Quote from: citb0in on Today at 03:40:50 PM

Code:

#!/usr/bin/env python3
# 2022/Dec/30, citb0in
import concurrent.futures
import os
import numpy as np
import fastecdsa.keys as fkeys
import fastecdsa.curve as fcurve
import secp256k1 as ice

# how many cores to use
num_cores = 1
#num_cores = os.cpu_count()

# Set the number of addresses to generate
num_addresses = 1000000

# Define a worker function that generates a batch of addresses and returns them
def worker(start, end):
  # Generate a NumPy array of random private keys using fastecdsa
  private_keys = np.array([fkeys.gen_private_key(fcurve.P256) for _ in range(start, end)])

  # Use secp256k1 to convert the private keys to addresses
  thread_addresses = np.array([ice.privatekey_to_address(2, True, dec) for dec in private_keys])

  return thread_addresses

# Use a ProcessPoolExecutor to generate the addresses in parallel
with concurrent.futures.ProcessPoolExecutor() as executor:
  # Divide the addresses evenly among the available CPU cores
  addresses_per_core = num_addresses // num_cores

  # Submit a task for each batch of addresses to the executor
  tasks = []
  for i in range(num_cores):
   start = i * addresses_per_core
   end = (i+1) * addresses_per_core
   tasks.append(executor.submit(worker, start, end))

  # Wait for the tasks to complete and retrieve the results
  addresses = []
  for task in concurrent.futures.as_completed(tasks):
   addresses.extend(task.result())

# Write the addresses to a file
np.savetxt('addresses_1M_with_singleCore.txt', addresses, fmt='%s')

Running this under one single core, now I get 26.5 sec for 1 million generated addresses. This is a rate of 37,735 keys/second. I am curious to see your program using all available cores.

Ok, finally I managed to use your multithread program with my script.

Results: 30s to generate 16.4 M of addresses, about 540k addresses / s.

Code:

time python3 gen_batches.py > addresses.out

real 0m29,810s
user 8m22,402s
sys 0m6,273s

wc addresses.out

16474040 16474040 575934502

less addresses.out

18GW1MFwpYZgmRSy8R4wgjUtBnscJtXeUZ
1FXUP9HViWxrtgAvqbpgVrd69yarx6Njdp
1MuxV99fRFnhoNopdxV7kkQB4u14chCtqA
17AzXPYExdE8nxkiV6zJGLuJq32WkAZ134
12DUcGmFMkmXV8tYihM2jLQ36pc4zLBDP6
1AqwPisfi9hDHoF95SEoSEVN89fX8NSGjo
17qRbvdShkoCDw3XJGhEqxaFkQHaK36Yk1
187x8hPiVpRLGDRsvy4nreeMNW7aW9zuFv
1EGSDnc2j7p3qsPJsPakKgavCTz4szYU3w
1F4KjKLNqQSbmw9xsJurRVQ3RciL7Kox7g
1AKaHBfhMEedrtnjGgmFFsdG3SzyN3hpcZ
1GaWuuPPM747Na3CTwaR3DcewoBHc9VVSv
12VeWT4jZCep17HJswMtfCEqh14oDcRr3L
16MBC6WLWMvNeexYYX853Ucf2ZsPoYLRoS
1JVrhzEKTcgzGtSc9u9LpFjksFZiQveFT6
17kz2Wpry3vnLM6RMjB3KXoG6kRxjeDQcj
1TBh3q7vQoTmgX6pY75ADHDSkcaWkmCvd
13QVDFeeWkErhTAFqX3SP2YzrzWt3Bh7iu
1GPyzJC8Ey8PasHn2Qa8aFanjrXuE1aNK5
17QE8iNrtLnju5pinG1vdoCVZpn8zbrM7S
17nKRy1XdKdroWXPsnsd4RvxWKVySZxath
1KWaY8K824oEFk5q4tZioCZ86viLJuKNgS
141m3BUZNjMxbs4a9Cb6SbCmEzx4nPJKCL
12ueqsLkT1XCVheGM34uQWW2fpnhVvtwmE
14eH2SFvCq3fxmrK9NgKTxfMCeKixNwQ6c
1G9CaAqoKJSfTRyGcvPhVFewLRXKWzkwh7
15RthH7DBuDSmYcFCYanUhBsrdEYzBahNw
13BTmQ2gCaZ8x4T7Sba9k2MRBSDRrZLy6u
1NpbKVze4pPVCghjaa2CV7LfRkotCNu76Y
1HfQ42v7DEDHEuQMhH5caPzX2RgtSmoYWw
...