@RetiredCoder, do You plan any other minipuzzles (or maxi) in the future? Let us know "when", if You plan something, please 
I don't plan them in advance, but anyway here is another one:
https://bitcointalksearch.org/topic/--5526453
Topic: Solving ECDLP with Kangaroos: Part 1 + 2 + RCKangaroo - page 2. (Read 7214 times)
I don't plan them in advance, but anyway here is another one:
https://bitcointalksearch.org/topic/--5526453
If one really plans to break 135 there are much more factors in play in order to determine the optimal parameters to minimize the total runtime, and I'm not only talking about the command line parameters, but the kernel code itself. RC's software is just a proof of concept about his strategy, it doesn't take a genius to figure out his actual code is totally different and most likely heavily more optimized so that it fits well with his algorithm.
Yes RCKangaroo is not heavily optimized and it's actually just a proof of concept to demonstrate that K=1.15(0.99) is possible in practice, but even well-optimized version is not even twice more faster, so it's not so bad.
As for DP overhead, from the table above it may look like DP=45 is a good idea
but of course it's not, as you have to use at least hundreds of GPUs for #135 and this table must be recalculated for the number of GPUs you use. Hey mjojo,
I created a table using what I recommended to do, to play around with DP values.
https://i.ibb.co/MV9M9yk/DP-values.png
You can see the values of using different DPs.
I created a table using what I recommended to do, to play around with DP values.
https://i.ibb.co/MV9M9yk/DP-values.png
You can see the values of using different DPs.
DP ovh also depends on the total # of kangaroos. And the total # of kangaroos depends on how many GPUs you use and how many kangs / GPU (you may have different GPU specs, some more powerful, some with less or more kangaroos to increase throughput to maximum).
Why? Simple, let's say you have 1000 GPUs all running at once, each jumping 1 million kangs.
So even if one lucky GPU jumps one lucky kangaroo to a DP that is later found to be a collision, there were still 999.999.999 kangaroos that were also each jumped the same amount of times as the lucky kangaroo, on the lucky GPU and on the other 999 GPUs.
If one really plans to break 135 there are much more factors in play in order to determine the optimal parameters to minimize the total runtime, and I'm not only talking about the command line parameters, but the kernel code itself. RC's software is just a proof of concept about his strategy, it doesn't take a genius to figure out his actual code is totally different and most likely heavily more optimized so that it fits well with his algorithm.
@kTimesG thank you for the explanation
Hey mjojo,
I created a table using what I recommended to do, to play around with DP values.
You can see the values of using different DPs.
I created a table using what I recommended to do, to play around with DP values.
You can see the values of using different DPs.
DP ovh also depends on the total # of kangaroos. And the total # of kangaroos depends on how many GPUs you use and how many kangs / GPU (you may have different GPU specs, some more powerful, some with less or more kangaroos to increase throughput to maximum).
Why? Simple, let's say you have 1000 GPUs all running at once, each jumping 1 million kangs.
So even if one lucky GPU jumps one lucky kangaroo to a DP that is later found to be a collision, there were still 999.999.999 kangaroos that were also each jumped the same amount of times as the lucky kangaroo, on the lucky GPU and on the other 999 GPUs.
If one really plans to break 135 there are much more factors in play in order to determine the optimal parameters to minimize the total runtime, and I'm not only talking about the command line parameters, but the kernel code itself. RC's software is just a proof of concept about his strategy, it doesn't take a genius to figure out his actual code is totally different and most likely heavily more optimized so that it fits well with his algorithm.
Hi @albertajuelo,
Would you elaborate again for your explanation below about DP.
2) To understand DPs and how they affect. First you need to know what a DP is: Distinguished points: a point is a distinguished point if its representation exhibits a certain bit pattern, e.g., has the top 20 bits equal to zero.
You have to know that you have a number of X kangaroos that make Y jumps every second.
Now if we use a DP of Z bits, that means that depending on the Z, that will be the average chance that a kangaroo will find a point that has that DP.
The higher the Z, the harder it will be to find that DP.
Now I recommend that for you to learn better in a practical way, you play with if I use a very low DP, what happens to the memory? How many points do I store?
Same if I use a very high DP.
Thank you.
Would you elaborate again for your explanation below about DP.
2) To understand DPs and how they affect. First you need to know what a DP is: Distinguished points: a point is a distinguished point if its representation exhibits a certain bit pattern, e.g., has the top 20 bits equal to zero.
