#!/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')
#!/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')

#!/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')

#!/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')