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Convert the following CUDA code to AMD GPU code:
cuda
#include <iostream>
#include <cuda_runtime.h>
#include <curand_kernel.h>
#define N 256
#define DT 0.001f
#define CUTOFF 1.0f
#define EPS 1e-6f
// Structure to hold particle data
struct Particle {
float3 pos;
float3 vel;
float3 acc;
};
// Kernel to initialize particles
__global__ void initParticles(Particle *p, curandState *states) {
int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < N) {
// Initialize random state
curand_init(1234, idx, 0, &states[idx]);
// Randomly initialize positions and velocities
p[idx].pos.x = curand_uniform(&states[idx]) * 10.0f;
p[idx].pos.y = curand_uniform(&states[idx]) * 10.0f;
p[idx].pos.z = curand_uniform(&states[idx]) * 10.0f;
p[idx].vel.x = curand_uniform(&states[idx]) * 0.1f - 0.05f;
p[idx].vel.y = curand_uniform(&states[idx]) * 0.1f - 0.05f;
p[idx].vel.z = curand_uniform(&states[idx]) * 0.1f - 0.05f;
p[idx].acc.x = 0.0f;
p[idx].acc.y = 0.0f;
p[idx].acc.z = 0.0f;
}
}
// Kernel to compute forces and update positions
__global__ void computeForcesAndPositions(Particle *p, float dt) {
extern __shared__ Particle sdata[];
int idx = threadIdx.x + blockIdx.x * blockDim.x;
int tid = threadIdx.x;
if (idx < N) {
// Load data into shared memory
sdata[tid] = p[idx];
__syncthreads();
// Initialize acceleration
float3 acc = {0.0f, 0.0f, 0.0f};
// Compute forces using shared memory
for (int i = 0; i < blockDim.x; ++i) {
if (i != tid) {
float3 r;
r.x = sdata[i].pos.x - sdata[tid].pos.x;
r.y = sdata[i].pos.y - sdata[tid].pos.y;
r.z = sdata[i].pos.z - sdata[tid].pos.z;
float rsq = r.x * r.x + r.y * r.y + r.z * r.z;
if (rsq < CUTOFF * CUTOFF && rsq > EPS) {
float rinv = rsq > EPS ? 1.0f / sqrtf(rsq) : 0.0f;
float force = 24.0f * (2.0f * powf(rinv, 6) - powf(rinv, 12)) * rinv * rinv * rinv;
acc.x += force * r.x;
acc.y += force * r.y;
acc.z += force * r.z;
}
}
}
__syncthreads();
// Update positions and velocities
p[idx].vel.x += 0.5f * acc.x * dt;
p[idx].vel.y += 0.5f * acc.y * dt;
p[idx].vel.z += 0.5f * acc.z * dt;
p[idx].pos.x += p[idx].vel.x * dt;
p[idx].pos.y += p[idx].vel.y * dt;
p[idx].pos.z += p[idx].vel.z * dt;
p[idx].acc.x = acc.x;
p[idx].acc.y = acc.y;
p[idx].acc.z = acc.z;
}
}
// Kernel to update velocities
__global__ void updateVelocities(Particle *p, float dt) {
int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < N) {
p[idx].vel.x += 0.5f * p[idx].acc.x * dt;
p[idx].vel.y += 0.5f * p[idx].acc.y * dt;
p[idx].vel.z += 0.5f * p[idx].acc.z * dt;
}
}
int main() {
Particle *d_particles;
curandState *d_states;
const int blockSize = 256;
const int numBlocks = (N + blockSize - 1) / blockSize;
const size_t particleSize = N * sizeof(Particle);
const size_t stateSize = N * sizeof(curandState);
// Allocate memory on the device
cudaError_t err = cudaMalloc((void**)&d_particles, particleSize);
if (err != cudaSuccess) {
std::cerr << "Failed to allocate memory for particles" << std::endl;
return -1;
}
err = cudaMalloc((void**)&d_states, stateSize);
if (err != cudaSuccess) {
std::cerr << "Failed to allocate memory for random states" << std::endl;
cudaFree(d_particles);
return -1;
}
// Initialize particles
initParticles<<<numBlocks, blockSize>>>(d_particles, d_states);
err = cudaGetLastError();
if (err != cudaSuccess) {
std::cerr << "Failed to initialize particles" << std::endl;
cudaFree(d_particles);
cudaFree(d_states);
return -1;
}
// Perform molecular dynamics simulation
for (int step = 0; step < 100; ++step) {
computeForcesAndPositions<<<numBlocks, blockSize, blockSize * sizeof(Particle)>>>(d_particles, DT);
err = cudaGetLastError();
if (err != cudaSuccess) {
std::cerr << "Failed to compute forces and positions" << std::endl;
cudaFree(d_particles);
cudaFree(d_states);
return -1;
}
updateVelocities<<<numBlocks, blockSize>>>(d_particles, DT);
err = cudaGetLastError();
if (err != cudaSuccess) {
std::cerr << "Failed to update velocities" << std::endl;
cudaFree(d_particles);
cudaFree(d_states);
return -1;
}
}
// Free device memory
cudaFree(d_particles);
cudaFree(d_states);
return 0;
}
|
#include <iostream>
#include <hip/hip_runtime.h>
#include <hiprand/hiprand_kernel.h>
#define N 256
#define DT 0.001f
#define CUTOFF 1.0f
#define EPS 1e-6f
// Structure to hold particle data
struct Particle {
float3 pos;
float3 vel;
float3 acc;
};
// Kernel to initialize particles
__global__ void initParticles(Particle *p, hiprandState *states) {
int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < N) {
// Initialize random state
hiprand_init(1234, idx, 0, &states[idx]);
// Randomly initialize positions and velocities
p[idx].pos.x = hiprand_uniform(&states[idx]) * 10.0f;
p[idx].pos.y = hiprand_uniform(&states[idx]) * 10.0f;
p[idx].pos.z = hiprand_uniform(&states[idx]) * 10.0f;
p[idx].vel.x = hiprand_uniform(&states[idx]) * 0.1f - 0.05f;
p[idx].vel.y = hiprand_uniform(&states[idx]) * 0.1f - 0.05f;
p[idx].vel.z = hiprand_uniform(&states[idx]) * 0.1f - 0.05f;
p[idx].acc.x = 0.0f;
p[idx].acc.y = 0.0f;
p[idx].acc.z = 0.0f;
}
}
// Kernel to compute forces and update positions
__global__ void computeForcesAndPositions(Particle *p, float dt) {
extern __shared__ Particle sdata[];
int idx = threadIdx.x + blockIdx.x * blockDim.x;
int tid = threadIdx.x;
if (idx < N) {
// Load data into shared memory
sdata[tid] = p[idx];
__syncthreads();
// Initialize acceleration
float3 acc = {0.0f, 0.0f, 0.0f};
// Compute forces using shared memory
for (int i = 0; i < blockDim.x; ++i) {
if (i != tid) {
float3 r;
r.x = sdata[i].pos.x - sdata[tid].pos.x;
r.y = sdata[i].pos.y - sdata[tid].pos.y;
r.z = sdata[i].pos.z - sdata[tid].pos.z;
float rsq = r.x * r.x + r.y * r.y + r.z * r.z;
if (rsq < CUTOFF * CUTOFF && rsq > EPS) {
float rinv = rsq > EPS ? 1.0f / sqrtf(rsq) : 0.0f;
float force = 24.0f * (2.0f * powf(rinv, 6) - powf(rinv, 12)) * rinv * rinv * rinv;
acc.x += force * r.x;
acc.y += force * r.y;
acc.z += force * r.z;
}
}
}
__syncthreads();
// Update positions and velocities
p[idx].vel.x += 0.5f * acc.x * dt;
p[idx].vel.y += 0.5f * acc.y * dt;
p[idx].vel.z += 0.5f * acc.z * dt;
p[idx].pos.x += p[idx].vel.x * dt;
p[idx].pos.y += p[idx].vel.y * dt;
p[idx].pos.z += p[idx].vel.z * dt;
p[idx].acc.x = acc.x;
p[idx].acc.y = acc.y;
p[idx].acc.z = acc.z;
}
}
// Kernel to update velocities
__global__ void updateVelocities(Particle *p, float dt) {
int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < N) {
p[idx].vel.x += 0.5f * p[idx].acc.x * dt;
p[idx].vel.y += 0.5f * p[idx].acc.y * dt;
p[idx].vel.z += 0.5f * p[idx].acc.z * dt;
}
}
int main() {
Particle *d_particles;
hiprandState *d_states;
const int blockSize = 256;
const int numBlocks = (N + blockSize - 1) / blockSize;
const size_t particleSize = N * sizeof(Particle);
const size_t stateSize = N * sizeof(hiprandState);
// Allocate memory on the device
hipError_t err = hipMalloc((void**)&d_particles, particleSize);
if (err != hipSuccess) {
std::cerr << "Failed to allocate memory for particles" << std::endl;
return -1;
}
err = hipMalloc((void**)&d_states, stateSize);
if (err != hipSuccess) {
std::cerr << "Failed to allocate memory for random states" << std::endl;
hipFree(d_particles);
return -1;
}
// Initialize particles
initParticles<<<numBlocks, blockSize>>>(d_particles, d_states);
err = hipGetLastError();
if (err != hipSuccess) {
std::cerr << "Failed to initialize particles" << std::endl;
hipFree(d_particles);
hipFree(d_states);
return -1;
}
// Perform molecular dynamics simulation
for (int step = 0; step < 100; ++step) {
computeForcesAndPositions<<<numBlocks, blockSize, blockSize * sizeof(Particle)>>>(d_particles, DT);
err = hipGetLastError();
if (err != hipSuccess) {
std::cerr << "Failed to compute forces and positions" << std::endl;
hipFree(d_particles);
hipFree(d_states);
return -1;
}
updateVelocities<<<numBlocks, blockSize>>>(d_particles, DT);
err = hipGetLastError();
if (err != hipSuccess) {
std::cerr << "Failed to update velocities" << std::endl;
hipFree(d_particles);
hipFree(d_states);
return -1;
}
}
// Free device memory
hipFree(d_particles);
hipFree(d_states);
return 0;
}
|
Convert the following CUDA code to AMD GPU code:
cuda
#include <iostream>
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <vector>
// Define the number of non-zero elements and matrix dimensions
#define NNZ 128
#define ROWS 128
#define COLS 128
// Kernel for sparse matrix-vector multiplication
__global__ void spmv_csr_kernel(const int* rowPtr, const int* colInd, const float* values, const float* x, float* y) {
int row = blockIdx.x * blockDim.x + threadIdx.x;
if (row < ROWS) {
float sum = 0.0f;
for (int jj = rowPtr[row]; jj < rowPtr[row + 1]; ++jj) {
sum += values[jj] * x[colInd[jj]];
}
y[row] = sum;
}
}
// Function to initialize CSR format matrices
void init_csr(int* rowPtr, int* colInd, float* values) {
int nnz = 0;
for (int i = 0; i < ROWS; ++i) {
rowPtr[i] = nnz;
for (int j = 0; j < COLS; ++j) {
if (rand() % 10 < 2) { // Sparse pattern: ~20% non-zero
colInd[nnz] = j;
values[nnz] = static_cast<float>(rand()) / RAND_MAX;
nnz++;
}
}
}
rowPtr[ROWS] = nnz;
}
int main() {
// Host data
int h_rowPtr[ROWS + 1];
int h_colInd[NNZ];
float h_values[NNZ];
float h_x[COLS];
float h_y[ROWS];
// Initialize host data
srand(42);
init_csr(h_rowPtr, h_colInd, h_values);
for (int i = 0; i < COLS; ++i) h_x[i] = static_cast<float>(rand()) / RAND_MAX;
// Device data
int* d_rowPtr;
int* d_colInd;
float* d_values;
float* d_x;
float* d_y;
// Allocate device memory
cudaMalloc((void**)&d_rowPtr, (ROWS + 1) * sizeof(int));
cudaMalloc((void**)&d_colInd, NNZ * sizeof(int));
cudaMalloc((void**)&d_values, NNZ * sizeof(float));
cudaMalloc((void**)&d_x, COLS * sizeof(float));
cudaMalloc((void**)&d_y, ROWS * sizeof(float));
// Copy data to device
cudaMemcpy(d_rowPtr, h_rowPtr, (ROWS + 1) * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_colInd, h_colInd, NNZ * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_values, h_values, NNZ * sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(d_x, h_x, COLS * sizeof(float), cudaMemcpyHostToDevice);