You have to know that you have a number of X kangaroos that make Y jumps every second.
Now if we use a DP of Z bits, that means that depending on the Z, that will be the average chance that a kangaroo will find a point that has that DP.
The higher the Z, the harder it will be to find that DP.
Now I recommend that for you to learn better in a practical way, you play with if I use a very low DP, what happens to the memory? How many points do I store?
Same if I use a very high DP.
Thank you.
Hey mjojo,
I created a table using what I recommended to do, to play around with DP values.
https://i.ibb.co/MV9M9yk/DP-values.png
You can see the values of using different DPs.
@albertajuelo thank you, may I talking with you in private, still confuse about DP.
Hi @albertajuelo,
Would you elaborate again for your explanation below about DP.
2) To understand DPs and how they affect. First you need to know what a DP is: Distinguished points: a point is a distinguished point if its representation exhibits a certain bit pattern, e.g., has the top 20 bits equal to zero.
You have to know that you have a number of X kangaroos that make Y jumps every second.
Now if we use a DP of Z bits, that means that depending on the Z, that will be the average chance that a kangaroo will find a point that has that DP.
The higher the Z, the harder it will be to find that DP.
Now I recommend that for you to learn better in a practical way, you play with if I use a very low DP, what happens to the memory? How many points do I store?
Same if I use a very high DP.
Thank you.
Would you elaborate again for your explanation below about DP.
2) To understand DPs and how they affect. First you need to know what a DP is: Distinguished points: a point is a distinguished point if its representation exhibits a certain bit pattern, e.g., has the top 20 bits equal to zero.
You have to know that you have a number of X kangaroos that make Y jumps every second.
Now if we use a DP of Z bits, that means that depending on the Z, that will be the average chance that a kangaroo will find a point that has that DP.
The higher the Z, the harder it will be to find that DP.
Now I recommend that for you to learn better in a practical way, you play with if I use a very low DP, what happens to the memory? How many points do I store?
Same if I use a very high DP.
Thank you.
Hey mjojo,
I created a table using what I recommended to do, to play around with DP values.
https://i.ibb.co/MV9M9yk/DP-values.png
You can see the values of using different DPs.
Hi everyone. Thank to RC for this software.
I just run the exe and get very low speeds for my 3080. Where can be the problem? You can also see that speed is increasing gradually.
After keeping benchmark mode running for a day I saw results like this, but they were not consistent. Some of the test were done with good speeds but some were very slow. I am not sure what it depends on. I use Windows 11.
I just run the exe and get very low speeds for my 3080. Where can be the problem? You can also see that speed is increasing gradually.
Code:
********************************************************************************
* RCKangaroo v3.0 (c) 2024 RetiredCoder *
********************************************************************************
This software is free and open-source: https://github.com/RetiredC
It demonstrates fast GPU implementation of SOTA Kangaroo method for solving ECDLP
Windows version
CUDA devices: 1, CUDA driver/runtime: 12.7/12.6
GPU 0: NVIDIA GeForce RTX 3080, 10.00 GB, 68 CUs, cap 8.6, PCI 65, L2 size: 5120 KB
Total GPUs for work: 1
BENCHMARK MODE
Solving point: Range 78 bits, DP 16, start...
SOTA method, estimated ops: 2^39.202, RAM for DPs: 0.547 GB. DP and GPU overheads not included!
Estimated DPs per kangaroo: 4.329. DP overhead is big, use less DP value if possible!
GPU 0: allocated 6557 MB, 2228224 kangaroos. OldGpuMode: Yes
GPUs started...
