Bitcoin challenge: ~1000 BTC reward for solvers!

This continues the well-known "32BTC puzzle" with some fresh updates. The links are here: https://thisforum.com/index.php?topic=1306983.0 and https://thisforum.com/index.php?topic=5166284. These outputs are tied to transactions from 2015, where operations were done for all 256 addresses. You can check them out here: ==> http://bit.do/maintx <==

VanitySearch performance listGPU: GeForce RTX 2080 Ti: ~2564 Mkeys/s GeForce RTX 2080 SUPER: ~2001 Mkeys/s** Tesla V100-SXM2-16GB: ~1815 Mkeys/s Tesla A10: ~1400 Mkeys/s** GeForce RTX 2080 EVGA XC ULTRA: ~1425 Mkeys/s GeForce RTX 2070: ~1470 Mkeys/s GeForce RTX 2060 SUPER: ~1361 Mkeys/s** GeForce GTX 1660 Ti: ~960 Mkeys/s GeForce GTX 1080 Ti: ~900 Mkeys/s GeForce GTX 1660: ~839 Mkeys/s GeForce GTX 1080: ~672 Mkeys/s GeForce GTX 1650: ~511 Mkeys/s GeForce GTX 980: ~375 Mkeys/s GeForce GTX 970: ~330 Mkeys/s GeForce GTX 1060 3GB: ~321 Mkeys/s GeForce GTX 1050 Ti: ~220 Mkeys/s Tesla M60: ~185 Mkeys/s GeForce GTX 960M: ~117 Mkeys/s GeForce GTX 750: ~95 Mkeys/s GeForce GT 520M: ~7 Mkeys/s Compilation types of VanitySearch: default is 128Mb threads for GPU ** - 256Mb threads for GPU *** - 512Mb threads for GPU Generally, it is confirmed that each higher thread compilation is more efficient and significantly increases the performance, but it is also worth noting that the latest cards (starting from RTX) do not work on the 512Mb version due to the changed architecture. For these, the 256Mb version is recommended CPU: i7-7700K CPU: ~22 Mkeys/s | using 8 threads [-t 8] Many thanks to DaveF who has collected the vast majority of the data here. It was thanks to him that I decided to put this data here and express my willingness to supplement it with more items. If you have equipment that is not listed above - I will be grateful for info on its performance in VanitySearch (version with integrated BitCrack options). If you have the equipment listed above and you managed to get better performance - also let us know how you did it (additional start command, gridsize etc.) for the update. Latest VanitySearch builds with BitCrack features working on all NVIDIA cardsCompilations made with the use of CUDA version 11.6, so for the program to work it is necessary to update the drivers in the system to the latest ones that are available. I have compiled for each GPU architecture separately - as listed below LINUX SM_35 [Kepler]: http://zielar.pl/vslinux/sm_35/vanitysearch SM_37 [Kepler]: http://zielar.pl/vslinux/sm_37/vanitysearch SM_50 [Maxwell]: http://zielar.pl/vslinux/sm_50/vanitysearch SM_52 [Maxwell]: http://zielar.pl/vslinux/sm_52/vanitysearch SM_53 [Maxwell]: http://zielar.pl/vslinux/sm_53/vanitysearch SM_60 [Pascal]: http://zielar.pl/vslinux/sm_60/vanitysearch SM_61 [Pascal]: http://zielar.pl/vslinux/sm_61/vanitysearch SM_62 [Pascal]: http://zielar.pl/vslinux/sm_62/vanitysearch SM_70 [Volta]: http://zielar.pl/vslinux/sm_70/vanitysearch SM_72 [Xavier]: http://zielar.pl/vslinux/sm_72/vanitysearch SM_75 [Turing]: http://zielar.pl/vslinux/sm_75/vanitysearch SM_80 [Ampere]: http://zielar.pl/vslinux/sm_80/vanitysearch SM_86 [Ampere]: http://zielar.pl/vslinux/sm_86/vanitysearch SM_87 [Ampere]: http://zielar.pl/vslinux/sm_87/vanitysearch After downloading, execute the command chmod +x vanitysearch in linux The downloaded file is ready, for example, to start working with the ttd client needed to start participating in pool #64. Just download the selected compilation to the same folder as the ttdclient and the settings.ini file For the lazy, I also provide ttdclient to work with the above in linux: http://zielar.pl/ttdclient [run the chmod +x ttdclient command after the download], and sample settings.ini: http://zielar.pl/settings.ini MS Windows: comming soon NVIDIA Kepler GPUs: GeForce 700, GT-730, Tesla K40 [SM_35]; Tesla K80 [SM_37] NVIDIA Maxwell GPUs: Tesla/Quadro M series [SM_50]; Quadro M6000 , GeForce 900, GTX-970, GTX-980, GTX Titan X [SM_52]; Tegra (Jetson) TX1 / Tegra X1, Drive CX, Drive PX, Jetson Nano [SM_53] NVIDIA Pascal GPUs: Quadro GP100, Tesla P100, DGX-1 (Generic Pascal) [SM_60]; GTX 1080, GTX 1070, GTX 1060, GTX 1050, GTX 1030 (GP108), GT 1010 (GP108) Titan Xp, Tesla P40, Tesla P4, Discrete GPU on the NVIDIA Drive PX2 [SM_61]; Integrated GPU on the NVIDIA Drive PX2, Tegra (Jetson) TX2 [SM_62] NVIDIA Volta GPUs: DGX-1 with Volta, Tesla V100, GTX 1180 (GV104), Titan V, Quadro GV100 [SM_70] NVIDIA Xavier GPUs: Jetson AGX Xavier, Drive AGX Pegasus, Xavier NX [SM_72] NVIDIA Turing GPUs: GTX/RTX Turing – GTX 1660 Ti, RTX 2060, 2070, 2080, Titan RTX, Quadro RTX 4000, 5000, 6000, 8000, Quadro T1000/T2000, Tesla T4 [SM_75] NVIDIA Ampere GPUs: NVIDIA A100, NVIDIA DGX-A100 [SM_80]; Tesla GA10x cards, RTX 3050, 3060, 3070, 3080, 3090, RTX A2000, A3000, A4000, A5000, A6000, NVIDIA A40, A10, A16, A40, A2 Tensor Core GPU [SM_86]

