Topic: Solving ECDLP with Kangaroos: Part 1 + 2 + RCKangaroo - page 5. (Read 3351 times)
As much for me 26 bTC I had not noticed that for #120 the price had not yet been *10! the reward remains correct. Thank you for your response and the details but 400 RTX 4090 for 2 months is colossal and impossible for 99.9% of people!! Well done anyway
wow, congratulations to you for finding #120, 125 and 130 (probably a world record for 130). Your skills have been rewarded with over 37 BTC recovered. How many GPUs did you have to use to find the 130? Have you used an optimized version of your RCKangaroo or another tool?
Less than 37BTC, check you calculations.
Most of GPU code from RCKangaroo was used for #130.
I spent two months, about 400 RTX4090, I was lucky and solved it twice faster than expected.
wow, congratulations to you for finding #120, 125 and 130 (probably a world record for 130). Your skills have been rewarded with over 37 BTC recovered. How many GPUs did you have to use to find the 130? Have you used an optimized version of your RCKangaroo or another tool?
As promised, here is the third and final part: RCKangaroo, Windows/Linux, open source:
https://github.com/RetiredC/RCKangaroo
This software demonstrates fast implementation of SOTA method and advanced loop handling on RTX40xx cards.
Note that I have not included all possible optimizations because it's public code and I want to keep it as simple/readable as possible. Anyway, it's fast enough to demonstrate the advantage and you can improve it further if you have enough skills.
https://github.com/RetiredC/RCKangaroo
This software demonstrates fast implementation of SOTA method and advanced loop handling on RTX40xx cards.
Note that I have not included all possible optimizations because it's public code and I want to keep it as simple/readable as possible. Anyway, it's fast enough to demonstrate the advantage and you can improve it further if you have enough skills.
Update: final part #3 (RCKangaroo) is ready and will be released shortly.
Thank you,
Update: final part #3 (RCKangaroo) is ready and will be released shortly.
Please don't spam my github repos, you waste your time.
I see a lot talking about GPU, yes for brute force calculation it seems relevant, but due to this issue of parallelism with multi tasks it seems to be the CPU and ram that count, am I right in saying that?
Of course you can use CPU instead of GPU if you don't care about speed.
You can do many things if you don't care about speed, for example, you can use Python instead of C++ to make life easier.
First of all, thank you guys for all your support! 
Slowly, but I keep working on making my ideas public, here is Part 2 of 3:
Open source: https://github.com/RetiredC/Kang-2
In this part I propose a new method to handle kangaroo looping, it works for all ranges and DP values and does not increase the number of required operations, the only requirement is keeping a short list of visited points which can be coded efficiently on GPU as well, and I will demonstrate it in Part 3.
Take care!
Slowly, but I keep working on making my ideas public, here is Part 2 of 3:
Open source: https://github.com/RetiredC/Kang-2
In this part I propose a new method to handle kangaroo looping, it works for all ranges and DP values and does not increase the number of required operations, the only requirement is keeping a short list of visited points which can be coded efficiently on GPU as well, and I will demonstrate it in Part 3.
Take care!
Thank you RetiredCoder for part 1,2.
First of all, thank you guys for all your support!
Slowly, but I keep working on making my ideas public, here is Part 2 of 3:
Open source: https://github.com/RetiredC/Kang-2
In this part I propose a new method to handle kangaroo looping, it works for all ranges and DP values and does not increase the number of required operations, the only requirement is keeping a short list of visited points which can be coded efficiently on GPU as well, and I will demonstrate it in Part 3.
Take care!
Slowly, but I keep working on making my ideas public, here is Part 2 of 3:
Open source: https://github.com/RetiredC/Kang-2
In this part I propose a new method to handle kangaroo looping, it works for all ranges and DP values and does not increase the number of required operations, the only requirement is keeping a short list of visited points which can be coded efficiently on GPU as well, and I will demonstrate it in Part 3.
Take care!
And what is the meaning of calculating K? As soon as you increase the number of kangaroos, your K will fly into space.
With 65536 kangaroos in the classic version K will be 3.07
And when using the GPU, K increases even more due to dp overhead, in this case you will easily reach k=10
With 65536 kangaroos in the classic version K will be 3.07
And when using the GPU, K increases even more due to dp overhead, in this case you will easily reach k=10
Correct, if you set a large number of kangaroos, DP overhead will be high and K will be high.
The number of kangaroos must be small related to sqrt(range), for example, it's a bad idea to use 64K kangaroos to solve 32bit range because total number of jumps of all kangs to solve 32bit is about 64K so it's just one jump for every kang, and DP>0 will cause a big overhead.