BENCH: Speed: 19 MKeys/s, Err: 0, DPs: 33K/9646K, Time: 0d:00h:00m/0d:09h:14m
BENCH: Speed: 64 MKeys/s, Err: 0, DPs: 101K/9646K, Time: 0d:00h:00m/0d:02h:44m
BENCH: Speed: 86 MKeys/s, Err: 0, DPs: 134K/9646K, Time: 0d:00h:00m/0d:02h:02m
BENCH: Speed: 131 MKeys/s, Err: 0, DPs: 202K/9646K, Time: 0d:00h:00m/0d:01h:20m
BENCH: Speed: 154 MKeys/s, Err: 0, DPs: 236K/9646K, Time: 0d:00h:00m/0d:01h:08m
BENCH: Speed: 197 MKeys/s, Err: 0, DPs: 305K/9646K, Time: 0d:00h:01m/0d:00h:53m
BENCH: Speed: 242 MKeys/s, Err: 0, DPs: 372K/9646K, Time: 0d:00h:01m/0d:00h:43m
BENCH: Speed: 265 MKeys/s, Err: 0, DPs: 406K/9646K, Time: 0d:00h:01m/0d:00h:39m
BENCH: Speed: 309 MKeys/s, Err: 0, DPs: 475K/9646K, Time: 0d:00h:01m/0d:00h:34m
BENCH: Speed: 331 MKeys/s, Err: 0, DPs: 508K/9646K, Time: 0d:00h:01m/0d:00h:31m
BENCH: Speed: 357 MKeys/s, Err: 0, DPs: 576K/9646K, Time: 0d:00h:01m/0d:00h:29m
BENCH: Speed: 357 MKeys/s, Err: 0, DPs: 644K/9646K, Time: 0d:00h:02m/0d:00h:29m
BENCH: Speed: 357 MKeys/s, Err: 0, DPs: 678K/9646K, Time: 0d:00h:02m/0d:00h:29m
BENCH: Speed: 356 MKeys/s, Err: 0, DPs: 746K/9646K, Time: 0d:00h:02m/0d:00h:29m
BENCH: Speed: 355 MKeys/s, Err: 0, DPs: 779K/9646K, Time: 0d:00h:02m/0d:00h:29m
BENCH: Speed: 356 MKeys/s, Err: 0, DPs: 848K/9646K, Time: 0d:00h:02m/0d:00h:29m
BENCH: Speed: 356 MKeys/s, Err: 0, DPs: 915K/9646K, Time: 0d:00h:02m/0d:00h:29m
BENCH: Speed: 356 MKeys/s, Err: 0, DPs: 949K/9646K, Time: 0d:00h:03m/0d:00h:29m
* RCKangaroo v3.0 (c) 2024 RetiredCoder *
********************************************************************************
This software is free and open-source: https://github.com/RetiredC
It demonstrates fast GPU implementation of SOTA Kangaroo method for solving ECDLP
Windows version
CUDA devices: 1, CUDA driver/runtime: 12.7/12.6
GPU 0: NVIDIA GeForce RTX 3080, 10.00 GB, 68 CUs, cap 8.6, PCI 65, L2 size: 5120 KB
Total GPUs for work: 1
BENCHMARK MODE
Solving point: Range 78 bits, DP 16, start...
SOTA method, estimated ops: 2^39.202, RAM for DPs: 0.547 GB. DP and GPU overheads not included!
Estimated DPs per kangaroo: 4.329. DP overhead is big, use less DP value if possible!
GPU 0: allocated 6557 MB, 2228224 kangaroos. OldGpuMode: Yes
GPUs started...
BENCH: Speed: 19 MKeys/s, Err: 0, DPs: 33K/9646K, Time: 0d:00h:00m/0d:09h:14m
BENCH: Speed: 64 MKeys/s, Err: 0, DPs: 101K/9646K, Time: 0d:00h:00m/0d:02h:44m
BENCH: Speed: 86 MKeys/s, Err: 0, DPs: 134K/9646K, Time: 0d:00h:00m/0d:02h:02m
BENCH: Speed: 131 MKeys/s, Err: 0, DPs: 202K/9646K, Time: 0d:00h:00m/0d:01h:20m
BENCH: Speed: 154 MKeys/s, Err: 0, DPs: 236K/9646K, Time: 0d:00h:00m/0d:01h:08m
BENCH: Speed: 197 MKeys/s, Err: 0, DPs: 305K/9646K, Time: 0d:00h:01m/0d:00h:53m
BENCH: Speed: 242 MKeys/s, Err: 0, DPs: 372K/9646K, Time: 0d:00h:01m/0d:00h:43m
BENCH: Speed: 265 MKeys/s, Err: 0, DPs: 406K/9646K, Time: 0d:00h:01m/0d:00h:39m
BENCH: Speed: 309 MKeys/s, Err: 0, DPs: 475K/9646K, Time: 0d:00h:01m/0d:00h:34m
BENCH: Speed: 331 MKeys/s, Err: 0, DPs: 508K/9646K, Time: 0d:00h:01m/0d:00h:31m
BENCH: Speed: 357 MKeys/s, Err: 0, DPs: 576K/9646K, Time: 0d:00h:01m/0d:00h:29m
BENCH: Speed: 357 MKeys/s, Err: 0, DPs: 644K/9646K, Time: 0d:00h:02m/0d:00h:29m
BENCH: Speed: 357 MKeys/s, Err: 0, DPs: 678K/9646K, Time: 0d:00h:02m/0d:00h:29m
BENCH: Speed: 356 MKeys/s, Err: 0, DPs: 746K/9646K, Time: 0d:00h:02m/0d:00h:29m
BENCH: Speed: 355 MKeys/s, Err: 0, DPs: 779K/9646K, Time: 0d:00h:02m/0d:00h:29m
BENCH: Speed: 356 MKeys/s, Err: 0, DPs: 848K/9646K, Time: 0d:00h:02m/0d:00h:29m
BENCH: Speed: 356 MKeys/s, Err: 0, DPs: 915K/9646K, Time: 0d:00h:02m/0d:00h:29m
BENCH: Speed: 356 MKeys/s, Err: 0, DPs: 949K/9646K, Time: 0d:00h:03m/0d:00h:29m
After keeping benchmark mode running for a day I saw results like this, but they were not consistent. Some of the test were done with good speeds but some were very slow. I am not sure what it depends on. I use Windows 11.