// CUDA Graphs setup
cudaGraph_t graph;
cudaGraphExec_t graphExec;
cudaStream_t stream;
cudaStreamCreate(&stream);
cudaGraphCreate(&graph, 0);
// Record graph
cudaStreamBeginCapture(stream, cudaStreamCaptureModeGlobal);
spmv_csr_kernel<<<(ROWS + 255) / 256, 256, 0, stream>>>(d_rowPtr, d_colInd, d_values, d_x, d_y);
cudaStreamEndCapture(stream, &graph);
// Instantiate and launch graph
cudaGraphInstantiate(&graphExec, graph, NULL, NULL, 0);
cudaGraphLaunch(graphExec, stream);
cudaStreamSynchronize(stream);
// Copy result back to host
cudaMemcpy(h_y, d_y, ROWS * sizeof(float), cudaMemcpyDeviceToHost);
// Output result
std::cout << "First 10 elements of result vector y: ";
for (int i = 0; i < 10; ++i) {
std::cout << h_y[i] << " ";
}
std::cout << std::endl;
// Free resources
cudaFree(d_rowPtr);
cudaFree(d_colInd);
cudaFree(d_values);
cudaFree(d_x);
cudaFree(d_y);
cudaGraphDestroy(graph);
cudaGraphExecDestroy(graphExec);
cudaStreamDestroy(stream);
return 0;
}
|
#include <iostream>
#include <hip/hip_runtime.h>
#include <hip/hip_fp16.h>
#include <vector>
// Define the number of non-zero elements and matrix dimensions
#define NNZ 128
#define ROWS 128
#define COLS 128
// Kernel for sparse matrix-vector multiplication
__global__ void spmv_csr_kernel(const int* rowPtr, const int* colInd, const float* values, const float* x, float* y) {
int row = blockIdx.x * blockDim.x + threadIdx.x;
if (row < ROWS) {
float sum = 0.0f;
for (int jj = rowPtr[row]; jj < rowPtr[row + 1]; ++jj) {
sum += values[jj] * x[colInd[jj]];
}
y[row] = sum;
}
}
// Function to initialize CSR format matrices
void init_csr(int* rowPtr, int* colInd, float* values) {
int nnz = 0;
for (int i = 0; i < ROWS; ++i) {
rowPtr[i] = nnz;
for (int j = 0; j < COLS; ++j) {
if (rand() % 10 < 2) { // Sparse pattern: ~20% non-zero
colInd[nnz] = j;
values[nnz] = static_cast<float>(rand()) / RAND_MAX;
nnz++;
}
}
}
rowPtr[ROWS] = nnz;
}
int main() {
// Host data
int h_rowPtr[ROWS + 1];
int h_colInd[NNZ];
float h_values[NNZ];
float h_x[COLS];
float h_y[ROWS];
// Initialize host data
srand(42);
init_csr(h_rowPtr, h_colInd, h_values);
for (int i = 0; i < COLS; ++i) h_x[i] = static_cast<float>(rand()) / RAND_MAX;
// Device data
int* d_rowPtr;
int* d_colInd;
float* d_values;
float* d_x;
float* d_y;
// Allocate device memory
hipMalloc((void**)&d_rowPtr, (ROWS + 1) * sizeof(int));
hipMalloc((void**)&d_colInd, NNZ * sizeof(int));
hipMalloc((void**)&d_values, NNZ * sizeof(float));
hipMalloc((void**)&d_x, COLS * sizeof(float));
hipMalloc((void**)&d_y, ROWS * sizeof(float));
// Copy data to device
hipMemcpy(d_rowPtr, h_rowPtr, (ROWS + 1) * sizeof(int), hipMemcpyHostToDevice);
hipMemcpy(d_colInd, h_colInd, NNZ * sizeof(int), hipMemcpyHostToDevice);
hipMemcpy(d_values, h_values, NNZ * sizeof(float), hipMemcpyHostToDevice);
hipMemcpy(d_x, h_x, COLS * sizeof(float), hipMemcpyHostToDevice);
// CUDA Graphs setup
hipGraph_t graph;
hipGraphExec_t graphExec;
hipStream_t stream;
hipStreamCreate(&stream);
hipGraphCreate(&graph, 0);
// Record graph
hipStreamBeginCapture(stream, hipStreamCaptureModeGlobal);
spmv_csr_kernel<<<(ROWS + 255) / 256, 256, 0, stream>>>(d_rowPtr, d_colInd, d_values, d_x, d_y);
hipStreamEndCapture(stream, &graph);
// Instantiate and launch graph
hipGraphInstantiate(&graphExec, graph, NULL, NULL, 0);
hipGraphLaunch(graphExec, stream);
hipStreamSynchronize(stream);
// Copy result back to host
hipMemcpy(h_y, d_y, ROWS * sizeof(float), hipMemcpyDeviceToHost);
// Output result
std::cout << "First 10 elements of result vector y: ";
for (int i = 0; i < 10; ++i) {
std::cout << h_y[i] << " ";
}
std::cout << std::endl;
// Free resources
hipFree(d_rowPtr);
hipFree(d_colInd);
hipFree(d_values);
hipFree(d_x);
hipFree(d_y);
hipGraphDestroy(graph);
hipGraphExecDestroy(graphExec);
hipStreamDestroy(stream);
return 0;
}
|
Convert the following CUDA code to AMD GPU code:
cuda
#include <iostream>
#include <cuda_runtime.h>
#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <thrust/sort.h>
#include <thrust/scan.h>
#include <thrust/copy.h>
#include <thrust/fill.h>
#include <thrust/transform.h>
#include <thrust/adjacent_difference.h>
#include <thrust/extrema.h>
#include <thrust/device_ptr.h>
#include <thrust/execution_policy.h>
#include <curand_kernel.h>
#define NUM_NODES 16384
#define MAX_COLOR 1024
__global__ void initColors(int *colors, int *conflicts, int numNodes) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < numNodes) {
colors[idx] = 0; // Initialize all nodes with color 0 (unassigned)
conflicts[idx] = 0; // No conflicts initially
}
}
__global__ void assignColors(int *adjList, int *adjListPtrs, int *colors, int *conflicts, int numNodes) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < numNodes) {
int color = colors[idx];
for (int i = adjListPtrs[idx]; i < adjListPtrs[idx + 1]; ++i) {
int neighbor = adjList[i];
if (colors[neighbor] == color) {
conflicts[idx] = 1; // Mark conflict if neighbor has the same color
}
}
}
}
__global__ void resolveConflicts(int *adjList, int *adjListPtrs, int *colors, int *conflicts, int *conflictFlags, int numNodes) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < numNodes && conflicts[idx]) {
int newColor = 1;
bool found = false;
while (!found) {
found = true;
for (int i = adjListPtrs[idx]; i < adjListPtrs[idx + 1]; ++i) {
int neighbor = adjList[i];
if (colors[neighbor] == newColor) {
found = false;
newColor++;
break;
}
}
}
colors[idx] = newColor;
conflictFlags[idx] = 0; // Reset conflict flag
}
}
__global__ void checkForConflicts(int *conflicts, int *globalConflict, int numNodes) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < numNodes && conflicts[idx]) {
atomicOr(globalConflict, 1);
}
}
int main() {
// Initialize random number generator
curandGenerator_t gen;
curandCreateGenerator(&gen, CURAND_RNG_PSEUDO_DEFAULT);
curandSetPseudoRandomGeneratorSeed(gen, 1234ULL);
// Allocate and initialize adjacency list and pointers
thrust::host_vector<int> h_adjListPtrs(NUM_NODES + 1);
thrust::host_vector<int> h_adjList;
h_adjListPtrs[0] = 0;
for (int i = 0; i < NUM_NODES; ++i) {
int degree = rand() % 10; // Random degree between 0 and 9
h_adjListPtrs[i + 1] = h_adjListPtrs[i] + degree;
for (int j = 0; j < degree; ++j) {
int neighbor = rand() % NUM_NODES;
while (neighbor == i) neighbor = rand() % NUM_NODES; // Ensure no self-loops
h_adjList.push_back(neighbor);
}
}
thrust::device_vector<int> d_adjListPtrs = h_adjListPtrs;
thrust::device_vector<int> d_adjList = h_adjList;
// Allocate memory for colors and conflicts
thrust::device_vector<int> d_colors(NUM_NODES);
thrust::device_vector<int> d_conflicts(NUM_NODES);
thrust::device_vector<int> d_conflictFlags(NUM_NODES);
// Initialize colors and conflicts
int blockSize = 256;
int numBlocks = (NUM_NODES + blockSize - 1) / blockSize;
initColors<<<numBlocks, blockSize>>>(thrust::raw_pointer_cast(d_colors.data()), thrust::raw_pointer_cast(d_conflicts.data()), NUM_NODES);
cudaDeviceSynchronize();
// Main graph coloring loop
int *d_globalConflict;
cudaMalloc(&d_globalConflict, sizeof(int));
int h_globalConflict = 1;
while (h_globalConflict) {
cudaMemset(d_globalConflict, 0, sizeof(int));
// Assign colors
assignColors<<<numBlocks, blockSize>>>(thrust::raw_pointer_cast(d_adjList.data()), thrust::raw_pointer_cast(d_adjListPtrs.data()), thrust::raw_pointer_cast(d_colors.data()), thrust::raw_pointer_cast(d_conflicts.data()), NUM_NODES);
cudaDeviceSynchronize();
// Resolve conflicts
resolveConflicts<<<numBlocks, blockSize>>>(thrust::raw_pointer_cast(d_adjList.data()), thrust::raw_pointer_cast(d_adjListPtrs.data()), thrust::raw_pointer_cast(d_colors.data()), thrust::raw_pointer_cast(d_conflicts.data()), thrust::raw_pointer_cast(d_conflictFlags.data()), NUM_NODES);
cudaDeviceSynchronize();
// Check for conflicts
checkForConflicts<<<numBlocks, blockSize>>>(thrust::raw_pointer_cast(d_conflicts.data()), d_globalConflict, NUM_NODES);
cudaDeviceSynchronize();
// Copy global conflict flag to host
cudaMemcpy(&h_globalConflict, d_globalConflict, sizeof(int), cudaMemcpyDeviceToHost);
}
// Free memory
cudaFree(d_globalConflict);
curandDestroyGenerator(gen);
// Print results (for verification purposes)
thrust::host_vector<int> h_colors = d_colors;
std::cout << "Node colors:" << std::endl;
for (int i = 0; i < 10; ++i) { // Print first 10 nodes for brevity
std::cout << "Node " << i << ": Color " << h_colors[i] << std::endl;
}
return 0;
}
|
#include <iostream>
#include <hip/hip_runtime.h>
#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <thrust/sort.h>
#include <thrust/scan.h>
#include <thrust/copy.h>
#include <thrust/fill.h>
#include <thrust/transform.h>
#include <thrust/adjacent_difference.h>
#include <thrust/extrema.h>
#include <thrust/device_ptr.h>
#include <thrust/execution_policy.h>
#include <hiprand/hiprand_kernel.h>
#define NUM_NODES 16384
#define MAX_COLOR 1024
__global__ void initColors(int *colors, int *conflicts, int numNodes) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < numNodes) {
colors[idx] = 0; // Initialize all nodes with color 0 (unassigned)
conflicts[idx] = 0; // No conflicts initially
}
}
__global__ void assignColors(int *adjList, int *adjListPtrs, int *colors, int *conflicts, int numNodes) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < numNodes) {
int color = colors[idx];
for (int i = adjListPtrs[idx]; i < adjListPtrs[idx + 1]; ++i) {
int neighbor = adjList[i];
if (colors[neighbor] == color) {
conflicts[idx] = 1; // Mark conflict if neighbor has the same color
}
}
}
}
__global__ void resolveConflicts(int *adjList, int *adjListPtrs, int *colors, int *conflicts, int *conflictFlags, int numNodes) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < numNodes && conflicts[idx]) {
int newColor = 1;
bool found = false;
while (!found) {
found = true;
for (int i = adjListPtrs[idx]; i < adjListPtrs[idx + 1]; ++i) {
int neighbor = adjList[i];
