Pluto and Dapt codes from GPT CUDA Traco code

To implement the given code in CUDA, we will focus on the parallelizable portion (loop over t4). We will make a kernel function that operates on the loop iterations of t4. The outer loop t2 will remain on the CPU, and the innermost loop t6 will be executed on each GPU thread corresponding to a particular t4 value.

Here's how the given code can be translated to CUDA:

1. Define a CUDA kernel that will handle the parallel part of the computation.
2. Launch the kernel for each iteration of the outer loop.
3. Copy the data to and from the GPU as necessary.

Let's get started:

cCopy code
#include <cuda_runtime.h>
#include <stdio.h>
#include <stdlib.h>
#include <assert.h>

#define MAX(a,b) ((a) > (b) ? (a) : (b))

// Device function for the can_pair logic
__device__ int can_pair_device(/* parameters */) {
    // This function's body will contain the logic of the `can_pair` function.
    // Fill this in with the correct implementation.
    return /* calculated value */;
}

__global__ void computeS_kernel(int N, int t2, int* d_S, /* RNA and other necessary parameters */) {
    int t4 = blockIdx.x * blockDim.x + threadIdx.x + t2;  // Adding t2 to get the correct starting point
    
    if(t4 <= N-1) {
        for (int t6=0; t6<=t2-1; t6++) {
            d_S[(-t2+t4)*N + t4] = MAX(d_S[(-t2+t4)*N + t6+(-t2+t4)] + d_S[(t6+(-t2+t4)+1)*N + t4], d_S[(-t2+t4)*N + t4]);
        }
        d_S[(-t2+t4)*N + t4] = MAX(d_S[(-t2+t4)*N + t4], d_S[(-t2+t4+1)*N + t4-1] + can_pair_device(/* parameters */));
    }
}

int main() {
    int N = 3000;  // Example size

    // Assume single dimensional array for simplicity
    int *h_S, *d_S;
    
    // Allocate and initialize host memory
    h_S = (int*)malloc(N * N * sizeof(int));
    for (int i = 0; i < N * N; i++) {
        h_S[i] = rand() % 100;  // Example initialization
    }

    // Allocate device memory
    cudaMalloc(&d_S, N * N * sizeof(int));

    // Copy data to device
    cudaMemcpy(d_S, h_S, N * N * sizeof(int), cudaMemcpyHostToDevice);

    int threadsPerBlock = 256;
    int numBlocks;

    if (N >= 2) {
        for (int t2=1; t2<=N-1; t2++) {
            numBlocks = (N - t2 + threadsPerBlock - 1) / threadsPerBlock; // This makes sure all required threads for t4 are launched
            computeS_kernel<<<numBlocks, threadsPerBlock>>>(N, t2, d_S /*, other parameters like RNA */);
            cudaDeviceSynchronize();
        }
    }

    // Copy results back to host
    cudaMemcpy(h_S, d_S, N * N * sizeof(int), cudaMemcpyDeviceToHost);

    // ... (Your code for verification and freeing memory)

    return 0;
}

This is a basic structure. Note that:

1. You'll need to implement the logic for can_pair_device.
2. The RNA data and other parameters must be passed to the kernel and device function as necessary.
3. This assumes S is stored as a single-dimensional array for simplicity, but you can adjust this if needed.
4. Error handling for CUDA calls is omitted for clarity. Make sure you include error checking in your actual implementation.

To make a compilable version that includes a comparison with the CPU version, we'll follow these steps:

1. Define the can_pair function both for the host (CPU) and device (GPU).
2. Set up the CPU version for the computation.
3. Include error checking for CUDA calls.
4. Compare the results from the GPU and CPU versions.