Code:
Solving point: Range 78 bits, DP 16, start...
SOTA method, estimated ops: 2^39.202, RAM for DPs: 0.547 GB. DP and GPU overheads not included!
Estimated DPs per kangaroo: 4.329. DP overhead is big, use less DP value if possible!
GPU 0: allocated 6557 MB, 2228224 kangaroos. OldGpuMode: Yes
GPUs started...
BENCH: Speed: 1224 MKeys/s, Err: 0, DPs: 305K/9646K, Time: 0d:00h:00m/0d:00h:08m
BENCH: Speed: 2261 MKeys/s, Err: 0, DPs: 645K/9646K, Time: 0d:00h:00m/0d:00h:04m
BENCH: Speed: 2261 MKeys/s, Err: 0, DPs: 985K/9646K, Time: 0d:00h:00m/0d:00h:04m
BENCH: Speed: 2258 MKeys/s, Err: 0, DPs: 1358K/9646K, Time: 0d:00h:00m/0d:00h:04m
BENCH: Speed: 2261 MKeys/s, Err: 0, DPs: 1699K/9646K, Time: 0d:00h:00m/0d:00h:04m
BENCH: Speed: 2261 MKeys/s, Err: 0, DPs: 2038K/9646K, Time: 0d:00h:01m/0d:00h:04m
BENCH: Speed: 2258 MKeys/s, Err: 0, DPs: 2378K/9646K, Time: 0d:00h:01m/0d:00h:04m
BENCH: Speed: 2261 MKeys/s, Err: 0, DPs: 2718K/9646K, Time: 0d:00h:01m/0d:00h:04m
BENCH: Speed: 2256 MKeys/s, Err: 0, DPs: 3056K/9646K, Time: 0d:00h:01m/0d:00h:04m
BENCH: Speed: 2258 MKeys/s, Err: 0, DPs: 3430K/9646K, Time: 0d:00h:01m/0d:00h:04m
BENCH: Speed: 2259 MKeys/s, Err: 0, DPs: 3770K/9646K, Time: 0d:00h:01m/0d:00h:04m
BENCH: Speed: 2256 MKeys/s, Err: 0, DPs: 4110K/9646K, Time: 0d:00h:02m/0d:00h:04m
BENCH: Speed: 2256 MKeys/s, Err: 0, DPs: 4450K/9646K, Time: 0d:00h:02m/0d:00h:04m
BENCH: Speed: 2258 MKeys/s, Err: 0, DPs: 4791K/9646K, Time: 0d:00h:02m/0d:00h:04m
BENCH: Speed: 2261 MKeys/s, Err: 0, DPs: 5131K/9646K, Time: 0d:00h:02m/0d:00h:04m
BENCH: Speed: 2261 MKeys/s, Err: 0, DPs: 5471K/9646K, Time: 0d:00h:02m/0d:00h:04m
BENCH: Speed: 2258 MKeys/s, Err: 0, DPs: 5846K/9646K, Time: 0d:00h:02m/0d:00h:04m
BENCH: Speed: 2250 MKeys/s, Err: 0, DPs: 6186K/9646K, Time: 0d:00h:03m/0d:00h:04m
BENCH: Speed: 2193 MKeys/s, Err: 0, DPs: 6492K/9646K, Time: 0d:00h:03m/0d:00h:04m
BENCH: Speed: 2185 MKeys/s, Err: 0, DPs: 6831K/9646K, Time: 0d:00h:03m/0d:00h:04m
Stopping work ...
