Quote from: citb0in on January 01, 2023, 06:01:47 PM

here's the updated complete code variant with using multicore functionality and the secrets library for enhanded speed:

Code:

#!/usr/bin/env python3
# 2023/Jan/01, citb0in_multicore_secrets.py
import concurrent.futures
import os
import numpy as np
import secrets
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 "secrets" library
  private_keys = np.array([secrets.randbelow(2**256) 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_multicore_secrets.txt', addresses, fmt='%s')

To speed up slightly this code (with multi core), you can write on different files:

Code:

#!/usr/bin/env python3
# 2023/Jan/01, citb0in_multicore_secrets.py
import concurrent.futures
import os
import numpy as np
import secrets
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, i):
# Generate a NumPy array of random private keys using "secrets" library
private_keys = np.array([secrets.randbelow(2**256) 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])
np.savetxt('addresses_1M_multicore_secrets'+ str(i) +'.txt', thread_addresses, fmt='%s')

return

# 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, i))

Your code doesn't store private keys, are you sure you want to have only a list of addresses without their private keys?