Re: the fastest possible way to mass-generate addresses in Python

Quote from: citb0in on December 30, 2022, 08:13:26 PM

==> I have generated 1,007,862 addresses (about 1 million keys) in about 15 seconds using one single core with your program.
==> I have generated exactly 1 millions addresses in 10 seconds using one single core with my program.

Conclusion:
The originally enormous speed advantage is therefore, according to these tests, not really to be found in the code difference but in the fact that randomly generated keys are simply more computationally intensive and cost more time than simple sequential key generation. So just as you originally said arulbero.

You said : "compare apples with apples"

but now you are comparing a library written in C against pure python, it seems not fair

For the record:

I wrote a library in C with a single function: from public keys to address (without b58 encoding)

Code:

Private key : 0000000000000000000000000000000000000000000000000000000000000001
Public key :
x: 79be667ef9dcbbac55a06295ce870b07029bfcdb2dce28d959f2815b16f81798
y: 483ada7726a3c4655da4fbfc0e1108a8fd17b448a68554199c47d08ffb10d4b8

PrKey WIF c.: KwDiBf89QgGbjEhKnhXJuH7LrciVrZi3qYjgd9M7rFU73sVHnoWn
Address before b58 encode : 751e76e8199196d454941c45d1b3a323f1433bd6
Address with b58 encode : 1BgGZ9tcN4rm9KBzDn7KprQz87SZ26SAMH

I imported this function as file .so in my python script,

result:

Code:

1 core, 1M of addresses
time python3 gen_batches.py > addresses.out

real 0m3,961s
user 0m3,910s
sys 0m0,055s

16 cores, 1M of addresses
time python3 gen_batches.py > addresses.out

real 0m0,527s
user 0m7,404s
sys 0m0,151s

1 core, 16.4M of addresses
time python3 gen_batches.py > addresses.out

real 1m1,757s
user 1m1,382s
sys 0m0,348s

16 cores, 16.4M of addresses
time python3 gen_batches.py > addresses.out

real 0m6,672s
user 2m2,989s
sys 0m0,698s

less addresses.out

c71c6741ae4862dadaf2d9c9295d47410a27e194
1d9a3ef1efeec75b05f0ece73ad9402cac767843
44abfeb8701c716380ed2a40aeb72370ef61d7aa
d6a2aa6799f42078faa889dcda92b29c6005d84d
f3a0e804269feeba68d8c1facf68206b73b2a987
7a795a7b4500f80101e489e9ad5d7bc1855edf13
d8e619b13be4c2d6182aa10c0e7237a35ba0ab05
be801790f2cefb432bdf5dce2946fd22364b36b0
531a9c231f3e42d4bdb02d44bf01104e6eccd83d
7c8391a816b578f0d6e0a56595b67e12a314f945
6d0b359ba6f3d382eb99a346f99f0d27c29e2963
aa4116886a8631699f5077d78f47e6013a1af05f
2574970ffa9d5b26d52d89a76eef9c3c1bfbe798
...

then the speed is now about 2.4 MKeys/s.

I'm pretty sure that more c functions you introduce in your python script, more fast it becomes.