Point solved, K: 0.823 (with DP and GPU overheads)
Points solved: 124, average K: 1.354 (with DP and GPU overheads)
SOTA method, estimated ops: 2^39.202, RAM for DPs: 0.547 GB. DP and GPU overheads not included!
Estimated DPs per kangaroo: 4.329. DP overhead is big, use less DP value if possible!
GPU 0: allocated 6557 MB, 2228224 kangaroos. OldGpuMode: Yes
GPUs started...
BENCH: Speed: 1224 MKeys/s, Err: 0, DPs: 305K/9646K, Time: 0d:00h:00m/0d:00h:08m
BENCH: Speed: 2261 MKeys/s, Err: 0, DPs: 645K/9646K, Time: 0d:00h:00m/0d:00h:04m
BENCH: Speed: 2261 MKeys/s, Err: 0, DPs: 985K/9646K, Time: 0d:00h:00m/0d:00h:04m
BENCH: Speed: 2258 MKeys/s, Err: 0, DPs: 1358K/9646K, Time: 0d:00h:00m/0d:00h:04m
BENCH: Speed: 2261 MKeys/s, Err: 0, DPs: 1699K/9646K, Time: 0d:00h:00m/0d:00h:04m
BENCH: Speed: 2261 MKeys/s, Err: 0, DPs: 2038K/9646K, Time: 0d:00h:01m/0d:00h:04m
BENCH: Speed: 2258 MKeys/s, Err: 0, DPs: 2378K/9646K, Time: 0d:00h:01m/0d:00h:04m
BENCH: Speed: 2261 MKeys/s, Err: 0, DPs: 2718K/9646K, Time: 0d:00h:01m/0d:00h:04m
BENCH: Speed: 2256 MKeys/s, Err: 0, DPs: 3056K/9646K, Time: 0d:00h:01m/0d:00h:04m
BENCH: Speed: 2258 MKeys/s, Err: 0, DPs: 3430K/9646K, Time: 0d:00h:01m/0d:00h:04m
BENCH: Speed: 2259 MKeys/s, Err: 0, DPs: 3770K/9646K, Time: 0d:00h:01m/0d:00h:04m
BENCH: Speed: 2256 MKeys/s, Err: 0, DPs: 4110K/9646K, Time: 0d:00h:02m/0d:00h:04m
BENCH: Speed: 2256 MKeys/s, Err: 0, DPs: 4450K/9646K, Time: 0d:00h:02m/0d:00h:04m
BENCH: Speed: 2258 MKeys/s, Err: 0, DPs: 4791K/9646K, Time: 0d:00h:02m/0d:00h:04m
BENCH: Speed: 2261 MKeys/s, Err: 0, DPs: 5131K/9646K, Time: 0d:00h:02m/0d:00h:04m
BENCH: Speed: 2261 MKeys/s, Err: 0, DPs: 5471K/9646K, Time: 0d:00h:02m/0d:00h:04m
BENCH: Speed: 2258 MKeys/s, Err: 0, DPs: 5846K/9646K, Time: 0d:00h:02m/0d:00h:04m
BENCH: Speed: 2250 MKeys/s, Err: 0, DPs: 6186K/9646K, Time: 0d:00h:03m/0d:00h:04m
BENCH: Speed: 2193 MKeys/s, Err: 0, DPs: 6492K/9646K, Time: 0d:00h:03m/0d:00h:04m
BENCH: Speed: 2185 MKeys/s, Err: 0, DPs: 6831K/9646K, Time: 0d:00h:03m/0d:00h:04m
Stopping work ...
Point solved, K: 0.823 (with DP and GPU overheads)
Points solved: 124, average K: 1.354 (with DP and GPU overheads)
Hello, won't there be a version for red cards (amd)?
Hi @albertajuelo,
Would you elaborate again for your explanation below about DP.
2) To understand DPs and how they affect. First you need to know what a DP is: Distinguished points: a point is a distinguished point if its representation exhibits a certain bit pattern, e.g., has the top 20 bits equal to zero.
You have to know that you have a number of X kangaroos that make Y jumps every second.
Now if we use a DP of Z bits, that means that depending on the Z, that will be the average chance that a kangaroo will find a point that has that DP.
The higher the Z, the harder it will be to find that DP.