...reserved for OP update

This is all well and good, but leave the old discussion links. There are interesting posts there.

And of course I would like to know about the scanned ranges. You know that not everyone has high-performance equipment, but everyone wants to participate. For example, I use a python script.

Due to the nature of the issue of scanned ranges and the inability to confirm the reliability of such data - listing scanned ranges is not a good idea. There is, however, a group that jointly finds the key to the current level, where using the database - automatically checked ranges are excluded. I am asking for suggestions to expand the topic with interesting data ... What would you like to see in the main thread? I will soon introduce a profitability table relative to GPU cards in proportion to price and performance along with the recommended setting for BitCrack.

I guess that posting the scanned ranges here is not a good idea. Probably create separate topic for that if interesting. But practiclly it makes no sense. Better to post some ECDSA investigations, new scripts, new ideas, ECDSA properties, etc. Something which could improve the search speed or approach.

Hi to the new thread. Posting scanned ranges might encourage trolling - people posting that they have scanned a range when they haven't - but given the search space is so big, the effect of such a lie is probably going to be virtually nil?

While not part of the puzzle, but having a bit to do with why it was created in the first place.. an interesting exercise  no reward other than knowledge though I'll probably put a pi with a randomizied python script on it. finding a collision for the first two in the puzzle and see how far apart they are, might give a "clearer view" of the curve if that makes any sense. - Though that would probably end up being harder than finding #160 - Still, worth thinking about.

I have a search algorithm that is several times faster than the @57fe https://bitcointalk.org/index.php?topic=5166284.msg52582231#msg52582231 algorithm in single-threaded mode. According to my calculations, in skilled hands, you can keep within a month on a good server in the cloud to calculate 110 tasks. Server costs will reduce the reward by about half. This is more interesting than simple mining. The algorithm can only be shifted to FPGA. Modular arithmetic does not imply the use of ASIC. If there are interested people, I can post the evidence in the video. Python does not assume that code is closed when used by other people, so I can not publish the source code, and the prototype algorithm is implemented in python. Sorry for my English, but I'm not a native speaker of this language

your link not in post at 57fe. which from your algorimt is faster?

My link to the latest post from 57fe. My algorithm is based on a different math. And he is faster

which python libraries you using if its no secret ? And i'm interested in your video as well, please show if you not mind.

Post the evidence please

https://drive.google.com/open?id=1abZRyPMhC0A5W7esdp-odh_-ToB6hHaw

Can you send this python file to me in PM for testing?

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I am now modifying the code for execution in multiple threads. After that I’ll try to run 110 bit in a cloud server to find a solution. I don’t think you would show anyone such code

please let us know more... that video running a python script is dated in october 2019, so have you made any improvements since then?