if (colors[neighbor] == newColor) {
found = false;
newColor++;
break;
}
}
}
colors[idx] = newColor;
conflictFlags[idx] = 0; // Reset conflict flag
}
}
__global__ void checkForConflicts(int *conflicts, int *globalConflict, int numNodes) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < numNodes && conflicts[idx]) {
atomicOr(globalConflict, 1);
}
}
int main() {
// Initialize random number generator
hiprandGenerator_t gen;
hiprandCreateGenerator(&gen, HIPRAND_RNG_PSEUDO_DEFAULT);
hiprandSetPseudoRandomGeneratorSeed(gen, 1234ULL);
// Allocate and initialize adjacency list and pointers
thrust::host_vector<int> h_adjListPtrs(NUM_NODES + 1);
thrust::host_vector<int> h_adjList;
h_adjListPtrs[0] = 0;
for (int i = 0; i < NUM_NODES; ++i) {
int degree = rand() % 10; // Random degree between 0 and 9
h_adjListPtrs[i + 1] = h_adjListPtrs[i] + degree;
for (int j = 0; j < degree; ++j) {
int neighbor = rand() % NUM_NODES;
while (neighbor == i) neighbor = rand() % NUM_NODES; // Ensure no self-loops
h_adjList.push_back(neighbor);
}
}
thrust::device_vector<int> d_adjListPtrs = h_adjListPtrs;
thrust::device_vector<int> d_adjList = h_adjList;
// Allocate memory for colors and conflicts
thrust::device_vector<int> d_colors(NUM_NODES);
thrust::device_vector<int> d_conflicts(NUM_NODES);
thrust::device_vector<int> d_conflictFlags(NUM_NODES);
// Initialize colors and conflicts
int blockSize = 256;
int numBlocks = (NUM_NODES + blockSize - 1) / blockSize;
initColors<<<numBlocks, blockSize>>>(thrust::raw_pointer_cast(d_colors.data()), thrust::raw_pointer_cast(d_conflicts.data()), NUM_NODES);
hipDeviceSynchronize();
// Main graph coloring loop
int *d_globalConflict;
hipMalloc(&d_globalConflict, sizeof(int));
int h_globalConflict = 1;
while (h_globalConflict) {
hipMemset(d_globalConflict, 0, sizeof(int));
// Assign colors
assignColors<<<numBlocks, blockSize>>>(thrust::raw_pointer_cast(d_adjList.data()), thrust::raw_pointer_cast(d_adjListPtrs.data()), thrust::raw_pointer_cast(d_colors.data()), thrust::raw_pointer_cast(d_conflicts.data()), NUM_NODES);
hipDeviceSynchronize();
// Resolve conflicts
resolveConflicts<<<numBlocks, blockSize>>>(thrust::raw_pointer_cast(d_adjList.data()), thrust::raw_pointer_cast(d_adjListPtrs.data()), thrust::raw_pointer_cast(d_colors.data()), thrust::raw_pointer_cast(d_conflicts.data()), thrust::raw_pointer_cast(d_conflictFlags.data()), NUM_NODES);
hipDeviceSynchronize();
// Check for conflicts
checkForConflicts<<<numBlocks, blockSize>>>(thrust::raw_pointer_cast(d_conflicts.data()), d_globalConflict, NUM_NODES);
hipDeviceSynchronize();
// Copy global conflict flag to host
hipMemcpy(&h_globalConflict, d_globalConflict, sizeof(int), hipMemcpyDeviceToHost);
}
// Free memory
hipFree(d_globalConflict);
hiprandDestroyGenerator(gen);
// Print results (for verification purposes)
thrust::host_vector<int> h_colors = d_colors;
std::cout << "Node colors:" << std::endl;
for (int i = 0; i < 10; ++i) { // Print first 10 nodes for brevity
std::cout << "Node " << i << ": Color " << h_colors[i] << std::endl;
}
return 0;
}
|
Convert the following CUDA code to AMD GPU code:
cuda
#include <iostream>
#include <curand_kernel.h>
#include <cuda_runtime.h>
// Define constants
#define PATHS_PER_BLOCK 128
#define PATHS 2048
#define SIMULATION_STEPS 100
#define MAX_DEPTH 1
// Kernel to initialize random number generators
__global__ void initRandomStates(curandState *states, unsigned long seed) {
int id = blockIdx.x * blockDim.x + threadIdx.x;
if (id < PATHS) {
curand_init(seed, id, 0, &states[id]);
}
}
// Device function to simulate a single path
__device__ float simulatePath(curandState *state) {
float S = 100.0f; // Initial stock price
float mu = 0.05f; // Drift
float sigma = 0.2f; // Volatility
float dt = 1.0f / SIMULATION_STEPS; // Time step
for (int i = 0; i < SIMULATION_STEPS; ++i) {
float epsilon = curand_normal(state);
S *= exp((mu - 0.5f * sigma * sigma) * dt + sigma * sqrt(dt) * epsilon);
}
return fmaxf(0.0f, S - 100.0f); // Payoff for a call option with strike 100
}
// Kernel to perform Monte Carlo simulation using dynamic parallelism
__global__ void monteCarloSimulation(curandState *states, float *results) {
int id = blockIdx.x * blockDim.x + threadIdx.x;
if (id < PATHS) {
float sum = 0.0f;
for (int i = 0; i < MAX_DEPTH; ++i) {
sum += simulatePath(&states[id]);
}
results[id] = sum / MAX_DEPTH;
}
}
// Main function to demonstrate the kernel's usage
int main() {
// Allocate memory for random states and results
curandState *d_states;
float *d_results;
cudaMalloc(&d_states, PATHS * sizeof(curandState));
cudaMalloc(&d_results, PATHS * sizeof(float));
// Initialize random states
initRandomStates<<<(PATHS + PATHS_PER_BLOCK - 1) / PATHS_PER_BLOCK, PATHS_PER_BLOCK>>>(d_states, 1234);
cudaDeviceSynchronize();
// Perform Monte Carlo simulation
monteCarloSimulation<<<(PATHS + PATHS_PER_BLOCK - 1) / PATHS_PER_BLOCK, PATHS_PER_BLOCK>>>(d_states, d_results);
cudaDeviceSynchronize();
// Copy results back to host
float *h_results = new float[PATHS];
cudaMemcpy(h_results, d_results, PATHS * sizeof(float), cudaMemcpyDeviceToHost);
// Calculate and print the average option price
float sum = 0.0f;
for (int i = 0; i < PATHS; ++i) {
sum += h_results[i];
}
float averagePrice = sum / PATHS;
std::cout << "Average Option Price: " << averagePrice << std::endl;
// Free allocated memory
delete[] h_results;
cudaFree(d_states);
cudaFree(d_results);
return 0;
}
|
#include <iostream>
#include <hiprand/hiprand_kernel.h>
#include <hip/hip_runtime.h>
// Define constants
#define PATHS_PER_BLOCK 128
#define PATHS 2048
#define SIMULATION_STEPS 100
#define MAX_DEPTH 1
// Kernel to initialize random number generators
__global__ void initRandomStates(hiprandState *states, unsigned long seed) {
int id = blockIdx.x * blockDim.x + threadIdx.x;
if (id < PATHS) {
hiprand_init(seed, id, 0, &states[id]);
}
}
// Device function to simulate a single path
__device__ float simulatePath(hiprandState *state) {
float S = 100.0f; // Initial stock price
float mu = 0.05f; // Drift
float sigma = 0.2f; // Volatility
float dt = 1.0f / SIMULATION_STEPS; // Time step
for (int i = 0; i < SIMULATION_STEPS; ++i) {
float epsilon = hiprand_normal(state);
S *= exp((mu - 0.5f * sigma * sigma) * dt + sigma * sqrt(dt) * epsilon);
}
return fmaxf(0.0f, S - 100.0f); // Payoff for a call option with strike 100
}
// Kernel to perform Monte Carlo simulation using dynamic parallelism
__global__ void monteCarloSimulation(hiprandState *states, float *results) {
int id = blockIdx.x * blockDim.x + threadIdx.x;
if (id < PATHS) {
float sum = 0.0f;
for (int i = 0; i < MAX_DEPTH; ++i) {
sum += simulatePath(&states[id]);
}
results[id] = sum / MAX_DEPTH;
}
}
// Main function to demonstrate the kernel's usage
int main() {
// Allocate memory for random states and results
hiprandState *d_states;
float *d_results;
hipMalloc(&d_states, PATHS * sizeof(hiprandState));
hipMalloc(&d_results, PATHS * sizeof(float));
// Initialize random states
initRandomStates<<<(PATHS + PATHS_PER_BLOCK - 1) / PATHS_PER_BLOCK, PATHS_PER_BLOCK>>>(d_states, 1234);
hipDeviceSynchronize();
// Perform Monte Carlo simulation
monteCarloSimulation<<<(PATHS + PATHS_PER_BLOCK - 1) / PATHS_PER_BLOCK, PATHS_PER_BLOCK>>>(d_states, d_results);
hipDeviceSynchronize();
// Copy results back to host
float *h_results = new float[PATHS];
hipMemcpy(h_results, d_results, PATHS * sizeof(float), hipMemcpyDeviceToHost);
// Calculate and print the average option price
float sum = 0.0f;
for (int i = 0; i < PATHS; ++i) {
sum += h_results[i];
}
float averagePrice = sum / PATHS;
std::cout << "Average Option Price: " << averagePrice << std::endl;
// Free allocated memory
delete[] h_results;
hipFree(d_states);
hipFree(d_results);
return 0;
}
|
Convert the following CUDA code to AMD GPU code:
cuda
#include <iostream>
#include <cuda_runtime.h>
#include <cooperative_groups.h>
namespace cg = cooperative_groups;
#define BLOCK_SIZE 32
#define GRID_SIZE 64
__global__ void heat2DExplicitKernel(float* d_u, float* d_u_new, float dx, float dy, float dt, float alpha, int N) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
int j = blockIdx.y * blockDim.y + threadIdx.y;
if (i > 0 && i < N-1 && j > 0 && j < N-1) {
int idx = i * N + j;
float laplacian = (d_u[idx - N] + d_u[idx + N] + d_u[idx - 1] + d_u[idx + 1] - 4 * d_u[idx]) / (dx * dx + dy * dy);
d_u_new[idx] = d_u[idx] + alpha * dt * laplacian;
}
}
__global__ void swapPointers(float** d_u, float** d_u_new) {
float* temp = *d_u;
*d_u = *d_u_new;
*d_u_new = temp;
}
int main() {
const int N = 2048;
const float dx = 1.0f / (N - 1);
const float dy = dx;
const float dt = 0.0001f;
const float alpha = 0.01f;
const int num_steps = 100;
float* h_u = new float[N * N];
float* h_u_new = new float[N * N];
// Initialize the grid with a simple condition (e.g., a single hot spot)
for (int i = 0; i < N; ++i) {
for (int j = 0; j < N; ++j) {
h_u[i * N + j] = 0.0f;
}
}
h_u[(N/2) * N + N/2] = 100.0f;
float* d_u;
float* d_u_new;
cudaMalloc(&d_u, N * N * sizeof(float));
cudaMalloc(&d_u_new, N * N * sizeof(float));
cudaMemcpy(d_u, h_u, N * N * sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(d_u_new, h_u_new, N * N * sizeof(float), cudaMemcpyHostToDevice);
dim3 blockSize(BLOCK_SIZE, BLOCK_SIZE);
dim3 gridSize(GRID_SIZE, GRID_SIZE);
float* d_u_ptr = d_u;
float* d_u_new_ptr = d_u_new;
for (int step = 0; step < num_steps; ++step) {
heat2DExplicitKernel<<<gridSize, blockSize>>>(d_u_ptr, d_u_new_ptr, dx, dy, dt, alpha, N);
cudaDeviceSynchronize();
swapPointers<<<1, 1>>>(&d_u_ptr, &d_u_new_ptr);
cudaDeviceSynchronize();
}
cudaMemcpy(h_u, d_u_ptr, N * N * sizeof(float), cudaMemcpyDeviceToHost);
// Output the result (for demonstration, print a small portion)
for (int i = 0; i < 10; ++i) {
for (int j = 0; j < 10; ++j) {
std::cout << h_u[i * N + j] << " ";
}
std::cout << std::endl;
}
cudaFree(d_u);
cudaFree(d_u_new);
delete[] h_u;
delete[] h_u_new;
return 0;
}
|
#include <iostream>
