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:
- Define a CUDA kernel that will handle the parallel part of the computation.
- Launch the kernel for each iteration of the outer loop.
- Copy the data to and from the GPU as necessary.
Let's get started:
c#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:
- You'll need to implement the logic for
can_pair_device
. - The RNA data and other parameters must be passed to the kernel and device function as necessary.
- This assumes
S
is stored as a single-dimensional array for simplicity, but you can adjust this if needed. - 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:
- Define the
can_pair
function both for the host (CPU) and device (GPU). - Set up the CPU version for the computation.
- Include error checking for CUDA calls.
- Compare the results from the GPU and CPU versions.
Here's the modified version:
c#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:
- Define a CUDA kernel for the parallel loop
h0
. - Loop over
w0
in the CPU. - For each
w0
, launch the CUDA kernel which computes the parallel portion ofh0
. - The innermost loop
i3
will be executed on each GPU thread corresponding to a particularh0
value.
Here's the CUDA code:
c#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:
isPair_device
andisPair_host
are placeholders for the actualisPair
function. Fill them with your actual implementation.- The logic assumes that
S
is stored as a 1D array for simplicity. 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. Let's adjust the code to use a 2D array for d_S
on the device, and also add CPU verification.
- We'll adjust the memory handling for a 2D array on the device.
- Incorporate the CPU verification after the GPU computation.
c#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][w0 - h0] = MAX(d_S[-h0][-h0 + i3] + d_S[-h0 + i3 + 1][w0 - h0], d_S[-h0][w0 - h0]);
}
d_S[-h0][w0 - h0] = MAX(d_S[-h0][w0 - h0], d_S[-h0 + 1][w0 - h0 - 1] + isPair_device(-h0, w0 - h0));
}
}
int main() {
int N = 3000;
int **h_S, **d_S, **cpu_S;
int *d_S_data;
h_S = (int**)malloc(N * sizeof(int*));
cpu_S = (int**)malloc(N * sizeof(int*));
for (int i = 0; i < N; i++) {
h_S[i] = (int*)malloc(N * sizeof(int));
cpu_S[i] = (int*)malloc(N * sizeof(int));
for (int j = 0; j < N; j++) {
h_S[i][j] = rand() % 100;
cpu_S[i][j] = h_S[i][j];
}
}
cudaMalloc(&d_S_data, N * N * sizeof(int));
cudaMalloc(&d_S, N * sizeof(int*));
int **h_S_array = (int **)malloc(N * sizeof(int *));
for (int i = 0; i < N; i++) {
h_S_array[i] = d_S_data + i * N;
}
cudaMemcpy(d_S, h_S_array, N * sizeof(int *), cudaMemcpyHostToDevice);
for (int i = 0; i < N; i++) {
cudaMemcpy(h_S_array[i], h_S[i], 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();
}
for (int i = 0; i < N; i++) {
cudaMemcpy(h_S[i], h_S_array[i], N * sizeof(int), cudaMemcpyDeviceToHost);
}
// CPU version for verification
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][w0 - h0] = MAX(cpu_S[-h0][-h0 + i3] + cpu_S[-h0 + i3 + 1][w0 - h0], cpu_S[-h0][w0 - h0]);
}
cpu_S[-h0][w0 - h0] = MAX(cpu_S[-h0][w0 - h0], cpu_S[-h0 + 1][w0 - h0 - 1] + isPair_host(-h0, w0 - h0));
}
}
// Validate results
for (int i = 0; i < N; i++) {
for (int j = 0; j < N; j++) {
assert(h_S[i][j] == cpu_S[i][j]);
}
}
printf("Validation successful!\n");
cudaFree(d_S_data);
cudaFree(d_S);
free(h_S_array);
for (int i = 0; i < N; i++) {
free(h_S[i]);
free(cpu_S[i]);
}
free(h_S);
free(cpu_S);
return 0;
}
In this version:
- We manage the 2D array for
d_S
using an array of pointers. - We perform a CPU verification after the GPU computation and check if the results match.
- Please replace the placeholder functions
isPair_device
andisPair_host
with your actualisPair
function logic.