On GPU you have a lot of kangs but you will solve high ranges so K will be good.
There are many ways to get high K, you can play with parameters and find out how to keep it small.
The reason to calculate K is to see what parameters are the best.
And what is the meaning of calculating K? As soon as you increase the number of kangaroos, your K will fly into space.
With 65536 kangaroos in the classic version K will be 3.07
And when using the GPU, K increases even more due to dp overhead, in this case you will easily reach k=10
Your experiment is purely laboratory conditions, in real life on the GPU, the number of kangaroos is huge.
With 65536 kangaroos in the classic version K will be 3.07
And when using the GPU, K increases even more due to dp overhead, in this case you will easily reach k=10
Your experiment is purely laboratory conditions, in real life on the GPU, the number of kangaroos is huge.
Hello.
I think I misunderstood.
I know Elliptic Curve operations and the Signature algorithm, I know the applicable attack methods.
I'm trying to find out what you're trying to explain.
I think you have a prejudice. Nevertheless, thank you for your work.
I think I misunderstood.
I know Elliptic Curve operations and the Signature algorithm, I know the applicable attack methods.
I'm trying to find out what you're trying to explain.
I think you have a prejudice. Nevertheless, thank you for your work.
I propose a new method to solve ECDLP with 1.15*sqrt(N) operations (real, not theoretical).
You can read this paper to see methods with 2*sqrt(N) and 1.714*sqrt(N) required ops:
https://arxiv.org/pdf/1501.07019
Or this paper with 1.36*sqrt(N) required ops:
https://www.iacr.org/archive/pkc2010/60560372/60560372.pdf
The best K that I can find in papers is 1.275 (theoretical, in practice it's always a bit worse).
If you know papers describing kangaroo methods with K=1.15 or lower - let me know.
Hello.
I analysed the diagram.
if we call pubkey the big X
according to the diagram
Tame , Wild and K, what kind of an elliptic curve process is applied to X when K = 1.15
Thank you
I analysed the diagram.
if we call pubkey the big X
according to the diagram
Tame , Wild and K, what kind of an elliptic curve process is applied to X when K = 1.15
Thank you
Check "Ec.cpp", this one:
https://en.bitcoin.it/wiki/Secp256k1
Hello.
I think I misunderstood.
I know Elliptic Curve operations and the Signature algorithm, I know the applicable attack methods.
I'm trying to find out what you're trying to explain.
I think you have a prejudice. Nevertheless, thank you for your work.
Correct.
Windows, VS MFC C++.
Oldschool hardcore
Windows, VS MFC C++.
Oldschool hardcore
Hi!
can it tested on MacOS ?
can it tested on MacOS ?
Windows only and You need Visual Studio with MFC
Hi!
can it tested on MacOS ?
can it tested on MacOS ?
Hello.
I analysed the diagram.
if we call pubkey the big X
according to the diagram
Tame , Wild and K, what kind of an elliptic curve process is applied to X when K = 1.15
Thank you
I analysed the diagram.
if we call pubkey the big X
according to the diagram
Tame , Wild and K, what kind of an elliptic curve process is applied to X when K = 1.15
Thank you
Check "Ec.cpp", this one:
https://en.bitcoin.it/wiki/Secp256k1
Hello.
I analysed the diagram.
if we call pubkey the big X
according to the diagram
Tame , Wild and K, what kind of an elliptic curve process is applied to X when K = 1.15
Thank you
I analysed the diagram.
if we call pubkey the big X
according to the diagram
Tame , Wild and K, what kind of an elliptic curve process is applied to X when K = 1.15
Thank you
Hello.
I find it difficult to understand C or C++ language in math operations.
Instead, I develop algorithms with the Fastecdsa Library in Python. I then switch to C or C++ for performance and then use it with GPU performance.
When I tried to read your article, I tried to understand what the value of K is based on. If I see this rule of 4 algorithm you mentioned (such as Fastecdsa or Sagemath in Python), I can join your conversation more.
Just wanted to point out. I am following your topic.
Thank you very much.
I find it difficult to understand C or C++ language in math operations.
Instead, I develop algorithms with the Fastecdsa Library in Python. I then switch to C or C++ for performance and then use it with GPU performance.
When I tried to read your article, I tried to understand what the value of K is based on. If I see this rule of 4 algorithm you mentioned (such as Fastecdsa or Sagemath in Python), I can join your conversation more.
Just wanted to point out. I am following your topic.
Thank you very much.
I use C++ because it's faster so I can test thousands of points to measure K carefully. CUDA is even faster of course, but it's difficult to use it for research, so I use it only at the last stage.
Yes, it's not an easy topic, so today I added "diagram.jpg" to show how it works.
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