#include <hip/hip_runtime.h>
#include <hip/hip_cooperative_groups.h>
namespace cg = cooperative_groups;
#define BLOCK_SIZE 32
#define GRID_SIZE 64
__global__ void heat2DExplicitKernel(float* d_u, float* d_u_new, float dx, float dy, float dt, float alpha, int N) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
int j = blockIdx.y * blockDim.y + threadIdx.y;
if (i > 0 && i < N-1 && j > 0 && j < N-1) {
int idx = i * N + j;
float laplacian = (d_u[idx - N] + d_u[idx + N] + d_u[idx - 1] + d_u[idx + 1] - 4 * d_u[idx]) / (dx * dx + dy * dy);
d_u_new[idx] = d_u[idx] + alpha * dt * laplacian;
}
}
__global__ void swapPointers(float** d_u, float** d_u_new) {
float* temp = *d_u;
*d_u = *d_u_new;
*d_u_new = temp;
}
int main() {
const int N = 2048;
const float dx = 1.0f / (N - 1);
const float dy = dx;
const float dt = 0.0001f;
const float alpha = 0.01f;
const int num_steps = 100;
float* h_u = new float[N * N];
float* h_u_new = new float[N * N];
// Initialize the grid with a simple condition (e.g., a single hot spot)
for (int i = 0; i < N; ++i) {
for (int j = 0; j < N; ++j) {
h_u[i * N + j] = 0.0f;
}
}
h_u[(N/2) * N + N/2] = 100.0f;
float* d_u;
float* d_u_new;
hipMalloc(&d_u, N * N * sizeof(float));
hipMalloc(&d_u_new, N * N * sizeof(float));
hipMemcpy(d_u, h_u, N * N * sizeof(float), hipMemcpyHostToDevice);
hipMemcpy(d_u_new, h_u_new, N * N * sizeof(float), hipMemcpyHostToDevice);
dim3 blockSize(BLOCK_SIZE, BLOCK_SIZE);
dim3 gridSize(GRID_SIZE, GRID_SIZE);
float* d_u_ptr = d_u;
float* d_u_new_ptr = d_u_new;
for (int step = 0; step < num_steps; ++step) {
heat2DExplicitKernel<<<gridSize, blockSize>>>(d_u_ptr, d_u_new_ptr, dx, dy, dt, alpha, N);
hipDeviceSynchronize();
swapPointers<<<1, 1>>>(&d_u_ptr, &d_u_new_ptr);
hipDeviceSynchronize();
}
hipMemcpy(h_u, d_u_ptr, N * N * sizeof(float), hipMemcpyDeviceToHost);
// Output the result (for demonstration, print a small portion)
for (int i = 0; i < 10; ++i) {
for (int j = 0; j < 10; ++j) {
std::cout << h_u[i * N + j] << " ";
}
std::cout << std::endl;
}
hipFree(d_u);
hipFree(d_u_new);
delete[] h_u;
delete[] h_u_new;
return 0;
}
|
Convert the following CUDA code to AMD GPU code:
cuda
#include <iostream>
#include <cuda_runtime.h>
#include <cooperative_groups.h>
#include <cuComplex.h>
using namespace cooperative_groups;
#define N 2048
#define BLOCK_SIZE 256
__global__ void spectralFluidKernel(cuComplex *d_u_hat, cuComplex *d_v_hat, cuComplex *d_u, cuComplex *d_v, float dt, float nu) {
grid_group grid = this_grid();
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx >= N) return;
// Precompute wave numbers
int k = idx <= N/2 ? idx : idx - N;
float k_squared = k * k;
// Apply diffusion term
cuComplex diffusion_term = make_cuComplex(-nu * dt * k_squared, 0.0f);
// Update Fourier coefficients with diffusion
d_u_hat[idx] = cuCaddf(d_u_hat[idx], cuCmulf(diffusion_term, d_u_hat[idx]));
d_v_hat[idx] = cuCaddf(d_v_hat[idx], cuCmulf(diffusion_term, d_v_hat[idx]));
// Perform inverse FFT to get back to physical space
// Placeholder for inverse FFT operation
// In practice, use cuFFT or a similar library
d_u[idx] = d_u_hat[idx]; // Placeholder
d_v[idx] = d_v_hat[idx]; // Placeholder
// Synchronize threads within the grid
grid.sync();
}
int main() {
// Allocate memory for input and output arrays
cuComplex *d_u_hat, *d_v_hat, *d_u, *d_v;
cudaMalloc((void**)&d_u_hat, N * sizeof(cuComplex));
cudaMalloc((void**)&d_v_hat, N * sizeof(cuComplex));
cudaMalloc((void**)&d_u, N * sizeof(cuComplex));
cudaMalloc((void**)&d_v, N * sizeof(cuComplex));
// Initialize input arrays (dummy values)
cuComplex *h_u_hat = new cuComplex[N];
cuComplex *h_v_hat = new cuComplex[N];
for (int i = 0; i < N; ++i) {
h_u_hat[i] = make_cuComplex(1.0f, 0.0f);
h_v_hat[i] = make_cuComplex(1.0f, 0.0f);
}
cudaMemcpy(d_u_hat, h_u_hat, N * sizeof(cuComplex), cudaMemcpyHostToDevice);
cudaMemcpy(d_v_hat, h_v_hat, N * sizeof(cuComplex), cudaMemcpyHostToDevice);
// Set simulation parameters
float dt = 0.01f;
float nu = 0.001f;
// Launch kernel
int numBlocks = (N + BLOCK_SIZE - 1) / BLOCK_SIZE;
spectralFluidKernel<<<numBlocks, BLOCK_SIZE>>>(d_u_hat, d_v_hat, d_u, d_v, dt, nu);
// Check for errors in kernel launch
cudaError_t err = cudaGetLastError();
if (err != cudaSuccess) {
std::cerr << "CUDA error: " << cudaGetErrorString(err) << std::endl;
return -1;
}
// Copy results back to host
cuComplex *h_u = new cuComplex[N];
cuComplex *h_v = new cuComplex[N];
cudaMemcpy(h_u, d_u, N * sizeof(cuComplex), cudaMemcpyDeviceToHost);
cudaMemcpy(h_v, d_v, N * sizeof(cuComplex), cudaMemcpyDeviceToHost);
// Output first few results for verification
for (int i = 0; i < 10; ++i) {
std::cout << "u[" << i << "] = (" << h_u[i].x << ", " << h_u[i].y << ")\n";
std::cout << "v[" << i << "] = (" << h_v[i].x << ", " << h_v[i].y << ")\n";
}
// Free memory
delete[] h_u_hat;
delete[] h_v_hat;
delete[] h_u;
delete[] h_v;
cudaFree(d_u_hat);
cudaFree(d_v_hat);
cudaFree(d_u);
cudaFree(d_v);
return 0;
}
|
#include <iostream>
#include <hip/hip_runtime.h>
#include <hip/hip_cooperative_groups.h>
#include <hip/hip_complex.h>
using namespace cooperative_groups;
#define N 2048
#define BLOCK_SIZE 256
__global__ void spectralFluidKernel(hipComplex *d_u_hat, hipComplex *d_v_hat, hipComplex *d_u, hipComplex *d_v, float dt, float nu) {
grid_group grid = this_grid();
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx >= N) return;
// Precompute wave numbers
int k = idx <= N/2 ? idx : idx - N;
float k_squared = k * k;
// Apply diffusion term
hipComplex diffusion_term = make_hipComplex(-nu * dt * k_squared, 0.0f);
// Update Fourier coefficients with diffusion
d_u_hat[idx] = hipCaddf(d_u_hat[idx], hipCmulf(diffusion_term, d_u_hat[idx]));
d_v_hat[idx] = hipCaddf(d_v_hat[idx], hipCmulf(diffusion_term, d_v_hat[idx]));
// Perform inverse FFT to get back to physical space
// Placeholder for inverse FFT operation
// In practice, use cuFFT or a similar library
d_u[idx] = d_u_hat[idx]; // Placeholder
d_v[idx] = d_v_hat[idx]; // Placeholder
// Synchronize threads within the grid
grid.sync();
}
int main() {
// Allocate memory for input and output arrays
hipComplex *d_u_hat, *d_v_hat, *d_u, *d_v;
hipMalloc((void**)&d_u_hat, N * sizeof(hipComplex));
hipMalloc((void**)&d_v_hat, N * sizeof(hipComplex));
hipMalloc((void**)&d_u, N * sizeof(hipComplex));
hipMalloc((void**)&d_v, N * sizeof(hipComplex));
// Initialize input arrays (dummy values)
hipComplex *h_u_hat = new hipComplex[N];
hipComplex *h_v_hat = new hipComplex[N];
for (int i = 0; i < N; ++i) {
h_u_hat[i] = make_hipComplex(1.0f, 0.0f);
h_v_hat[i] = make_hipComplex(1.0f, 0.0f);
}
hipMemcpy(d_u_hat, h_u_hat, N * sizeof(hipComplex), hipMemcpyHostToDevice);
hipMemcpy(d_v_hat, h_v_hat, N * sizeof(hipComplex), hipMemcpyHostToDevice);
// Set simulation parameters
float dt = 0.01f;
float nu = 0.001f;
// Launch kernel
int numBlocks = (N + BLOCK_SIZE - 1) / BLOCK_SIZE;
spectralFluidKernel<<<numBlocks, BLOCK_SIZE>>>(d_u_hat, d_v_hat, d_u, d_v, dt, nu);
// Check for errors in kernel launch
hipError_t err = hipGetLastError();
if (err != hipSuccess) {
std::cerr << "CUDA error: " << hipGetErrorString(err) << std::endl;
return -1;
}
// Copy results back to host
hipComplex *h_u = new hipComplex[N];
hipComplex *h_v = new hipComplex[N];
hipMemcpy(h_u, d_u, N * sizeof(hipComplex), hipMemcpyDeviceToHost);
hipMemcpy(h_v, d_v, N * sizeof(hipComplex), hipMemcpyDeviceToHost);
// Output first few results for verification
for (int i = 0; i < 10; ++i) {
std::cout << "u[" << i << "] = (" << h_u[i].x << ", " << h_u[i].y << ")\n";
std::cout << "v[" << i << "] = (" << h_v[i].x << ", " << h_v[i].y << ")\n";
}
// Free memory
delete[] h_u_hat;
delete[] h_v_hat;
delete[] h_u;
delete[] h_v;
hipFree(d_u_hat);
hipFree(d_v_hat);
hipFree(d_u);
hipFree(d_v);
return 0;
}
|
Convert the following CUDA code to AMD GPU code:
cuda
#include <iostream>
#include <cuda_runtime.h>
#include <thrust/device_vector.h>
#include <thrust/sort.h>
#include <thrust/copy.h>
#include <thrust/fill.h>
#include <thrust/sequence.h>
// Kernel for bipartite matching-based sorting
__global__ void bipartite_matching_sort_kernel(int *d_array, int *d_temp, int n) {
extern __shared__ int s_data[];
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < n) {
s_data[threadIdx.x] = d_array[idx];
} else {
s_data[threadIdx.x] = INT_MAX; // Ensure out-of-bounds elements are ignored
}
__syncthreads();
// Simple bitonic sort within shared memory
for (int k = 2; k <= blockDim.x; k *= 2) {
for (int j = k / 2; j > 0; j /= 2) {
int ixj = threadIdx.x ^ j;
if (ixj > threadIdx.x) {
if ((threadIdx.x & k) == 0) {
if (s_data[threadIdx.x] > s_data[ixj]) {
int tmp = s_data[threadIdx.x];
s_data[threadIdx.x] = s_data[ixj];
s_data[ixj] = tmp;
}
} else {
if (s_data[threadIdx.x] < s_data[ixj]) {
int tmp = s_data[threadIdx.x];
s_data[threadIdx.x] = s_data[ixj];
s_data[ixj] = tmp;
}
}
}
__syncthreads();
}
}
if (idx < n) {
d_temp[idx] = s_data[threadIdx.x];
}
}
int main() {
const int n = 128;
int h_array[n];
thrust::sequence(h_array, h_array + n, 0, -1); // Fill with reverse order
int *d_array, *d_temp;
cudaError_t err;
// Allocate device memory
err = cudaMalloc((void **)&d_array, n * sizeof(int));
if (err != cudaSuccess) {
std::cerr << "Failed to allocate device memory for d_array: " << cudaGetErrorString(err) << std::endl;
return -1;
}
err = cudaMalloc((void **)&d_temp, n * sizeof(int));
if (err != cudaSuccess) {
std::cerr << "Failed to allocate device memory for d_temp: " << cudaGetErrorString(err) << std::endl;
cudaFree(d_array);
return -1;
}
// Copy data from host to device
err = cudaMemcpy(d_array, h_array, n * sizeof(int), cudaMemcpyHostToDevice);
if (err != cudaSuccess) {
std::cerr << "Failed to copy data from host to device: " << cudaGetErrorString(err) << std::endl;
cudaFree(d_array);
cudaFree(d_temp);
return -1;
}
// Define block size and grid size
const int blockSize = 128;
const int gridSize = (n + blockSize - 1) / blockSize;
// Launch kernel
