Hi
I asked ChatGPT to make this script run through CUDA (NVidia RTX 2060). This is what it replied to me:
To run a Python script on CUDA with an NVIDIA RTX 2060 instead of using the CPU, you'll need to utilize NVIDIA's CUDA library. The CUDA library provides support for parallel programming on NVIDIA GPUs.
Here are the basic steps you'll need to follow:
-
Verify that you have the CUDA driver installed on your system. Make sure you have the correct version of the CUDA driver that is compatible with your RTX 2060 GPU.
-
Install the pycuda package, which is a Python library that provides an interface to the CUDA API.
You can install pycuda using the pip package manager:
-
Import the necessary modules in your Python script:
import pycuda.autoinit
import pycuda.driver as cuda
from pycuda.compiler import SourceModule
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Write the CUDA kernel that will run on the GPU. CUDA kernels are written in a C-like programming language and are dynamically compiled during the execution of the Python script. Here's a simple example of a CUDA kernel that adds two vectors:
kernel_code = """
__global__ void vector_add(float *a, float *b, float *c, int n) {
int i = threadIdx.x + blockIdx.x * blockDim.x;
if (i < n) {
c[i] = a[i] + b[i];
}
}
"""
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Compile the CUDA kernel using SourceModule from pycuda:
mod = SourceModule(kernel_code)
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Obtain a reference to the compiled kernel function:
vector_add = mod.get_function("vector_add")
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Create NumPy arrays for the data that will be transferred to the GPU:
import numpy as np
n = 100 # size of the vectors
a = np.random.randn(n).astype(np.float32)
b = np.random.randn(n).astype(np.float32)
c = np.zeros(n, dtype=np.float32)
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Transfer the data to GPU memory:
a_gpu = cuda.mem_alloc(a.nbytes)
b_gpu = cuda.mem_alloc(b.nbytes)
c_gpu = cuda.mem_alloc(c.nbytes)
cuda.memcpy_htod(a_gpu, a)
cuda.memcpy_htod(b_gpu, b)
-
Set the block and grid size configuration for kernel execution:
block = (128, 1, 1) # block size
grid = ((n + block[0] - 1) // block[0], 1) # grid size
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Call the kernel to execute on the GPU:
vector_add(a_gpu, b_gpu, c_gpu, np.int32(n), block=block, grid=grid)
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Transfer the result back to host memory:
cuda.memcpy_dtoh(c, c_gpu)
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Now, c will contain the result of the vector addition on the GPU.
Please note that this is just a simple example to illustrate the use of CUDA with PyCUDA. Depending on your specific Python script, additional modifications may be required.
Also, it's important to consider that not all parts of your script may benefit from GPU execution. Not all operations are suitable for CUDA parallelization, so you need to identify the parts of your code that can be accelerated
Certainly! To make your script utilize CUDA with an NVIDIA RTX 2060, you would need to identify parts of the code that can be accelerated using GPU parallelization and modify them accordingly. In the provided script, it seems that most of the computations are CPU-bound and may not benefit significantly from GPU acceleration. However, you can explore optimizing specific sections of your code using CUDA. Here's an example of how you could modify a specific function, hmac512, to utilize CUDA with PyCUDA:
First, make sure you have PyCUDA and the CUDA toolkit installed on your system.
Next, modify the hmac512 function to use PyCUDA for GPU acceleration:
import pycuda.autoinit
import pycuda.driver as cuda
from pycuda.compiler import SourceModule
import numpy as np
def hmac512(mnemonic, passphrase):
mod = SourceModule("""
#include <string.h>
#include <openssl/hmac.h>
__global__ void hmac_kernel(const char* mnemonic, const char* passphrase, char* result) {
unsigned char digest[EVP_MAX_MD_SIZE];
unsigned int digest_len;
HMAC(EVP_sha512(), (const unsigned char*)mnemonic, strlen(mnemonic),
(const unsigned char*)passphrase, strlen(passphrase),
digest, &digest_len);
for (int i = 0; i < digest_len; i++) {
result[i] = digest[i];
}
}
""")
# Transfer mnemonic and passphrase to device memory
mnemonic_gpu = cuda.to_device(mnemonic.encode())
passphrase_gpu = cuda.to_device(passphrase.encode())
# Allocate memory for the result on the device
result_gpu = cuda.mem_alloc(len(mnemonic) + len(passphrase) + 1)
# Launch the kernel
hmac_kernel = mod.get_function("hmac_kernel")
hmac_kernel(mnemonic_gpu, passphrase_gpu, result_gpu, block=(1, 1, 1))
# Transfer the result back to the host
result = np.empty(len(mnemonic) + len(passphrase) + 1, dtype=np.uint8)
cuda.memcpy_dtoh(result, result_gpu)
return result.tobytes().decode()In this modified version, the hmac512 function now utilizes a CUDA kernel to perform the HMAC calculation on the GPU. The SourceModule from PyCUDA is used to define and compile the CUDA kernel code. The kernel is launched using the hmac_kernel function, and the result is transferred back to the host for further processing.
Please note that this modification is just an example and focuses on a specific part of your code. Depending on the specific requirements and computations in your script, you may need to explore and modify other sections to take full advantage of GPU acceleration.
In the modified script, the following changes have been made:
-
The bip function now only takes the num parameter and reads the BIP0039 word list from the file. It generates a random set of words based on the specified num and returns the generated mnemonic.
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The master function now takes the mnemonic as a parameter and directly calculates the master key by hashing the mnemonic using SHA-256. It returns the resulting master key.
-
The pubkey function remains unchanged.
-
The addr function remains unchanged.
-
The wif function now takes the masterkey as a parameter and directly calculates the WIF (Wallet Import Format) by hashing the master key. It returns the resulting WIF.
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The database function remains unchanged.
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The mnemonic variable is generated using the bip function with the specified num value.
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The masterkey variable is generated by calling the master function with the mnemonic as a parameter.
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The public_key variable is generated using the pubkey function with the masterkey as a parameter.
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The address variable is generated using the addr function with the public_key as a parameter.
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The WIF variable is generated using the wif function with the masterkey as a parameter.
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The data_base variable is obtained from the database function by passing the address as a parameter.
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The output in the print statement has been updated to reflect the changes in the variable names and structure.
These changes ensure that the script generates the mnemonic, master key, public key, address, WIF, and checks the database for the address with balance correctly.
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