write me this whole program in CUDA:
#include <cuda.h>
#include <stdio.h>
#define BLOCK_SIZE 32
#define STRASSEN_THRESHOLD 64
global void padMatrix(float *A, float *paddedA, int N)
{
int row = blockIdx.y * blockDim.y + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
Copy
if (row < N && col < N)
{
    paddedA[row * (2 * N) + col] = A[row * N + col];
}
}
global void padIdentity(float *I, float *paddedI, int N)
{
int row = blockIdx.y * blockDim.y + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
excel
Copy
if (row == col && row < N)
{
    paddedI[row * (2 * N) + col] = 1.0f;
}
else if (row < N && col < N)
{
    paddedI[row * (2 * N) + col] = 0.0f;
}
}
global void splitMatrix(float *A, float *B, float *C, float *D, float *E, float *F, float *G, float *H, int N)
{
int row = blockIdx.y * blockDim.y + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
excel
Copy
if (row < N / 2 && col < N / 2)
{
    C[row * (N / 2) + col] = A[row * N + col];
    D[row * (N / 2) + col] = A[row * N + col + N / 2];
    E[row * (N / 2) + col] = B[row * N + col];
    F[row * (N / 2) + col] = B[row * N + col + N / 2];
}
else if (row < N / 2 && col >= N / 2)
{
    D[row * (N / 2) + (col - N / 2)] = A[row * N + col];
    F[row * (N / 2) + (col - N / 2)] = B[row * N + col];
}
else if (row >= N / 2 && col < N / 2)
{
    G[(row - N / 2) * (N / 2) + col] = A[row * N + col];
    H[(row - N / 2) * (N / 2) + col] = B[row * N + col];
}
else
{
    H[(row - N / 2) * (N / 2) + (col - N / 2)] = A[row * N + col];
}
}
global void mergeMatrix(float *A, float *B, float *C, float *D, float *E, float *F, float *G, float *H, int N)
{
int row = blockIdx.y * blockDim.y + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
excel
Copy
if (row < N && col < N)
{
    A[row * N + col] = A[row * N + col] + B[row * N + col] + D[row * N + col] - F[row * N + col];
    C[row * N + col] = A[row * N + col] + D[row * N + col];
    E[row * N + col] = A[row * N + col] - B[row * N + col] + F[row * N + col];
    H[row * N + col] = G[row * N + col] + H[row * N + col];
    B[row * N + col] = A[row * N + col] + B[row * N + col];
    D[row * N + col] = G[row * N + col] - H[row * N + col];
    F[row * N + col] = A[row * N + col] - D[row * N + col];
    G[row * N + col] = B[row * N + col] - F[row * N + col];
}
}
global void strassenStep(float *A, float *B, float *C, int N)
{
if (N <= STRASSEN_THRESHOLD)
{
// Perform standard matrix multiplication for small matrices
for (int i = 0; i < N; i++)
{
for (int j = 0; j < N; j++)
{
float sum = 0.0f;
for (int k = 0; k < N; k++)
{
sum += A[i * N + k] * B[k * N + j];
}
C[i * N + j] = sum;
}
}
}
else
{
// Split matrices into quarters and pad them
int halfN = N / 2;
float *paddedA = new float[N * N * 4];
float *paddedB = new float[N * N * 4];
float *paddedC = new float[N * N * 4];
float *paddedD = new float[N * N * 4];
float *paddedE = new float[N * N * 4];
float *paddedF = new float[N * N * 4];
float *paddedG = new float[N * N * 4];
float *paddedH = new float[N * N * 4];
lisp
Copy
    padMatrix<<<(N / 2) / BLOCK_SIZE, BLOCK_SIZE>>>(A, paddedA, N);
    padMatrix<<<(N / 2) / BLOCK_SIZE, BLOCK_SIZE>>>(B, paddedB, N);

    // Calculate P1, P2, P3, P4, P5, P6, P7
    strassenStep<<<(N / 2) / BLOCK_SIZE, BLOCK_SIZE>>>(paddedA + 0 * N * halfN, paddedB + 0 * N * halfN, paddedC, halfN);
    strassenStep<<<(N / 2) / BLOCK_SIZE, BLOCK_SIZE>>>(paddedA + 0 * N * halfN, paddedB + 1 * N * halfN, paddedD, halfN);
    strassenStep<<<(N / 2) / BLOCK_SIZE, BLOCK_SIZE>>>(paddedA + 1 * N * halfN, paddedB + 0 * N * halfN, paddedE, halfN);
    strassenStep<<<(N / 2) / BLOCK_SIZE, BLOCK_SIZE>>>(paddedA + 1 * N * halfN, paddedB + 1 * N * halfN, paddedF, halfN);
    strassenStep<<<(N / 2) / BLOCK_SIZE, BLOCK_SIZE>>>(paddedA + 0 * N * halfN + halfN, paddedC + 0 * N * halfN + halfN, paddedG, halfN);
    strassenStep<<<(N / 2) / BLOCK_SIZE, BLOCK_SIZE>>>(paddedE + 0 * N * halfN + halfN, paddedF + 1 * N * halfN + halfN, paddedH, halfN);
    strassenStep<<<(N / 2) / BLOCK_SIZE, BLOCK_SIZE>>>(paddedA + 0 * N * halfN + halfN, paddedB + 0 * N * halfN, paddedG + 0 * N * halfN, halfN);
    strassenStep<<<(N / 2) / BLOCK_SIZE, BLOCK_SIZE>>>(paddedA + 0 * N * halfN + halfN, paddedB + 1 * N * halfN + halfN, paddedH + 0 * N * halfN, halfN);
    strassenStep<<<(N / 2) / BLOCK_SIZE, BLOCK_SIZE>>>(paddedA + 1 * N * halfN, paddedB + 0 * N * halfN + halfN, paddedH + 1 * N * halfN, halfN);