Now I recommend that for you to learn better in a practical way, you play with if I use a very low DP, what happens to the memory? How many points do I store?
Same if I use a very high DP.
Thank you.
Would you elaborate again for your explanation below about DP.
2) To understand DPs and how they affect. First you need to know what a DP is: Distinguished points: a point is a distinguished point if its representation exhibits a certain bit pattern, e.g., has the top 20 bits equal to zero.
You have to know that you have a number of X kangaroos that make Y jumps every second.
Now if we use a DP of Z bits, that means that depending on the Z, that will be the average chance that a kangaroo will find a point that has that DP.
The higher the Z, the harder it will be to find that DP.
Now I recommend that for you to learn better in a practical way, you play with if I use a very low DP, what happens to the memory? How many points do I store?
Same if I use a very high DP.
Thank you.
not at all. did you manage to assemble the code?
I finally managed to compile the VanitySearch and Kangaroo projects by Jean-Luc with the version he uploaded on GitHub. It's much easier now! I hope he'll update his projects to allow us to randomly define a search interval. If you're here, Jean-Luc, we need you! (As for Satoshi, we'll save you for last, haha!)
Best regards,
P.S. A big thank you to Satoshi, even though I lost all my 10 BTC back in 2017 Cry. That’s why I want to try my luck with Jean Luc’s code.
PFR ==> bc1qltyqxw94nynyj9nq8kqfvzuxjrwejd6vrdvhlm
Hi,
this is what i was looking for and just came across!
can you please share your code?
Thanks
not at all. did you manage to assemble the code?
I finally managed to compile the VanitySearch and Kangaroo projects by Jean-Luc with the version he uploaded on GitHub. It's much easier now! I hope he'll update his projects to allow us to randomly define a search interval. If you're here, Jean-Luc, we need you! (As for Satoshi, we'll save you for last, haha!)
Best regards,
P.S. A big thank you to Satoshi, even though I lost all my 10 BTC back in 2017 Cry. That’s why I want to try my luck with Jean Luc’s code.
PFR ==> bc1qltyqxw94nynyj9nq8kqfvzuxjrwejd6vrdvhlm
Hi,
Did You guys check newest release od RCKangaroo? Does it work on eg. 100bit space?
@RetiredCoder, do You plan any other minipuzzles (or maxi) in the future? Let us know "when", if You plan something, please
BR
Damian
Did You guys check newest release od RCKangaroo? Does it work on eg. 100bit space?
@RetiredCoder, do You plan any other minipuzzles (or maxi) in the future? Let us know "when", if You plan something, please
BR
Damian
Hello, I previously asked you a question about adding the -end search range function, you answered me why you are not satisfied with -range .. I will answer why if I am looking for a 130 puzzle, then -range 84 will search where the zeros are in this example -dp 16 -range 84 -start 33e7665705350000000000000000000000 but nothing more right?) and what I mean is that with the -end function I can break the same 135 puzzle into a dozen or a hundred pieces and search throughout -range 134 but with short distances as an example -range 134 -start 6d9999999999999999999999999999996 -end 7ffffffffffffffffffffffffffff I can calculate the work here, let’s say that I would go through one path in one day and the next day start another path, but only with the start I can’t do this because I don’t know where this path ended if I turn off the program in other words, -end is needed so that you can start with it later) but with -range this is not possible
Your idea sounds senseless for me, but anyway it's open-source so you can modify sources and implement all ideas you have.
Can you confirm that you are using the code from Jean Luc here: https://github.com/JeanLucPons/Kangaroo ?
You can download both sources and compare, my code is not related to JLP's code.
I'm really interested, and for us beginners, it would be super helpful to have a detailed tutorial explaining how you set it up from start to finish
I won't write articles like "step-by-step guide how to crack #135", sorry
And by the way can you send me your BTC
Some people don´t understand how hard is this to crack
Thanks for your response. Could you please share the GitHub repository for your solution? I’d like to explore different approaches, but I want to start by testing Jean Luc’s solution first. I’m not asking for a step-by-step guide to crack #135—I’m still figuring out how to compile Jean Luc’s program with the latest versions. For example, to compile VanitySearch, you had to update Visual Studio Code in 2017 instead of using the 2022 version. Unfortunately, there haven't been any updates from him for a while, and I hope he’s doing well.
As for the other person, it seems you’re taking the easy way out. I never asked for BTC; I prefer to solve the puzzles that the most kind and fair Satoshi made available to us. At least he let us try freely. He would probably be sad to see shortcuts like the ones you’ve suggested. Anyway, I forgive you.
