I would like to ask the community about your energy efficiency metrics. I think this is an important piece of information in this topic, which hasn't really been discussed yet.
Firstly, you need to scale down the "1 quantillion" to a way way lower number.. And measure what does it take to produce 1 point on the curve, not "i covered this much distance with 1 point". With that claim "2.9w per 1 EOS", the 135bit range of challenge address 135, that has roughly what, 22000 quantillions to check? Would be solved by 100 RTX 5090 GPU-s in couple of days while undervolted to save what ever space they occupy from burning down. But how much memory it requires? Few petabytes? Exabytes? You need to store roughly 2⁶⁷ bytes worth of baby steps?
And how would you compare your BSGS with for eg. Kangaroo? Kangaroo measures raw computation of per point compares, not how much it traveled across the elliptic curve space, but is far more efficient then bsgs regardless of the console display of the speed. Or bluntly, lets just compare what do we need to produce 1 point on the curve, and if we are hashing it then also account that?
My program currently, on 3070 Ti:
Accounting only gpu power draw, for the whole pipeline (private key -> public key -> sha256 -> ripemd160 -> check if its a match) form the program start, does 7.8m hash160 checks per 1watt (gpu draws 290w, stock settings).
Accounting only gpu power draw, for the whole pipeline (private key -> public key -> check if its a match) form the program start, does 27.8m coordinate checks per 1watt (gpu draws 310w, stock settings).
I divided 2.25b hash160 checks a second with 290w power draw, to get 0.0000001289w per hash160 check.
Same with 8.65b coordinates checks a second with 310w power draw, to get 0.0000000360w per coordinate check.
And also, the coordinate compare kernel only produces valid points on the curve and compares them to 1 target public key, kinda "useless regardless of the speed, but is purely there to see differences of what ripemd160 does to the gpu and how costly it is".
And also, efficiency is heavily tied to the gpu that is currently crunching numbers. 3070 Ti will never be as efficient as 4090, so its heavily dependent on the gpu it self. And perhaps 4090 isnt as efficient after all, perhaps some other gpu might be more efficient.
There are too many variables to account to say confidently "my setup is more efficient then yours", especially when everyone has their own metrics of efficiency.
Just my opinion, not nesessarly correct one, feel free to agree or disagree, i dont care, who knows, maybe from gazilion
shit ideas one turns out to be acctually usefull.
