is your Swiss Knife) Mild Shock <janburse@fastmail.fm> - 2025-08-16 12:38 +0200
Monte Carlo sampling the frontier version (Was: Yeah, we have another name!) Mild Shock <janburse@fastmail.fm> - 2025-08-16 12:42 +0200
AI most hated by formal verification (Was: Fuzzy Testing is your Swiss Knife) Mild Shock <janburse@fastmail.fm> - 2025-11-26 17:02 +0100
An NPU could give 1000x more LIPS (Was: Mercio’s Algorithm for Rational Tree Compare in Prolog) Mild Shock <janburse@fastmail.fm> - 2025-11-27 14:19 +0100
Neural Network based dif/2 respectively (#\=)/2 (Re: An NPU could give 1000x more LIPS) Mild Shock <janburse@fastmail.fm> - 2025-11-27 14:51 +0100
Zeus: A Language for Expressing Algorithms in Hardware (Re: Neural Network based dif/2 respectively (#\=)/2) Mild Shock <janburse@fastmail.fm> - 2025-11-27 15:03 +0100
Googles TPU muscle in 2017 [Prolog Community is Sleepy Joe] (Re: Zeus: A Language for Expressing Algorithms in Hardware) Mild Shock <janburse@fastmail.fm> - 2025-11-27 15:23 +0100
100% serious Giga Logical Inferences per Second (GLIPS) (Was: An NPU could give 1000x more LIPS) Mild Shock <janburse@fastmail.fm> - 2025-11-28 14:50 +0100
The tables have turned: GigaLIP on the Laptop? (Re: 100% serious Giga Logical Inferences per Second (GLIPS) Mild Shock <janburse@fastmail.fm> - 2025-11-28 15:12 +0100
Page 2 of 2 — ← Prev page 1 [2]
| From | Mild Shock <janburse@fastmail.fm> |
|---|---|
| Date | 2025-11-28 14:50 +0100 |
| Subject | 100% serious Giga Logical Inferences per Second (GLIPS) (Was: An NPU could give 1000x more LIPS) |
| Message-ID | <10gc9bm$oenf$1@solani.org> |
| In reply to | #15052 |
Hi, I am 100% serious about Giga Logical Inferences per Second (GLIPS). Leaving behind the sequential constraint solving world: The Complexity of Constraint Satisfaction Revisited https://www.cs.ubc.ca/~mack/Publications/AIP93.pdf Only I have missed the deep learning bandwagon, never programmed with PyTorch or Keras. So even for the banal problem of coding some ReLU networks and shipping them to a GPU or NPU, or a hybrid, I don't have much experience. So I am marveling at papers such as: Learning Variable Ordering Heuristics for Solving Constraint Satisfaction Problems https://arxiv.org/abs/1912.10762 Given that the AI Boom started after 2019, the above paper is already old, and it has currious antique terminology like Multilayer Perceptron, which is not so common anymore? It does also more than what I want to demonstrate, it does also do policy learning. Bye Mild Shock schrieb: > Hi, > > I am spekulating an NPU could give 1000x more LIPS. > For certain combinatorial search problems. It all > boils down to implement this thingy: > > In June 2020, Stockfish introduced the efficiently > updatable neural network (NNUE) approach, based > on earlier work by computer shogi programmers > https://en.wikipedia.org/wiki/Stockfish_%28chess%29 > > There are varying degrees what gets updated of > a neural network. But the specs of an NPU tell > me very simply the following: > > - An NPU can make 40 TFLOPS, all my AI Laptops > from 2025 can do that right now. The brands > are Intel Ultra, AMD Ryzen and Snapdragon X, > > but I guess there might be more brands around, > which can do that with a price tag less > than 1000.- USD. > > - SWI Prolog can make 30 MLIPS, Dogelog Player > runs similar, some Prolog systems are faster. > > Now thats is 10^12 versus 10^6. If some of the > LIPS can be delegated to a NPU, and if we assume > for example less locality or more primitive > > operations that require a layering. Would could assume > that from the NPU 10^12 a factor of 1000 goes > away. So we might still see 10'9 LIPS emerge. > > Now make the calculation: > > - Without NPU: MLIPS > - With NPU: GLIPS > - Ratio: 1000x times faster > > Have fun! > > Bye > > Mild Shock schrieb: >> Mercio’s Algorithm (2012) for Rational >> Tree Compare is specified here mathematically. >> It is based on computing truncations A' = (A_0, >> A_1, etc..) of a rational tree A: >> >> A < B ⟺ A′ <_lex B′ >> >> https://math.stackexchange.com/a/210730 >> >> Here is an implementation in Prolog. >> First the truncation: >> >> trunc(_, T, T) :- var(T), !. >> trunc(0, T, F) :- !, functor(T, F, _). >> trunc(N, T, S) :- >> M is N-1, >> T =.. [F|L], >> maplist(trunc(M), L, R), >> S =.. [F|R]. >> >> And then the iterative deepening: >> >> mercio(N, X, Y, C) :- >> trunc(N, X, A), >> trunc(N, Y, B), >> compare(D, A, B), >> D \== (=), !, C = D. >> mercio(N, X, Y, C) :- >> M is N + 1, >> mercio(M, X, Y, C). >> >> The main entry first uses (==)/2 for a >> terminating equality check and if the >> rational trees are not equal, falls back >> to the iterative deepening: >> >> mercio(C, X, Y) :- X == Y, !, C = (=). >> mercio(C, X, Y) :- mercio(0, X, Y, C). >> >> I couldn’t find yet a triple that violates >> transitivity. But I am also not much happy >> with the code. Looks a little bit expensive >> to create a truncation copy iteratively. >> >> Provided there is really no counter example, >> maybe we can do mit more smart and faster? It >> might also stand the test of conservativity? >
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| From | Mild Shock <janburse@fastmail.fm> |
|---|---|
| Date | 2025-11-28 15:12 +0100 |
| Subject | The tables have turned: GigaLIP on the Laptop? (Re: 100% serious Giga Logical Inferences per Second (GLIPS) |
| Message-ID | <10gcakd$ofi3$2@solani.org> |
| In reply to | #15057 |
Hi, Notably we try to do something with AI laptops that came out in 2025. Tapping into their NPU. Back in the late 80's GigaLIP were rather hypothetical, small machines could hardly do KLIPs. Today small machines do easily MLIPs. But hardly any popular Prolog systems already taps into the AI Boom, they are all Sleepy Joes. Not to mention populate by morons like Boris the Loris and Nazi Retard Julio. They simply cannot connect the dots. Bye P.S.: Nice travel to the past is this paper: Is a GigaLIP Fast Enough? TOM W. KELLER - December 1988 DOI: 10.1007/BF00436711 Mild Shock schrieb: > Hi, > > I am 100% serious about Giga Logical Inferences > per Second (GLIPS). Leaving behind the sequential > constraint solving world: > > The Complexity of Constraint Satisfaction Revisited > https://www.cs.ubc.ca/~mack/Publications/AIP93.pdf > > Only I have missed the deep learning bandwagon, > never programmed with PyTorch or Keras. So even > for the banal problem of coding some > > ReLU networks and shipping them to a GPU or NPU, > or a hybrid, I don't have much experience. So > I am marveling at papers such as: > > Learning Variable Ordering Heuristics > for Solving Constraint Satisfaction Problems > https://arxiv.org/abs/1912.10762 > > Given that the AI Boom started after 2019, > the above paper is already old, and it has > currious antique terminology like Multilayer > > Perceptron, which is not so common anymore? > It does also more than what I want to demonstrate, > it does also do policy learning. > > Bye > > Mild Shock schrieb: >> Hi, >> >> I am spekulating an NPU could give 1000x more LIPS. >> For certain combinatorial search problems. It all >> boils down to implement this thingy: >> >> In June 2020, Stockfish introduced the efficiently >> updatable neural network (NNUE) approach, based >> on earlier work by computer shogi programmers >> https://en.wikipedia.org/wiki/Stockfish_%28chess%29 >> >> There are varying degrees what gets updated of >> a neural network. But the specs of an NPU tell >> me very simply the following: >> >> - An NPU can make 40 TFLOPS, all my AI Laptops >> from 2025 can do that right now. The brands >> are Intel Ultra, AMD Ryzen and Snapdragon X, >> >> but I guess there might be more brands around, >> which can do that with a price tag less >> than 1000.- USD. >> >> - SWI Prolog can make 30 MLIPS, Dogelog Player >> runs similar, some Prolog systems are faster. >> >> Now thats is 10^12 versus 10^6. If some of the >> LIPS can be delegated to a NPU, and if we assume >> for example less locality or more primitive >> >> operations that require a layering. Would could assume >> that from the NPU 10^12 a factor of 1000 goes >> away. So we might still see 10'9 LIPS emerge. >> >> Now make the calculation: >> >> - Without NPU: MLIPS >> - With NPU: GLIPS >> - Ratio: 1000x times faster >> >> Have fun!
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