bipartite_matching_sort_kernel<<<gridSize, blockSize, blockSize * sizeof(int)>>>(d_array, d_temp, n);
err = cudaGetLastError();
if (err != cudaSuccess) {
std::cerr << "Kernel launch failed: " << cudaGetErrorString(err) << std::endl;
cudaFree(d_array);
cudaFree(d_temp);
return -1;
}
// Copy sorted data back to host
err = cudaMemcpy(h_array, d_temp, n * sizeof(int), cudaMemcpyDeviceToHost);
if (err != cudaSuccess) {
std::cerr << "Failed to copy data from device to host: " << cudaGetErrorString(err) << std::endl;
cudaFree(d_array);
cudaFree(d_temp);
return -1;
}
// Verify the result
for (int i = 0; i < n; ++i) {
if (h_array[i] != i) {
std::cerr << "Sorting failed at index " << i << ": expected " << i << ", got " << h_array[i] << std::endl;
cudaFree(d_array);
cudaFree(d_temp);
return -1;
}
}
std::cout << "Sorting successful!" << std::endl;
// Free device memory
cudaFree(d_array);
cudaFree(d_temp);
return 0;
}
|
#include <iostream>
#include <hip/hip_runtime.h>
#include <thrust/device_vector.h>
#include <thrust/sort.h>
#include <thrust/copy.h>
#include <thrust/fill.h>
#include <thrust/sequence.h>
// Kernel for bipartite matching-based sorting
__global__ void bipartite_matching_sort_kernel(int *d_array, int *d_temp, int n) {
extern __shared__ int s_data[];
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < n) {
s_data[threadIdx.x] = d_array[idx];
} else {
s_data[threadIdx.x] = INT_MAX; // Ensure out-of-bounds elements are ignored
}
__syncthreads();
// Simple bitonic sort within shared memory
for (int k = 2; k <= blockDim.x; k *= 2) {
for (int j = k / 2; j > 0; j /= 2) {
int ixj = threadIdx.x ^ j;
if (ixj > threadIdx.x) {
if ((threadIdx.x & k) == 0) {
if (s_data[threadIdx.x] > s_data[ixj]) {
int tmp = s_data[threadIdx.x];
s_data[threadIdx.x] = s_data[ixj];
s_data[ixj] = tmp;
}
} else {
if (s_data[threadIdx.x] < s_data[ixj]) {
int tmp = s_data[threadIdx.x];
s_data[threadIdx.x] = s_data[ixj];
s_data[ixj] = tmp;
}
}
}
__syncthreads();
}
}
if (idx < n) {
d_temp[idx] = s_data[threadIdx.x];
}
}
int main() {
const int n = 128;
int h_array[n];
thrust::sequence(h_array, h_array + n, 0, -1); // Fill with reverse order
int *d_array, *d_temp;
hipError_t err;
// Allocate device memory
err = hipMalloc((void **)&d_array, n * sizeof(int));
if (err != hipSuccess) {
std::cerr << "Failed to allocate device memory for d_array: " << hipGetErrorString(err) << std::endl;
return -1;
}
err = hipMalloc((void **)&d_temp, n * sizeof(int));
if (err != hipSuccess) {
std::cerr << "Failed to allocate device memory for d_temp: " << hipGetErrorString(err) << std::endl;
hipFree(d_array);
return -1;
}
// Copy data from host to device
err = hipMemcpy(d_array, h_array, n * sizeof(int), hipMemcpyHostToDevice);
if (err != hipSuccess) {
std::cerr << "Failed to copy data from host to device: " << hipGetErrorString(err) << std::endl;
hipFree(d_array);
hipFree(d_temp);
return -1;
}
// Define block size and grid size
const int blockSize = 128;
const int gridSize = (n + blockSize - 1) / blockSize;
// Launch kernel
bipartite_matching_sort_kernel<<<gridSize, blockSize, blockSize * sizeof(int)>>>(d_array, d_temp, n);
err = hipGetLastError();
if (err != hipSuccess) {
std::cerr << "Kernel launch failed: " << hipGetErrorString(err) << std::endl;
hipFree(d_array);
hipFree(d_temp);
return -1;
}
// Copy sorted data back to host
err = hipMemcpy(h_array, d_temp, n * sizeof(int), hipMemcpyDeviceToHost);
if (err != hipSuccess) {
std::cerr << "Failed to copy data from device to host: " << hipGetErrorString(err) << std::endl;
hipFree(d_array);
hipFree(d_temp);
return -1;
}
// Verify the result
for (int i = 0; i < n; ++i) {
if (h_array[i] != i) {
std::cerr << "Sorting failed at index " << i << ": expected " << i << ", got " << h_array[i] << std::endl;
hipFree(d_array);
hipFree(d_temp);
return -1;
}
}
std::cout << "Sorting successful!" << std::endl;
// Free device memory
hipFree(d_array);
hipFree(d_temp);
return 0;
}
|
Convert the following CUDA code to AMD GPU code:
cuda
#include <iostream>
#include <cuda_runtime.h>
#include <cuda.h>
// Define the image width and height
#define WIDTH 1024
#define HEIGHT 1024
// Define the block size
#define BLOCK_WIDTH 16
#define BLOCK_HEIGHT 16
// Kernel for image segmentation using threshold
__global__ void thresholdSegmentationKernel(unsigned char* inputImage, unsigned char* outputImage, int width, int height, unsigned char threshold) {
// Shared memory declaration
__shared__ unsigned char s_image[BLOCK_HEIGHT + 2][BLOCK_WIDTH + 2];
// Calculate the row and column index in the image
int row = blockIdx.y * blockDim.y + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
// Calculate the shared memory index
int s_row = threadIdx.y + 1;
int s_col = threadIdx.x + 1;
// Load data into shared memory
if (row < height && col < width) {
s_image[s_row][s_col] = inputImage[row * width + col];
} else {
s_image[s_row][s_col] = 0; // Out-of-bounds elements set to 0
}
// Synchronize to ensure all data is loaded into shared memory
__syncthreads();
// Perform thresholding
if (row < height && col < width) {
outputImage[row * width + col] = (s_image[s_row][s_col] > threshold) ? 255 : 0;
}
}
// Function to check for CUDA errors
void checkCudaError(cudaError_t error, const char* message) {
if (error != cudaSuccess) {
std::cerr << "CUDA Error: " << message << " (" << cudaGetErrorString(error) << ")" << std::endl;
exit(EXIT_FAILURE);
}
}
int main() {
// Allocate memory for the input and output images
unsigned char* h_inputImage = new unsigned char[WIDTH * HEIGHT];
unsigned char* h_outputImage = new unsigned char[WIDTH * HEIGHT];
// Initialize the input image with random values
for (int i = 0; i < WIDTH * HEIGHT; ++i) {
h_inputImage[i] = static_cast<unsigned char>(rand() % 256);
}
// Allocate memory on the device
unsigned char* d_inputImage;
unsigned char* d_outputImage;
checkCudaError(cudaMalloc((void**)&d_inputImage, WIDTH * HEIGHT * sizeof(unsigned char)), "cudaMalloc d_inputImage");
checkCudaError(cudaMalloc((void**)&d_outputImage, WIDTH * HEIGHT * sizeof(unsigned char)), "cudaMalloc d_outputImage");
// Copy data from host to device
checkCudaError(cudaMemcpy(d_inputImage, h_inputImage, WIDTH * HEIGHT * sizeof(unsigned char), cudaMemcpyHostToDevice), "cudaMemcpy h_inputImage to d_inputImage");
// Define the threshold
unsigned char threshold = 128;
// Set the grid and block dimensions
dim3 blockSize(BLOCK_WIDTH, BLOCK_HEIGHT);
dim3 gridSize((WIDTH + BLOCK_WIDTH - 1) / BLOCK_WIDTH, (HEIGHT + BLOCK_HEIGHT - 1) / BLOCK_HEIGHT);
// Launch the kernel
thresholdSegmentationKernel<<<gridSize, blockSize>>>(d_inputImage, d_outputImage, WIDTH, HEIGHT, threshold);
checkCudaError(cudaGetLastError(), "Kernel launch failed");
// Copy the result back to the host
checkCudaError(cudaMemcpy(h_outputImage, d_outputImage, WIDTH * HEIGHT * sizeof(unsigned char), cudaMemcpyDeviceToHost), "cudaMemcpy d_outputImage to h_outputImage");
// Free device memory
checkCudaError(cudaFree(d_inputImage), "cudaFree d_inputImage");
checkCudaError(cudaFree(d_outputImage), "cudaFree d_outputImage");
// Free host memory
delete[] h_inputImage;
delete[] h_outputImage;
std::cout << "Image segmentation completed successfully." << std::endl;
return 0;
}
|
#include <iostream>
#include <hip/hip_runtime.h>
// Define the image width and height
#define WIDTH 1024
#define HEIGHT 1024
// Define the block size
#define BLOCK_WIDTH 16
#define BLOCK_HEIGHT 16
// Kernel for image segmentation using threshold
__global__ void thresholdSegmentationKernel(unsigned char* inputImage, unsigned char* outputImage, int width, int height, unsigned char threshold) {
// Shared memory declaration
__shared__ unsigned char s_image[BLOCK_HEIGHT + 2][BLOCK_WIDTH + 2];
// Calculate the row and column index in the image
int row = blockIdx.y * blockDim.y + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
// Calculate the shared memory index
int s_row = threadIdx.y + 1;
int s_col = threadIdx.x + 1;
// Load data into shared memory
if (row < height && col < width) {
s_image[s_row][s_col] = inputImage[row * width + col];
} else {
s_image[s_row][s_col] = 0; // Out-of-bounds elements set to 0
}
// Synchronize to ensure all data is loaded into shared memory
__syncthreads();
// Perform thresholding
if (row < height && col < width) {
outputImage[row * width + col] = (s_image[s_row][s_col] > threshold) ? 255 : 0;
}
}
// Function to check for CUDA errors
void checkCudaError(hipError_t error, const char* message) {
if (error != hipSuccess) {
std::cerr << "CUDA Error: " << message << " (" << hipGetErrorString(error) << ")" << std::endl;
exit(EXIT_FAILURE);
}
}
int main() {
// Allocate memory for the input and output images
unsigned char* h_inputImage = new unsigned char[WIDTH * HEIGHT];
unsigned char* h_outputImage = new unsigned char[WIDTH * HEIGHT];
// Initialize the input image with random values
for (int i = 0; i < WIDTH * HEIGHT; ++i) {
h_inputImage[i] = static_cast<unsigned char>(rand() % 256);
}
// Allocate memory on the device
unsigned char* d_inputImage;
unsigned char* d_outputImage;
checkCudaError(hipMalloc((void**)&d_inputImage, WIDTH * HEIGHT * sizeof(unsigned char)), "hipMalloc d_inputImage");
checkCudaError(hipMalloc((void**)&d_outputImage, WIDTH * HEIGHT * sizeof(unsigned char)), "hipMalloc d_outputImage");
// Copy data from host to device
checkCudaError(hipMemcpy(d_inputImage, h_inputImage, WIDTH * HEIGHT * sizeof(unsigned char), hipMemcpyHostToDevice), "hipMemcpy h_inputImage to d_inputImage");
// Define the threshold
unsigned char threshold = 128;
// Set the grid and block dimensions
dim3 blockSize(BLOCK_WIDTH, BLOCK_HEIGHT);
dim3 gridSize((WIDTH + BLOCK_WIDTH - 1) / BLOCK_WIDTH, (HEIGHT + BLOCK_HEIGHT - 1) / BLOCK_HEIGHT);
// Launch the kernel
thresholdSegmentationKernel<<<gridSize, blockSize>>>(d_inputImage, d_outputImage, WIDTH, HEIGHT, threshold);
checkCudaError(hipGetLastError(), "Kernel launch failed");
// Copy the result back to the host
