answer of qwen-2.5:15b for CUDA SHA1 hasher

sha1.md

Certainly! Below is an example of how you could implement a simple SHA-1 hashing algorithm using PyCUDA. PyCUDA allows you to leverage the power of your GPU for parallel processing tasks, including cryptographic operations like hash functions.

First, make sure you have PyCUDA installed:

pip install pycuda

Here's a basic implementation of SHA-1 using PyCUDA:

import pycuda.autoinit
import pycuda.driver as driver
from pycuda.compiler import SourceModule

# Define the CUDA kernel for SHA-1
mod = SourceModule("""
__global__ void sha1_hash(unsigned int *data, unsigned long long n) {
    // Constants for SHA-1
    const unsigned int K[] = {0x5A827999, 0x6ED9EBA1, 0x8F1BBCDC, 0xCA62C1D6};

    __shared__ unsigned int A[32];
    __shared__ unsigned int B[32];
    __shared__ unsigned int C[32];
    __shared__ unsigned int D[32];
    __shared__ unsigned int E[32];

    // Load initial SHA-1 hash values
    if (threadIdx.x == 0) {
        A[0] = 0x67452301;
        B[0] = 0xEFCDAB89;
        C[0] = 0x98BADCFE;
        D[0] = 0x10325476;
        E[0] = 0xC3D2E1F0;
    }

    __syncthreads();

    for (unsigned long long i = blockIdx.x * blockDim.x + threadIdx.x; i < n; i += blockDim.x * gridDim.x) {
        unsigned int W[80];
        // Message padding and processing
        if (i % 16 == 0) {
            // Load message block into W
            for (int j = 0; j < 16; ++j) {
                W[j] = data[i + j];
            }

            // Expand the message schedule
            for (int j = 16; j < 80; ++j) {
                unsigned int temp = W[j - 3] ^ W[j - 8] ^ W[j - 14] ^ W[j - 16];
                W[j] = ((temp << 1) | (temp >> 31));
            }

            // Initialize hash values for this block
            unsigned int a = A[0], b = B[0], c = C[0], d = D[0], e = E[0];

            // SHA-1 main loop
            for (int j = 0; j < 80; ++j) {
                unsigned int f, k;

                if (j < 20) {
                    f = (b & c) | ((~b) & d);
                    k = K[0];
                } else if (j < 40) {
                    f = b ^ c ^ d;
                    k = K[1];
                } else if (j < 60) {
                    f = (b & c) | (b & d) | (c & d);
                    k = K[2];
                } else {
                    f = b ^ c ^ d;
                    k = K[3];
                }

                unsigned int temp = ((a << 5) | (a >> 27)) + f + e + k + W[j];
                e = d;
                d = c;
                c = (b << 30) | (b >> 2);
                b = a;
                a = temp;
            }

            // Update hash values
            A[0] += a;
            B[0] += b;
            C[0] += c;
            D[0] += d;
            E[0] += e;
        }
    }
}
""")

sha1_hash = mod.get_function("sha1_hash")

# Example usage
import numpy as np

data = np.array([0x67452301, 0xEFCDAB89, 0x98BADCFE, 0x10325476, 0xC3D2E1F0], dtype=np.uint32)
n = np.int64(len(data))

sha1_hash(driver.InOut(data), n, block=(32, 1, 1), grid=(1, 1))

print("SHA-1 Hash (hex):", ' '.join(f'{x:08x}' for x in data))

Explanation:

1. CUDA Kernel: The kernel sha1_hash processes the input data and computes the SHA-1 hash using shared memory to store intermediate values.
2. Constants: The constants for SHA-1 are defined in the kernel.
3. Message Processing: The message is processed in blocks of 512 bits (64 bytes), which is expanded into an array of 80 words.
4. Main Loop: The main loop computes the hash values using the SHA-1 algorithm.

This code assumes that the input data is already padded and preprocessed according to the SHA-1 specification. For a complete implementation, you would need to handle padding and ensure that the input message fits the required block size.

Note:

SHA-1 is considered cryptographically broken and unsuitable for further use in security-sensitive applications. It is recommended to use stronger algorithms like SHA-256 or SHA-3 for new applications.