Re: Is there a way to use Python GPU for ECC speed up?

I had driver problems on Debian 11

Code:

sudo nano /etc/modprobe.d/blacklist-nouveau.conf

blacklist-nouveau.conf :

Code:

lacklist nouveau
blacklist lbm-nouveau
options nouveau modeset=0
alias nouveau off
alias lbm-nouveau off

Then reboot and reinstall

Code:

sudo apt -y install nvidia-cuda-toolkit nvidia-cuda-dev nvidia-driver

It works like this. GPU must be specified as "0" . Or who has more than one of them in the same command.

Code:

import os
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
import numpy as np
import numba
from numba import cuda, jit
from timeit import default_timer as timer
from fastecdsa import keys, curve
import secp256k1 as ice
# Run on CPU
def cpu(a):
for i in range(100000):
dec = keys.gen_private_key(curve.P256)
HEX = "%064x" % dec
wifc = ice.btc_pvk_to_wif(HEX)
wifu = ice.btc_pvk_to_wif(HEX, False)
uaddr = ice.privatekey_to_address(0, False, dec)
caddr = ice.privatekey_to_address(0, True, dec)
a[i]+= 1
# Run on GPU
@numba.jit(forceobj=True)
def gpu(x):
dec = keys.gen_private_key(curve.P256)
HEX = "%064x" % dec
wifc = ice.btc_pvk_to_wif(HEX)
wifu = ice.btc_pvk_to_wif(HEX, False)
uaddr = ice.privatekey_to_address(0, False, dec)
caddr = ice.privatekey_to_address(0, True, dec)
return x+1
if __name__=="__main__":
n = 100000
a = np.ones(n, dtype = np.float64)
start = timer()
cpu(a)
print("without GPU:", timer()-start)
start = timer()
gpu(a)
numba.cuda.profile_stop()
print("with GPU:", timer()-start)

Result
without GPU: 10.30411118400002
with GPU: 0.2935101880000275

Grin