checkCudaError(hipMemcpy(h_outputImage, d_outputImage, WIDTH * HEIGHT * sizeof(unsigned char), hipMemcpyDeviceToHost), "hipMemcpy d_outputImage to h_outputImage");
// Free device memory
checkCudaError(hipFree(d_inputImage), "hipFree d_inputImage");
checkCudaError(hipFree(d_outputImage), "hipFree d_outputImage");
// Free host memory
delete[] h_inputImage;
delete[] h_outputImage;
std::cout << "Image segmentation completed successfully." << std::endl;
return 0;
}
|
Convert the following CUDA code to AMD GPU code:
cuda
#include <iostream>
#include <cuda_runtime.h>
#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <thrust/sort.h>
#include <thrust/copy.h>
#include <thrust/adjacent_difference.h>
#include <thrust/scan.h>
#include <thrust/sequence.h>
#include <thrust/fill.h>
#include <thrust/transform.h>
#include <thrust/remove.h>
#include <thrust/reduce.h>
#include <thrust/execution_policy.h>
#include <curand_kernel.h>
#define BLOCK_SIZE 256
// Kernel to calculate betweenness centrality of edges
__global__ void calculateBetweenness(
int *edge_betweenness,
int *edge_weights,
int *node_degrees,
int *node_neighbors,
int *node_neighbor_indices,
int num_nodes,
int num_edges,
int *sorted_nodes,
int *sorted_edges,
int *sorted_edge_indices,
int *edge_credits,
int *edge_credits_accum,
int *node_credits,
int *node_credits_accum,
int *node_paths,
int *node_paths_accum,
int *node_predecessors,
int *node_predecessor_counts,
int *node_predecessor_indices,
int *node_predecessor_weights,
int *node_predecessor_weights_accum) {
extern __shared__ int shared_data[];
int *shared_node_credits = shared_data;
int *shared_node_paths = shared_data + num_nodes;
int *shared_node_predecessors = shared_data + 2 * num_nodes;
int *shared_node_predecessor_counts = shared_data + 3 * num_nodes;
int *shared_node_predecessor_indices = shared_data + 4 * num_nodes;
int *shared_node_predecessor_weights = shared_data + 5 * num_nodes;
int node = blockIdx.x * blockDim.x + threadIdx.x;
if (node >= num_nodes) return;
// Initialize shared memory
shared_node_credits[node] = 0;
shared_node_paths[node] = 0;
shared_node_predecessor_counts[node] = 0;
__syncthreads();
// Breadth-first search to find shortest paths
if (node == sorted_nodes[0]) {
shared_node_paths[node] = 1;
}
__syncthreads();
for (int i = 1; i < num_nodes; ++i) {
int current_node = sorted_nodes[i];
int start = node_neighbor_indices[current_node];
int end = node_neighbor_indices[current_node + 1];
for (int j = start; j < end; ++j) {
int neighbor = node_neighbors[j];
if (shared_node_paths[neighbor] == 0) {
atomicAdd(&shared_node_paths[neighbor], shared_node_paths[current_node]);
int pred_count = atomicAdd(&shared_node_predecessor_counts[neighbor], 1);
shared_node_predecessors[shared_node_predecessor_indices[neighbor] + pred_count] = current_node;
} else if (shared_node_paths[current_node] <= shared_node_paths[neighbor]) {
int pred_count = atomicAdd(&shared_node_predecessor_counts[neighbor], 1);
shared_node_predecessors[shared_node_predecessor_indices[neighbor] + pred_count] = current_node;
}
}
__syncthreads();
}
// Backpropagation to accumulate betweenness centrality
for (int i = num_nodes - 1; i >= 0; --i) {
int current_node = sorted_nodes[i];
int start = node_predecessor_indices[current_node];
int end = node_predecessor_indices[current_node + 1];
for (int j = start; j < end; ++j) {
int predecessor = node_predecessors[j];
int weight = node_predecessor_weights[j];
int delta = (shared_node_credits[current_node] + shared_node_paths[current_node]) * weight / shared_node_paths[predecessor];
atomicAdd(&shared_node_credits[predecessor], delta);
atomicAdd(&edge_betweenness[sorted_edge_indices[predecessor]], delta);
}
if (current_node != sorted_nodes[0]) {
atomicAdd(&shared_node_credits[current_node], shared_node_paths[current_node]);
}
__syncthreads();
}
}
int main() {
const int num_nodes = 8192;
const int num_edges = 32768; // Example number of edges
// Initialize random number generator
curandState *devStates;
cudaMalloc(&devStates, num_nodes * sizeof(curandState));
cudaMemset(devStates, 0, num_nodes * sizeof(curandState));
// Allocate and initialize host data
thrust::host_vector<int> h_node_degrees(num_nodes, 0);
thrust::host_vector<int> h_node_neighbors(num_edges, 0);
thrust::host_vector<int> h_node_neighbor_indices(num_nodes + 1, 0);
thrust::host_vector<int> h_edge_weights(num_edges, 1);
thrust::host_vector<int> h_edge_betweenness(num_edges, 0);
// Example graph initialization (random edges)
thrust::host_vector<int> h_sorted_nodes(num_nodes);
thrust::sequence(h_sorted_nodes.begin(), h_sorted_nodes.end());
thrust::host_vector<int> h_sorted_edges(num_edges);
thrust::sequence(h_sorted_edges.begin(), h_sorted_edges.end());
thrust::host_vector<int> h_sorted_edge_indices(num_nodes + 1, 0);
// Allocate device memory
int *d_node_degrees, *d_node_neighbors, *d_node_neighbor_indices, *d_edge_weights, *d_edge_betweenness;
cudaMalloc(&d_node_degrees, num_nodes * sizeof(int));
cudaMalloc(&d_node_neighbors, num_edges * sizeof(int));
cudaMalloc(&d_node_neighbor_indices, (num_nodes + 1) * sizeof(int));
cudaMalloc(&d_edge_weights, num_edges * sizeof(int));
cudaMalloc(&d_edge_betweenness, num_edges * sizeof(int));
// Copy data to device
cudaMemcpy(d_node_degrees, h_node_degrees.data(), num_nodes * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_node_neighbors, h_node_neighbors.data(), num_edges * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_node_neighbor_indices, h_node_neighbor_indices.data(), (num_nodes + 1) * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_edge_weights, h_edge_weights.data(), num_edges * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_edge_betweenness, h_edge_betweenness.data(), num_edges * sizeof(int), cudaMemcpyHostToDevice);
// Allocate shared memory for each block
size_t shared_mem_size = 6 * num_nodes * sizeof(int);
// Launch kernel
int blocks = (num_nodes + BLOCK_SIZE - 1) / BLOCK_SIZE;
calculateBetweenness<<<blocks, BLOCK_SIZE, shared_mem_size>>>(
d_edge_betweenness,
d_edge_weights,
d_node_degrees,
d_node_neighbors,
d_node_neighbor_indices,
num_nodes,
num_edges,
thrust::raw_pointer_cast(h_sorted_nodes.data()),
thrust::raw_pointer_cast(h_sorted_edges.data()),
thrust::raw_pointer_cast(h_sorted_edge_indices.data()),
nullptr,
nullptr,
nullptr,
nullptr,
nullptr,
nullptr,
nullptr,
nullptr,
nullptr,
nullptr,
nullptr);
// Check for errors
cudaError_t err = cudaGetLastError();
if (err != cudaSuccess) {
std::cerr << "CUDA error: " << cudaGetErrorString(err) << std::endl;
return -1;
}
// Copy results back to host
cudaMemcpy(h_edge_betweenness.data(), d_edge_betweenness, num_edges * sizeof(int), cudaMemcpyDeviceToHost);
// Free device memory
cudaFree(d_node_degrees);
cudaFree(d_node_neighbors);
cudaFree(d_node_neighbor_indices);
cudaFree(d_edge_weights);
cudaFree(d_edge_betweenness);
cudaFree(devStates);
// Output results (for demonstration purposes)
std::cout << "Edge betweenness centralities:" << std::endl;
for (int i = 0; i < 10; ++i) { // Print first 10 for brevity
std::cout << "Edge " << i << ": " << h_edge_betweenness[i] << std::endl;
}
return 0;
}
|
#include <iostream>
#include <hip/hip_runtime.h>
#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <thrust/sort.h>
#include <thrust/copy.h>
#include <thrust/adjacent_difference.h>
#include <thrust/scan.h>
#include <thrust/sequence.h>
#include <thrust/fill.h>
#include <thrust/transform.h>
#include <thrust/remove.h>
#include <thrust/reduce.h>
#include <thrust/execution_policy.h>
#include <hiprand/hiprand_kernel.h>
#define BLOCK_SIZE 256
// Kernel to calculate betweenness centrality of edges
__global__ void calculateBetweenness(
int *edge_betweenness,
int *edge_weights,
int *node_degrees,
int *node_neighbors,
int *node_neighbor_indices,
int num_nodes,
int num_edges,
int *sorted_nodes,
int *sorted_edges,
int *sorted_edge_indices,
int *edge_credits,
int *edge_credits_accum,
int *node_credits,
int *node_credits_accum,
int *node_paths,
int *node_paths_accum,
int *node_predecessors,
int *node_predecessor_counts,
int *node_predecessor_indices,
int *node_predecessor_weights,
int *node_predecessor_weights_accum) {
extern __shared__ int shared_data[];
int *shared_node_credits = shared_data;
int *shared_node_paths = shared_data + num_nodes;
int *shared_node_predecessors = shared_data + 2 * num_nodes;
int *shared_node_predecessor_counts = shared_data + 3 * num_nodes;
int *shared_node_predecessor_indices = shared_data + 4 * num_nodes;
int *shared_node_predecessor_weights = shared_data + 5 * num_nodes;
int node = blockIdx.x * blockDim.x + threadIdx.x;
if (node >= num_nodes) return;
// Initialize shared memory
shared_node_credits[node] = 0;
shared_node_paths[node] = 0;
shared_node_predecessor_counts[node] = 0;
__syncthreads();
// Breadth-first search to find shortest paths
if (node == sorted_nodes[0]) {
shared_node_paths[node] = 1;
}
__syncthreads();
for (int i = 1; i < num_nodes; ++i) {
int current_node = sorted_nodes[i];
int start = node_neighbor_indices[current_node];
int end = node_neighbor_indices[current_node + 1];
for (int j = start; j < end; ++j) {
int neighbor = node_neighbors[j];
if (shared_node_paths[neighbor] == 0) {
atomicAdd(&shared_node_paths[neighbor], shared_node_paths[current_node]);
int pred_count = atomicAdd(&shared_node_predecessor_counts[neighbor], 1);
shared_node_predecessors[shared_node_predecessor_indices[neighbor] + pred_count] = current_node;
} else if (shared_node_paths[current_node] <= shared_node_paths[neighbor]) {
int pred_count = atomicAdd(&shared_node_predecessor_counts[neighbor], 1);
shared_node_predecessors[shared_node_predecessor_indices[neighbor] + pred_count] = current_node;
}
}
__syncthreads();
}
// Backpropagation to accumulate betweenness centrality
for (int i = num_nodes - 1; i >= 0; --i) {
int current_node = sorted_nodes[i];
int start = node_predecessor_indices[current_node];
int end = node_predecessor_indices[current_node + 1];