Here's the modified version:

cCopy code
#include <cuda_runtime.h>
#include <stdio.h>
#include <stdlib.h>
#include <assert.h>

#define MAX(a,b) ((a) > (b) ? (a) : (b))

// Mock definition for can_pair, replace with actual logic
int can_pair(int* RNA, int a, int b) {
    return a + b; // Placeholder
}

__device__ int can_pair_device(int* RNA, int a, int b) {
    return a + b; // Placeholder, update with actual logic
}

__global__ void computeS_kernel(int N, int t2, int* d_S, int* d_RNA) {
    int t4 = blockIdx.x * blockDim.x + threadIdx.x + t2;
    
    if(t4 <= N-1) {
        for (int t6=0; t6<=t2-1; t6++) {
            d_S[(-t2+t4)*N + t4] = MAX(d_S[(-t2+t4)*N + t6+(-t2+t4)] + d_S[(t6+(-t2+t4)+1)*N + t4], d_S[(-t2+t4)*N + t4]);
        }
        d_S[(-t2+t4)*N + t4] = MAX(d_S[(-t2+t4)*N + t4], d_S[(-t2+t4+1)*N + t4-1] + can_pair_device(d_RNA, (-t2+t4), t4));
    }
}

int main() {
    int N = 3000;
    int *h_S, *d_S, *h_RNA, *d_RNA, *cpu_S;

    // Initialize and allocate memory
    h_S = (int*)malloc(N * N * sizeof(int));
    cpu_S = (int*)malloc(N * N * sizeof(int));
    h_RNA = (int*)malloc(N * sizeof(int));
    for (int i = 0; i < N * N; i++) {
        h_S[i] = rand() % 100;
        cpu_S[i] = h_S[i];
    }
    for (int i = 0; i < N; i++) {
        h_RNA[i] = rand() % 100;
    }

    cudaMalloc(&d_S, N * N * sizeof(int));
    cudaMalloc(&d_RNA, N * sizeof(int));

    cudaMemcpy(d_S, h_S, N * N * sizeof(int), cudaMemcpyHostToDevice);
    cudaMemcpy(d_RNA, h_RNA, N * sizeof(int), cudaMemcpyHostToDevice);

    int threadsPerBlock = 256;
    int numBlocks;

    if (N >= 2) {
        for (int t2=1; t2<=N-1; t2++) {
            numBlocks = (N - t2 + threadsPerBlock - 1) / threadsPerBlock;
            computeS_kernel<<<numBlocks, threadsPerBlock>>>(N, t2, d_S, d_RNA);
            cudaDeviceSynchronize();
        }
    }

    cudaMemcpy(h_S, d_S, N * N * sizeof(int), cudaMemcpyDeviceToHost);

    // CPU version
    for (int t2=1; t2<=N-1; t2++) {
        for (int t4=t2; t4<=N-1; t4++) {
            for (int t6=0; t6<=t2-1; t6++) {
                cpu_S[(-t2+t4)*N + t4] = MAX(cpu_S[(-t2+t4)*N + t6+(-t2+t4)] + cpu_S[(t6+(-t2+t4)+1)*N + t4], cpu_S[(-t2+t4)*N + t4]);
            }
            cpu_S[(-t2+t4)*N + t4] = MAX(cpu_S[(-t2+t4)*N + t4], cpu_S[(-t2+t4+1)*N + t4-1] + can_pair(h_RNA, (-t2+t4), t4));
        }
    }

    // Validate results
    for (int i = 0; i < N * N; i++) {
        assert(h_S[i] == cpu_S[i]);
    }

    printf("Validation successful!\n");

    cudaFree(d_S);
    cudaFree(d_RNA);

    free(h_S);
    free(cpu_S);
    free(h_RNA);

    return 0;
}

Notes:

• can_pair is a placeholder function. Replace it with the actual logic.
• This code assumes the RNA sequence is of size N and is an array of integers. Adjust as needed for your data.
• This code initializes the RNA sequence randomly, similar to the S matrix. Adjust as needed for your data.
• Error checking for CUDA calls is still omitted for clarity. Make sure you include error checking in your actual implementation.

