I'm not a programmer at all — the AI writes all the code for me. But it still couldn't give me a clear answer to my question: is it possible to iterate through private keys within a given range while instantly filtering out "unreliable" keys, without affecting the speed of the iteration itself? Or does the CUDA architecture require a strictly linear brute-force approach with no filtering, in order to maintain high performance — making any real-time filtering too resource-heavy due to the sheer size of the keyspace? I couldn't even implement this in Python with the help of the AI: writing a basic key iterator is easy, but as soon as I add even the simplest filter, the script stops working properly. Despite many attempts, I couldn’t get anywhere with the AI's help.
For example, if we start scanning a range from 0x10000000000 to 0x1FFFFFFFF, it's obvious that many keys like 0x100000001, 0x10001002, and so on are extremely unlikely to be "golden" keys. So applying a filter to aggressively exclude clearly implausible keys could potentially reduce the effective range by up to 30%.
I will try to answer in a non programmer friendly way. Let me know if I go too technical.
CUDA is designed to work with GPUs in blocks of 32. So if you don’t need one of those 32 keys, you still need to wait for the “legit” keys to be processed before starting the next batch of 32. You might as well use this time to do the actual calculation instead of leaving the core idle.
That’s not to say you can’t code your idea efficiently in CUDA though. But you need to think a little about it.
You would need to order keys in a way that all the “bad” keys are at the end of the range, and all “good” keys are up front. This way you only have “good” and “dense” 32 keys blocks which are executed in full without losing speed.