for (int j = start; j < end; ++j) {
int predecessor = node_predecessors[j];
int weight = node_predecessor_weights[j];
int delta = (shared_node_credits[current_node] + shared_node_paths[current_node]) * weight / shared_node_paths[predecessor];
atomicAdd(&shared_node_credits[predecessor], delta);
atomicAdd(&edge_betweenness[sorted_edge_indices[predecessor]], delta);
}
if (current_node != sorted_nodes[0]) {
atomicAdd(&shared_node_credits[current_node], shared_node_paths[current_node]);
}
__syncthreads();
}
}
int main() {
const int num_nodes = 8192;
const int num_edges = 32768; // Example number of edges
// Initialize random number generator
hiprandState *devStates;
hipMalloc(&devStates, num_nodes * sizeof(hiprandState));
hipMemset(devStates, 0, num_nodes * sizeof(hiprandState));
// Allocate and initialize host data
thrust::host_vector<int> h_node_degrees(num_nodes, 0);
thrust::host_vector<int> h_node_neighbors(num_edges, 0);
thrust::host_vector<int> h_node_neighbor_indices(num_nodes + 1, 0);
thrust::host_vector<int> h_edge_weights(num_edges, 1);
thrust::host_vector<int> h_edge_betweenness(num_edges, 0);
// Example graph initialization (random edges)
thrust::host_vector<int> h_sorted_nodes(num_nodes);
thrust::sequence(h_sorted_nodes.begin(), h_sorted_nodes.end());
thrust::host_vector<int> h_sorted_edges(num_edges);
thrust::sequence(h_sorted_edges.begin(), h_sorted_edges.end());
thrust::host_vector<int> h_sorted_edge_indices(num_nodes + 1, 0);
// Allocate device memory
int *d_node_degrees, *d_node_neighbors, *d_node_neighbor_indices, *d_edge_weights, *d_edge_betweenness;
hipMalloc(&d_node_degrees, num_nodes * sizeof(int));
hipMalloc(&d_node_neighbors, num_edges * sizeof(int));
hipMalloc(&d_node_neighbor_indices, (num_nodes + 1) * sizeof(int));
hipMalloc(&d_edge_weights, num_edges * sizeof(int));
hipMalloc(&d_edge_betweenness, num_edges * sizeof(int));
// Copy data to device
hipMemcpy(d_node_degrees, h_node_degrees.data(), num_nodes * sizeof(int), hipMemcpyHostToDevice);
hipMemcpy(d_node_neighbors, h_node_neighbors.data(), num_edges * sizeof(int), hipMemcpyHostToDevice);
hipMemcpy(d_node_neighbor_indices, h_node_neighbor_indices.data(), (num_nodes + 1) * sizeof(int), hipMemcpyHostToDevice);
hipMemcpy(d_edge_weights, h_edge_weights.data(), num_edges * sizeof(int), hipMemcpyHostToDevice);
hipMemcpy(d_edge_betweenness, h_edge_betweenness.data(), num_edges * sizeof(int), hipMemcpyHostToDevice);
// Allocate shared memory for each block
size_t shared_mem_size = 6 * num_nodes * sizeof(int);
// Launch kernel
int blocks = (num_nodes + BLOCK_SIZE - 1) / BLOCK_SIZE;
calculateBetweenness<<<blocks, BLOCK_SIZE, shared_mem_size>>>(
d_edge_betweenness,
d_edge_weights,
d_node_degrees,
d_node_neighbors,
d_node_neighbor_indices,
num_nodes,
num_edges,
thrust::raw_pointer_cast(h_sorted_nodes.data()),
thrust::raw_pointer_cast(h_sorted_edges.data()),
thrust::raw_pointer_cast(h_sorted_edge_indices.data()),
nullptr,
nullptr,
nullptr,
nullptr,
nullptr,
nullptr,
nullptr,
nullptr,
nullptr,
nullptr,
nullptr);
// Check for errors
hipError_t err = hipGetLastError();
if (err != hipSuccess) {
std::cerr << "CUDA error: " << hipGetErrorString(err) << std::endl;
return -1;
}
// Copy results back to host
hipMemcpy(h_edge_betweenness.data(), d_edge_betweenness, num_edges * sizeof(int), hipMemcpyDeviceToHost);
// Free device memory
hipFree(d_node_degrees);
hipFree(d_node_neighbors);
hipFree(d_node_neighbor_indices);
hipFree(d_edge_weights);
hipFree(d_edge_betweenness);
hipFree(devStates);
// Output results (for demonstration purposes)
std::cout << "Edge betweenness centralities:" << std::endl;
for (int i = 0; i < 10; ++i) { // Print first 10 for brevity
std::cout << "Edge " << i << ": " << h_edge_betweenness[i] << std::endl;
}
return 0;
}
|
Convert the following CUDA code to AMD GPU code:
cuda
#include <iostream>
#include <cuda_runtime.h>
// Define a simple structure to represent a sparse tensor element
struct SparseTensorElement {
int row;
int col;
float value;
};
// CUDA kernel to perform operations on a sparse tensor
__global__ void sparseTensorKernel(SparseTensorElement* elements, float* result, int numElements) {
// Calculate the global thread index
int idx = blockIdx.x * blockDim.x + threadIdx.x;
// Warp-level optimization: ensure all threads in a warp process elements
if (idx < numElements) {
// Each thread processes one element
int row = elements[idx].row;
int col = elements[idx].col;
float value = elements[idx].value;
// Perform a simple operation: accumulate the value in the result array at position row*col
// This is just a placeholder operation for demonstration
atomicAdd(&result[row * col], value);
}
}
int main() {
// Define the number of non-zero elements
const int numElements = 1024;
// Allocate host memory for sparse tensor elements and result
SparseTensorElement* h_elements = new SparseTensorElement[numElements];
float* h_result = new float[numElements * numElements]; // Assuming a square matrix for simplicity
// Initialize the sparse tensor elements with dummy data
for (int i = 0; i < numElements; ++i) {
h_elements[i].row = i % 32; // Arbitrary row index
h_elements[i].col = i % 32; // Arbitrary column index
h_elements[i].value = 1.0f; // Arbitrary value
}
// Initialize the result array to zero
for (int i = 0; i < numElements * numElements; ++i) {
h_result[i] = 0.0f;
}
// Allocate device memory
SparseTensorElement* d_elements;
float* d_result;
cudaError_t err = cudaMalloc((void**)&d_elements, numElements * sizeof(SparseTensorElement));
if (err != cudaSuccess) {
std::cerr << "Failed to allocate device memory for elements: " << cudaGetErrorString(err) << std::endl;
return -1;
}
err = cudaMalloc((void**)&d_result, numElements * numElements * sizeof(float));
if (err != cudaSuccess) {
std::cerr << "Failed to allocate device memory for result: " << cudaGetErrorString(err) << std::endl;
cudaFree(d_elements);
return -1;
}
// Copy data from host to device
err = cudaMemcpy(d_elements, h_elements, numElements * sizeof(SparseTensorElement), cudaMemcpyHostToDevice);
if (err != cudaSuccess) {
std::cerr << "Failed to copy elements to device: " << cudaGetErrorString(err) << std::endl;
cudaFree(d_elements);
cudaFree(d_result);
return -1;
}
err = cudaMemcpy(d_result, h_result, numElements * numElements * sizeof(float), cudaMemcpyHostToDevice);
if (err != cudaSuccess) {
std::cerr << "Failed to copy result to device: " << cudaGetErrorString(err) << std::endl;
cudaFree(d_elements);
cudaFree(d_result);
return -1;
}
// Define the grid and block dimensions
dim3 blockSize(256);
dim3 gridSize((numElements + blockSize.x - 1) / blockSize.x);
// Launch the kernel
sparseTensorKernel<<<gridSize, blockSize>>>(d_elements, d_result, numElements);
// Check for kernel launch errors
err = cudaGetLastError();
if (err != cudaSuccess) {
std::cerr << "Kernel launch failed: " << cudaGetErrorString(err) << std::endl;
cudaFree(d_elements);
cudaFree(d_result);
return -1;
}
// Copy the result back to the host
err = cudaMemcpy(h_result, d_result, numElements * numElements * sizeof(float), cudaMemcpyDeviceToHost);
if (err != cudaSuccess) {
std::cerr << "Failed to copy result back to host: " << cudaGetErrorString(err) << std::endl;
cudaFree(d_elements);
cudaFree(d_result);
return -1;
}
// Free device memory
cudaFree(d_elements);
cudaFree(d_result);
// Clean up host memory
delete[] h_elements;
delete[] h_result;
std::cout << "Sparse tensor operation completed successfully." << std::endl;
return 0;
}
|
#include <iostream>
#include <hip/hip_runtime.h>
// Define a simple structure to represent a sparse tensor element
struct SparseTensorElement {
int row;
int col;
float value;
};
// CUDA kernel to perform operations on a sparse tensor
__global__ void sparseTensorKernel(SparseTensorElement* elements, float* result, int numElements) {
// Calculate the global thread index
int idx = blockIdx.x * blockDim.x + threadIdx.x;
// Warp-level optimization: ensure all threads in a warp process elements
if (idx < numElements) {
// Each thread processes one element
int row = elements[idx].row;
int col = elements[idx].col;
float value = elements[idx].value;
// Perform a simple operation: accumulate the value in the result array at position row*col
// This is just a placeholder operation for demonstration
atomicAdd(&result[row * col], value);
}
}
int main() {
// Define the number of non-zero elements
const int numElements = 1024;
// Allocate host memory for sparse tensor elements and result
SparseTensorElement* h_elements = new SparseTensorElement[numElements];
float* h_result = new float[numElements * numElements]; // Assuming a square matrix for simplicity
// Initialize the sparse tensor elements with dummy data
for (int i = 0; i < numElements; ++i) {
h_elements[i].row = i % 32; // Arbitrary row index
h_elements[i].col = i % 32; // Arbitrary column index
h_elements[i].value = 1.0f; // Arbitrary value
}
// Initialize the result array to zero
for (int i = 0; i < numElements * numElements; ++i) {
h_result[i] = 0.0f;
}
// Allocate device memory
SparseTensorElement* d_elements;
float* d_result;
hipError_t err = hipMalloc((void**)&d_elements, numElements * sizeof(SparseTensorElement));
if (err != hipSuccess) {
std::cerr << "Failed to allocate device memory for elements: " << hipGetErrorString(err) << std::endl;
return -1;
}
err = hipMalloc((void**)&d_result, numElements * numElements * sizeof(float));
if (err != hipSuccess) {
std::cerr << "Failed to allocate device memory for result: " << hipGetErrorString(err) << std::endl;
hipFree(d_elements);
return -1;
}
// Copy data from host to device
err = hipMemcpy(d_elements, h_elements, numElements * sizeof(SparseTensorElement), hipMemcpyHostToDevice);
if (err != hipSuccess) {
std::cerr << "Failed to copy elements to device: " << hipGetErrorString(err) << std::endl;
hipFree(d_elements);
hipFree(d_result);
return -1;
}
err = hipMemcpy(d_result, h_result, numElements * numElements * sizeof(float), hipMemcpyHostToDevice);