Certainly! The transformation process is similar to the previous case, but we have to modify it based on the updated loop structure and the new isPair function.

Here's how the CUDA version of the code could look:

1. Define a CUDA kernel for the parallel loop h0.
2. Loop over w0 in the CPU.
3. For each w0, launch the CUDA kernel which computes the parallel portion of h0.
4. The innermost loop i3 will be executed on each GPU thread corresponding to a particular h0 value.

Here's the CUDA code:

cCopy code
#include <cuda_runtime.h>
#include <stdio.h>
#include <stdlib.h>
#include <assert.h>

#define MAX(a,b) ((a) > (b) ? (a) : (b))

// Placeholder for the isPair function. Replace with your actual implementation.
__device__ int isPair_device(int a, int b) {
    return a + b; // Placeholder logic
}

int isPair_host(int a, int b) {
    return a + b; // Placeholder logic
}

__global__ void computeS_kernel(int N, int w0, int* d_S) {
    int h0 = blockIdx.x * blockDim.x + threadIdx.x - N + w0 + 1;

    if (h0 <= 0) {
        for (int i3 = 0; i3 < w0; i3++) {
            d_S[-h0*N + w0 - h0] = MAX(d_S[-h0*N + (-h0 + i3)] + d_S[(-h0 + i3 + 1)*N + w0 - h0], d_S[-h0*N + w0 - h0]);
        }
        d_S[-h0*N + w0 - h0] = MAX(d_S[-h0*N + w0 - h0], d_S[(-h0 + 1)*N + w0 - h0 - 1] + isPair_device(-h0, w0 - h0));
    }
}

int main() {
    int N = 3000;
    int *h_S, *d_S, *cpu_S;

    h_S = (int*)malloc(N * N * sizeof(int));
    cpu_S = (int*)malloc(N * N * sizeof(int));
    for (int i = 0; i < N * N; i++) {
        h_S[i] = rand() % 100;
        cpu_S[i] = h_S[i];
    }

    cudaMalloc(&d_S, N * N * sizeof(int));
    cudaMemcpy(d_S, h_S, N * N * sizeof(int), cudaMemcpyHostToDevice);

    int threadsPerBlock = 256;
    int numBlocks;

    for (int w0 = 1; w0 < N; w0++) {
        numBlocks = (N + threadsPerBlock - 1) / threadsPerBlock;
        computeS_kernel<<<numBlocks, threadsPerBlock>>>(N, w0, d_S);
        cudaDeviceSynchronize();
    }

    cudaMemcpy(h_S, d_S, N * N * sizeof(int), cudaMemcpyDeviceToHost);

    // CPU version for comparison
    for (int w0 = 1; w0 < N; w0++) {
        for (int h0 = -N + w0 + 1; h0 <= 0; h0++) {
            for (int i3 = 0; i3 < w0; i3++) {
                cpu_S[-h0*N + w0 - h0] = MAX(cpu_S[-h0*N + (-h0 + i3)] + cpu_S[(-h0 + i3 + 1)*N + w0 - h0], cpu_S[-h0*N + w0 - h0]);
            }
            cpu_S[-h0*N + w0 - h0] = MAX(cpu_S[-h0*N + w0 - h0], cpu_S[(-h0 + 1)*N + w0 - h0 - 1] + isPair_host(-h0, w0 - h0));
        }
    }

    // Validate results
    for (int i = 0; i < N * N; i++) {
        assert(h_S[i] == cpu_S[i]);
    }

    printf("Validation successful!\n");

    cudaFree(d_S);
    free(h_S);
    free(cpu_S);

    return 0;
}

Things to note:

1. isPair_device and isPair_host are placeholders for the actual isPair function. Fill them with your actual implementation.
2. The logic assumes that S is stored as a 1D array for simplicity. Adjust as needed for your data.
3. Error checking for CUDA calls is still omitted for clarity. Make sure you include error checking in your actual implementation.