Best regards,
P.S. A big thank you to Satoshi, even though I lost all my 10 BTC back in 2017 Cry. That’s why I want to try my luck with Jean Luc’s code.
PFR ==> bc1qltyqxw94nynyj9nq8kqfvzuxjrwejd6vrdvhlm
in the 2022 version
Hello, I previously asked you a question about adding the -end search range function, you answered me why you are not satisfied with -range .. I will answer why if I am looking for a 130 puzzle, then -range 84 will search where the zeros are in this example -dp 16 -range 84 -start 33e7665705350000000000000000000000 but nothing more right?) and what I mean is that with the -end function I can break the same 135 puzzle into a dozen or a hundred pieces and search throughout -range 134 but with short distances as an example -range 134 -start 6d9999999999999999999999999999996 -end 7ffffffffffffffffffffffffffff I can calculate the work here, let’s say that I would go through one path in one day and the next day start another path, but only with the start I can’t do this because I don’t know where this path ended if I turn off the program in other words, -end is needed so that you can start with it later) but with -range this is not possible
Your idea sounds senseless for me, but anyway it's open-source so you can modify sources and implement all ideas you have.
Can you confirm that you are using the code from Jean Luc here: https://github.com/JeanLucPons/Kangaroo ?
You can download both sources and compare, my code is not related to JLP's code.
I'm really interested, and for us beginners, it would be super helpful to have a detailed tutorial explaining how you set it up from start to finish
I won't write articles like "step-by-step guide how to crack #135", sorry
And by the way can you send me your BTC
Some people don´t understand how hard is this to crack
Thanks for your response. Could you please share the GitHub repository for your solution? I’d like to explore different approaches, but I want to start by testing Jean Luc’s solution first. I’m not asking for a step-by-step guide to crack #135—I’m still figuring out how to compile Jean Luc’s program with the latest versions. For example, to compile VanitySearch, you had to update Visual Studio Code in 2017 instead of using the 2022 version. Unfortunately, there haven't been any updates from him for a while, and I hope he’s doing well.
As for the other person, it seems you’re taking the easy way out. I never asked for BTC; I prefer to solve the puzzles that the most kind and fair Satoshi made available to us. At least he let us try freely. He would probably be sad to see shortcuts like the ones you’ve suggested. Anyway, I forgive you.
Best regards,
P.S. A big thank you to Satoshi, even though I lost all my 10 BTC back in 2017 Cry. That’s why I want to try my luck with Jean Luc’s code.
PFR ==> bc1qltyqxw94nynyj9nq8kqfvzuxjrwejd6vrdvhlm
This thread is a great opportunity to learn how Bitcoin works and use Kangaroos to solve ECDLP but in the end people are only interested in asking for money.
I am doing a project based on yours, RetiredCoder, where I have created the following:
- Lib: Where all the logic is and it is a library available to different executables.
- Worker: Connect to a Dpserver, receives a job and is responsible for sending points to the dpserver
- Dpserver: Receives the points and processes them.
Additionally I have created the following:
- GitHub Actions to validate that it can be compiled on Windows/Ubuntu
- Added CMake to generate the solution on Windows/Ubuntu
- Unit and performance tests to validate different solutions.
Maybe in a few months I will share it with the community, in the meantime I will continue learning since it seems like a great opportunity.
I am doing a project based on yours, RetiredCoder, where I have created the following:
- Lib: Where all the logic is and it is a library available to different executables.
- Worker: Connect to a Dpserver, receives a job and is responsible for sending points to the dpserver
- Dpserver: Receives the points and processes them.
Additionally I have created the following:
- GitHub Actions to validate that it can be compiled on Windows/Ubuntu
- Added CMake to generate the solution on Windows/Ubuntu
- Unit and performance tests to validate different solutions.
Maybe in a few months I will share it with the community, in the meantime I will continue learning since it seems like a great opportunity.
Hello, I previously asked you a question about adding the -end search range function, you answered me why you are not satisfied with -range .. I will answer why if I am looking for a 130 puzzle, then -range 84 will search where the zeros are in this example -dp 16 -range 84 -start 33e7665705350000000000000000000000 but nothing more right?) and what I mean is that with the -end function I can break the same 135 puzzle into a dozen or a hundred pieces and search throughout -range 134 but with short distances as an example -range 134 -start 6d9999999999999999999999999999996 -end 7ffffffffffffffffffffffffffff I can calculate the work here, let’s say that I would go through one path in one day and the next day start another path, but only with the start I can’t do this because I don’t know where this path ended if I turn off the program in other words, -end is needed so that you can start with it later) but with -range this is not possible
Your idea sounds senseless for me, but anyway it's open-source so you can modify sources and implement all ideas you have.