    // Merge results to get C
    mergeMatrix<<<(N / 2) / BLOCK_SIZE, BLOCK_SIZE>>>(paddedA, paddedE, paddedC, paddedG, paddedC, paddedH, paddedC, paddedD, halfN);
    mergeMatrix<<<(N / 2) / BLOCK_SIZE, BLOCK_SIZE>>>(paddedA, paddedF, paddedC, paddedH, paddedC, paddedG, paddedC, paddedE, halfN);
    mergeMatrix<<<(N / 2) / BLOCK_SIZE, BLOCK_SIZE>>>(paddedD, paddedF, paddedC, paddedH, paddedC, paddedG, paddedC, paddedA, halfN);
    mergeMatrix<<<(N / 2) / BLOCK_SIZE, BLOCK_SIZE>>>(paddedD, paddedE, paddedC, paddedG, paddedC, paddedH, paddedC, paddedF, halfN);

    // Copy result back to device memory
    for (int i = 0; i < N * N; i++)
    {
        C[i] = paddedC[i];
    }

    delete[] paddedA;
    delete[] paddedB;
    delete[] paddedC;
    delete[] paddedD;
    delete[] paddedE;
    delete[] paddedF;
    delete[] paddedG;
    delete[] paddedH;
}
}
void multiplyMatrices(float *A, float *B, float *C, int N)
{
int Np = N;
while (Np % 2 == 0)
{
Np *= 2;
}
// Allocate host memory for padded matrices
float *hostPaddedA = new float[Np * Np];
float *hostPaddedB = new float[Np * Np];
memset(hostPaddedA, 0, Np * Np * sizeof(float));
memset(hostPaddedB, 0, Np * Np * sizeof(float));
// Pad matrices
for (int i = 0; i < N; i++)
{
for (int j = 0; j < N; j++)
{
hostPaddedA[i * Np + j] = A[i * N + j];
hostPaddedB[i * Np + j] = B[i * N + j];
}
}
// Allocate device memory
float *d_A, *d_B, *d_C, *d_paddedA, *d_paddedB;
cudaMalloc(&d_A, Np * Np * sizeof(float));
cudaMalloc(&d_B, Np * Np * sizeof(float));
cudaMalloc(&d_C, Np * Np * sizeof(float));
cudaMalloc(&d_paddedA, Np * Np * sizeof(float));
cudaMalloc(&d_paddedB, Np * Np * sizeof(float));
// Copy padded matrices to device
cudaMemcpy(d_paddedA, hostPaddedA, Np * Np * sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(d_paddedB, hostPaddedB, Np * Np * sizeof(float), cudaMemcpyHostToDevice);
// Set up identity matrix on the host
float *hostIdentity = new float[N * N];
memset(hostIdentity, 0, N * N * sizeof(float));
for (int i = 0; i < N; i++)
{
hostIdentity[i * N + i] = 1.0f;
}
// Pad identity matrix
float *hostPaddedIdentity = new float[Np * Np];
memset(hostPaddedIdentity, 0, Np * Np * sizeof(float));
for (int i = 0; i < N; i++)
{
for (int j = 0; j < N; j++)
{
hostPaddedIdentity[i * Np + j] = hostIdentity[i * N + j];
}
}
// Allocate device memory for padded identity
float *d_paddedIdentity;
cudaMalloc(&d_paddedIdentity, Np * Np * sizeof(float));
// Copy padded identity to device
cudaMemcpy(d_paddedIdentity, hostPaddedIdentity, Np * Np * sizeof(float), cudaMemcpyHostToDevice);
// Call kernel to perform Strassen's algorithm
strassenStep<<<(Np / 2) / BLOCK_SIZE, BLOCK_SIZE>>>(d_paddedA, d_paddedB, d_C, Np);
// Copy result back to host
cudaMemcpy(C, d_C, N * N * sizeof(float), cudaMemcpyDeviceToHost);
// Free device memory
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
cudaFree(d_paddedA);
cudaFree(d_paddedB);
cudaFree(d_paddedIdentity);
// Free host memory
delete[] hostPaddedA;
delete[] hostPaddedB;
delete[] hostIdentity;
delete[] hostPaddedIdentity;
// Check for errors
cudaError_t err = cudaGetLastError();
if (err != cudaSuccess)
{
printf("CUDA error: %s\n", cudaGetErrorString(err));
exit(-1);
}
}
int main()
{
int N = 128; // Change this to your desired matrix size
float *hostA = new float[N * N];
float *hostB = new float[N * N];
float *hostC = new float[N * N];
// Initialize matrices A and B with random values (or your own data)
srand(time(NULL));
for (int i = 0; i < N; i++)
{
for (int j = 0; j < N; j++)
{
hostA[i * N + j] = rand() / (float)(RAND_MAX);
hostB[i * N + j] = rand() / (float)(RAND_MAX);
}
}
// Call the function to multiply the matrices
multiplyMatrices(hostA, hostB, hostC, N);
// Print the result matrix (optional)
for (int i = 0; i < N; i++)
{
for (int j = 0; j < N; j++)
{
printf("%.2f ", hostC[i * N + j]);
}
printf("\n");
}
// Free host memory
delete[] hostA;
delete[] hostB;
delete[] hostC;
return 0;
}