Yes, I saw them but they are partially implemented. Your approach was always the SOTA (state-of-the-art) method, so it's not an impartial environment, which is what I'm referring to. The final Rckangaroo should be able to compare all methods equitably with all its advantages; that would be a fair and rigorous test.

I wasn't referring to you when I mentioned 'theories with zero code'; I'm talking about those who give opinions without having contributed any code, commit, or fork, saying 'this would be more efficient.' And yes, I just want to test your final version with all the methods and see it for myself. Rest assured, if the SOTA (state-of-the-art) method is better, I will say it. The truth cannot be hidden.

I want to clarify again: it's not my goal to publish the fastest version of ECDLP solver I have, it's not RCKangaroo.
My goal is to share the best kangaroo method for solving ECDLP with proofs on CPU so you can learn/use it. And I have done it.
RCKangaroo is a "Proof of Concept" of SOTA method for GPU (however, it's fastest in public) and I give it for free with sources so you can use and improve it.
If someone thinks that I must do even more and publish some ultimate software for cracking #135 - it's funny

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#135 takes about 5.6 more calculations than #130, so I think #135 is the last high puzzle that will be solved in this decade.


About zero code, may be people mean this your post where you promise to show something? It's ok that you changed your mind

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Ok, let's keep all our achievements secret, it will be a great progress for science
Take care, I will come back when something interesting happens, for example, if someone publishes a method with K lower than mine.

[moderator's note: consecutive posts merged]

Thanks for this quick and optimized Kangaroo program, it is the fastest.

Is it possible to have the "save work" option and "load work" from file like in JLP's Kangaroo?

Thanks

Hey RetiredCoder,

I'm the guy who broke 67. Trying to DM you but I can't as a newbie.
Can you please DM me ?

Thanks

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Good Morning RetiredCoder:

I have been studying the puzzles for a year now.  I was able to comunicate with professor Teske and professor Galbraith, they both worked with professor Pollard, and pointed me in the right direction. I have some questions and some original ideas about the pollard methods. I would like to ask if you can give me some advice.

I believe the Pollard methods can be improved using this observations I have made:

The key to accelerate the Pollard method computations is to improve the efficiency of the inversion part.

Teske in her paper says it is ok to use 32 types of jumps to give enough randomness. What about using more jump types, let´s say 2**32 types of jumps? Then you select every jump by the first 32 bits of the X coordinate. You store the X and Y in a database, which index is the first 32 bits of X. Now the jump formula considers the 32 most significant bits of the actual point, and adds the corresponding point in the database, with the same 32 bits .

This has the advantage that X2-X1 for the inversion, can be selected to give a number of only 224 Bits instead of 256. When you calculate the batch inversion, let's say you make 400 inversions, you can multiply a 256 bit number times a 224 bit number for the partial product part.

I also have made an algorithm for batch inversion using instead a pairwise multiplication, where I think you could improve the efficiency a little bit more, because you will begin making 224 bit*224 bit multiplications. 

Does this make any sense? Will the big database search at every jump make the program slower vs the speedup of the multiplication of smaller numbers?

Thanks for your time.

  Thank's for your answer.

I have other question:
I have studied bitcrack and kangaroo (From Jean-Luc Pons) in detail. In the Pollard method with only 2 heards, Pollard recommends using a jump formula of powers of 2. When I tried to understand the Van-Oorschot method of parallelization, I realized that if you double the amount of Kangaroos, you must double the jump size mean. This results in huge jumps for a big range and a big number of kangaroos if you use powers of 2. Do you recommend better selecting the jump size using random K's in a given interval?

Thanks

I understand that you use a base magnitude for the jumps and add a random portion to make the jumps, in a different way for each of the 3 heards, I will study this in detail. I'm not able to find an implementation for batch inversion on your code, do you use batch inversion to speed up the computation of the inverses? I understand that in the Classic Pollard method, you can use this approach, computing the next point for a group kangaroo paths. This will make the cost for one inversion only 3 - 4 multiplications? Is that correct?

Hello everyone, can you tell me how to run 135 puzzle on several machines? Something like a pool or hashtopolis server