Can you confirm that you are using the code from Jean Luc here: https://github.com/JeanLucPons/Kangaroo ?
You can download both sources and compare, my code is not related to JLP's code.
I'm really interested, and for us beginners, it would be super helpful to have a detailed tutorial explaining how you set it up from start to finish
I won't write articles like "step-by-step guide how to crack #135", sorry
And by the way can you send me your BTC
Some people don´t understand how hard is this to crack
Hello, I previously asked you a question about adding the -end search range function, you answered me why you are not satisfied with -range .. I will answer why if I am looking for a 130 puzzle, then -range 84 will search where the zeros are in this example -dp 16 -range 84 -start 33e7665705350000000000000000000000 but nothing more right?) and what I mean is that with the -end function I can break the same 135 puzzle into a dozen or a hundred pieces and search throughout -range 134 but with short distances as an example -range 134 -start 6d9999999999999999999999999999996 -end 7ffffffffffffffffffffffffffff I can calculate the work here, let’s say that I would go through one path in one day and the next day start another path, but only with the start I can’t do this because I don’t know where this path ended if I turn off the program in other words, -end is needed so that you can start with it later) but with -range this is not possible
Your idea sounds senseless for me, but anyway it's open-source so you can modify sources and implement all ideas you have.
Can you confirm that you are using the code from Jean Luc here: https://github.com/JeanLucPons/Kangaroo ?
You can download both sources and compare, my code is not related to JLP's code.
I'm really interested, and for us beginners, it would be super helpful to have a detailed tutorial explaining how you set it up from start to finish
I won't write articles like "step-by-step guide how to crack #135", sorry
Hi RetiredCoder,
Can you confirm that you are using the code from Jean Luc here: https://github.com/JeanLucPons/Kangaroo ? I'm really interested, and for us beginners, it would be super helpful to have a detailed tutorial explaining how you set it up from start to finish, including allocating resources on https://cloud.vast.ai.
If you could create a tutorial detailing the whole process, including how to configure and run the Kangaroo, it would be amazing! Thanks in advance for your help and the time you could dedicate to it!
Best regards,
P.S. A big thank you to Satoshi, even though I lost all my 10 BTC back in 2017
. That’s why I want to try my luck with Jean Luc’s code.
PFR ==> bc1qltyqxw94nynyj9nq8kqfvzuxjrwejd6vrdvhlm
Can you confirm that you are using the code from Jean Luc here: https://github.com/JeanLucPons/Kangaroo ? I'm really interested, and for us beginners, it would be super helpful to have a detailed tutorial explaining how you set it up from start to finish, including allocating resources on https://cloud.vast.ai.
If you could create a tutorial detailing the whole process, including how to configure and run the Kangaroo, it would be amazing! Thanks in advance for your help and the time you could dedicate to it!
Best regards,
P.S. A big thank you to Satoshi, even though I lost all my 10 BTC back in 2017
. That’s why I want to try my luck with Jean Luc’s code.PFR ==> bc1qltyqxw94nynyj9nq8kqfvzuxjrwejd6vrdvhlm
Updated Part #1, v1.6:
- Best K = 0.99.
- optimized SOTA/SOTA+ parameters for smooth K for all intervals.
- Best K = 0.99.
- optimized SOTA/SOTA+ parameters for smooth K for all intervals.
Hello, I previously asked you a question about adding the -end search range function, you answered me why you are not satisfied with -range .. I will answer why if I am looking for a 130 puzzle, then -range 84 will search where the zeros are in this example -dp 16 -range 84 -start 33e7665705350000000000000000000000 but nothing more right?) and what I mean is that with the -end function I can break the same 135 puzzle into a dozen or a hundred pieces and search throughout -range 134 but with short distances as an example -range 134 -start 6d9999999999999999999999999999996 -end 7ffffffffffffffffffffffffffff I can calculate the work here, let’s say that I would go through one path in one day and the next day start another path, but only with the start I can’t do this because I don’t know where this path ended if I turn off the program in other words, -end is needed so that you can start with it later) but with -range this is not possible
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