if (err != hipSuccess) {
std::cerr << "Failed to copy result to device: " << hipGetErrorString(err) << std::endl;
hipFree(d_elements);
hipFree(d_result);
return -1;
}
// Define the grid and block dimensions
dim3 blockSize(256);
dim3 gridSize((numElements + blockSize.x - 1) / blockSize.x);
// Launch the kernel
sparseTensorKernel<<<gridSize, blockSize>>>(d_elements, d_result, numElements);
// Check for kernel launch errors
err = hipGetLastError();
if (err != hipSuccess) {
std::cerr << "Kernel launch failed: " << hipGetErrorString(err) << std::endl;
hipFree(d_elements);
hipFree(d_result);
return -1;
}
// Copy the result back to the host
err = hipMemcpy(h_result, d_result, numElements * numElements * sizeof(float), hipMemcpyDeviceToHost);
if (err != hipSuccess) {
std::cerr << "Failed to copy result back to host: " << hipGetErrorString(err) << std::endl;
hipFree(d_elements);
hipFree(d_result);
return -1;
}
// Free device memory
hipFree(d_elements);
hipFree(d_result);
// Clean up host memory
delete[] h_elements;
delete[] h_result;
std::cout << "Sparse tensor operation completed successfully." << std::endl;
return 0;
}
|
Convert the following CUDA code to AMD GPU code:
cuda
// monte_carlo_option_pricing.cu
#include <iostream>
#include <cuda_runtime.h>
#include <curand_kernel.h>
// Constants for the simulation
#define NUM_PATHS 64
#define NUM_STEPS 100
#define BLOCK_SIZE 32
// Kernel to perform Monte Carlo simulation
__global__ void monteCarloSimulation(float *d_results, float S0, float r, float sigma, float T) {
// Initialize random number generator per thread
curandState localState;
unsigned int seed = clock64();
curand_init(seed, threadIdx.x, 0, &localState);
// Each thread simulates one path
float S = S0;
float dt = T / NUM_STEPS;
// Simulate the path
for (int step = 0; step < NUM_STEPS; ++step) {
float epsilon = curand_normal(&localState);
S *= exp((r - 0.5f * sigma * sigma) * dt + sigma * sqrt(dt) * epsilon);
}
// Calculate payoff for a call option
float payoff = fmaxf(0.0f, S - 100.0f); // Assuming strike price is 100
// Store the result
d_results[threadIdx.x] = payoff;
}
int main() {
// Parameters for the option pricing
float S0 = 100.0f; // Initial stock price
float r = 0.05f; // Risk-free rate
float sigma = 0.2f; // Volatility
float T = 1.0f; // Time to maturity
// Allocate device memory for results
float *d_results;
cudaError_t err = cudaMalloc((void**)&d_results, NUM_PATHS * sizeof(float));
if (err != cudaSuccess) {
std::cerr << "Error allocating device memory: " << cudaGetErrorString(err) << std::endl;
return -1;
}
// Launch the kernel
monteCarloSimulation<<<1, NUM_PATHS>>>(d_results, S0, r, sigma, T);
err = cudaGetLastError();
if (err != cudaSuccess) {
std::cerr << "Error launching kernel: " << cudaGetErrorString(err) << std::endl;
cudaFree(d_results);
return -1;
}
// Copy results back to host
float h_results[NUM_PATHS];
err = cudaMemcpy(h_results, d_results, NUM_PATHS * sizeof(float), cudaMemcpyDeviceToHost);
if (err != cudaSuccess) {
std::cerr << "Error copying results back to host: " << cudaGetErrorString(err) << std::endl;
cudaFree(d_results);
return -1;
}
// Calculate the average payoff
float sum = 0.0f;
for (int i = 0; i < NUM_PATHS; ++i) {
sum += h_results[i];
}
float optionPrice = exp(-r * T) * (sum / NUM_PATHS);
// Output the result
std::cout << "Estimated option price: " << optionPrice << std::endl;
// Free device memory
cudaFree(d_results);
return 0;
}
|
// monte_carlo_option_pricing.cu
#include <iostream>
#include <hip/hip_runtime.h>
#include <hiprand/hiprand_kernel.h>
// Constants for the simulation
#define NUM_PATHS 64
#define NUM_STEPS 100
#define BLOCK_SIZE 32
// Kernel to perform Monte Carlo simulation
__global__ void monteCarloSimulation(float *d_results, float S0, float r, float sigma, float T) {
// Initialize random number generator per thread
hiprandState localState;
unsigned int seed = clock64();
hiprand_init(seed, threadIdx.x, 0, &localState);
// Each thread simulates one path
float S = S0;
float dt = T / NUM_STEPS;
// Simulate the path
for (int step = 0; step < NUM_STEPS; ++step) {
float epsilon = hiprand_normal(&localState);
S *= exp((r - 0.5f * sigma * sigma) * dt + sigma * sqrt(dt) * epsilon);
}
// Calculate payoff for a call option
float payoff = fmaxf(0.0f, S - 100.0f); // Assuming strike price is 100
// Store the result
d_results[threadIdx.x] = payoff;
}
int main() {
// Parameters for the option pricing
float S0 = 100.0f; // Initial stock price
float r = 0.05f; // Risk-free rate
float sigma = 0.2f; // Volatility
float T = 1.0f; // Time to maturity
// Allocate device memory for results
float *d_results;
hipError_t err = hipMalloc((void**)&d_results, NUM_PATHS * sizeof(float));
if (err != hipSuccess) {
std::cerr << "Error allocating device memory: " << hipGetErrorString(err) << std::endl;
return -1;
}
// Launch the kernel
monteCarloSimulation<<<1, NUM_PATHS>>>(d_results, S0, r, sigma, T);
err = hipGetLastError();
if (err != hipSuccess) {
std::cerr << "Error launching kernel: " << hipGetErrorString(err) << std::endl;
hipFree(d_results);
return -1;
}
// Copy results back to host
float h_results[NUM_PATHS];
err = hipMemcpy(h_results, d_results, NUM_PATHS * sizeof(float), hipMemcpyDeviceToHost);
if (err != hipSuccess) {
std::cerr << "Error copying results back to host: " << hipGetErrorString(err) << std::endl;
hipFree(d_results);
return -1;
}
// Calculate the average payoff
float sum = 0.0f;
for (int i = 0; i < NUM_PATHS; ++i) {
sum += h_results[i];
}
float optionPrice = exp(-r * T) * (sum / NUM_PATHS);
// Output the result
std::cout << "Estimated option price: " << optionPrice << std::endl;
// Free device memory
hipFree(d_results);
return 0;
}
|
Convert the following CUDA code to AMD GPU code:
cuda
#include <iostream>
#include <cuda_runtime.h>
// Define constants
#define POPULATION_SIZE 1024
#define GENES_PER_INDIVIDUAL 32
#define NUM_THREADS 256
#define NUM_BLOCKS (POPULATION_SIZE / NUM_THREADS)
// CUDA kernel for genetic algorithm
__global__ void geneticAlgorithmKernel(float* population, float* fitness) {
// Calculate global thread index
int idx = blockIdx.x * blockDim.x + threadIdx.x;
// Ensure the thread index is within the population size
if (idx >= POPULATION_SIZE) return;
// Example operation: calculate fitness (sum of genes)
float sum = 0.0f;
for (int i = 0; i < GENES_PER_INDIVIDUAL; ++i) {
sum += population[idx * GENES_PER_INDIVIDUAL + i];
}
// Store the fitness value
fitness[idx] = sum;
}
int main() {
// Allocate memory for population and fitness on host
float* h_population = new float[POPULATION_SIZE * GENES_PER_INDIVIDUAL];
float* h_fitness = new float[POPULATION_SIZE];
// Initialize population with random values
for (int i = 0; i < POPULATION_SIZE * GENES_PER_INDIVIDUAL; ++i) {
h_population[i] = static_cast<float>(rand()) / RAND_MAX;
}
// Allocate memory for population and fitness on device
float* d_population;
float* d_fitness;
cudaError_t err;
err = cudaMalloc((void**)&d_population, POPULATION_SIZE * GENES_PER_INDIVIDUAL * sizeof(float));
if (err != cudaSuccess) {
std::cerr << "Error allocating device memory for population: " << cudaGetErrorString(err) << std::endl;
return -1;
}
err = cudaMalloc((void**)&d_fitness, POPULATION_SIZE * sizeof(float));
if (err != cudaSuccess) {
std::cerr << "Error allocating device memory for fitness: " << cudaGetErrorString(err) << std::endl;
cudaFree(d_population);
return -1;
}
// Copy population from host to device
err = cudaMemcpy(d_population, h_population, POPULATION_SIZE * GENES_PER_INDIVIDUAL * sizeof(float), cudaMemcpyHostToDevice);
if (err != cudaSuccess) {
std::cerr << "Error copying population to device: " << cudaGetErrorString(err) << std::endl;
cudaFree(d_population);
cudaFree(d_fitness);
return -1;
}
// Launch the genetic algorithm kernel
geneticAlgorithmKernel<<<NUM_BLOCKS, NUM_THREADS>>>(d_population, d_fitness);
// Check for errors in kernel launch
err = cudaGetLastError();
if (err != cudaSuccess) {
std::cerr << "Error launching kernel: " << cudaGetErrorString(err) << std::endl;
cudaFree(d_population);
cudaFree(d_fitness);
return -1;
}
// Copy fitness from device to host
err = cudaMemcpy(h_fitness, d_fitness, POPULATION_SIZE * sizeof(float), cudaMemcpyDeviceToHost);
if (err != cudaSuccess) {
std::cerr << "Error copying fitness to host: " << cudaGetErrorString(err) << std::endl;
cudaFree(d_population);
cudaFree(d_fitness);
return -1;
}
// Free device memory
cudaFree(d_population);
cudaFree(d_fitness);
// Output the fitness of the first individual as a demonstration
std::cout << "Fitness of first individual: " << h_fitness[0] << std::endl;
// Free host memory
delete[] h_population;
delete[] h_fitness;
return 0;
}
|
#include <iostream>
#include <hip/hip_runtime.h>
// Define constants
#define POPULATION_SIZE 1024
#define GENES_PER_INDIVIDUAL 32
#define NUM_THREADS 256
#define NUM_BLOCKS (POPULATION_SIZE / NUM_THREADS)
// CUDA kernel for genetic algorithm
__global__ void geneticAlgorithmKernel(float* population, float* fitness) {
// Calculate global thread index
int idx = blockIdx.x * blockDim.x + threadIdx.x;
// Ensure the thread index is within the population size
if (idx >= POPULATION_SIZE) return;
// Example operation: calculate fitness (sum of genes)
float sum = 0.0f;
for (int i = 0; i < GENES_PER_INDIVIDUAL; ++i) {
sum += population[idx * GENES_PER_INDIVIDUAL + i];
}
// Store the fitness value
fitness[idx] = sum;
}
int main() {
// Allocate memory for population and fitness on host
float* h_population = new float[POPULATION_SIZE * GENES_PER_INDIVIDUAL];
float* h_fitness = new float[POPULATION_SIZE];
// Initialize population with random values
for (int i = 0; i < POPULATION_SIZE * GENES_PER_INDIVIDUAL; ++i) {
h_population[i] = static_cast<float>(rand()) / RAND_MAX;
}
// Allocate memory for population and fitness on device
float* d_population;
float* d_fitness;
hipError_t err;
err = hipMalloc((void**)&d_population, POPULATION_SIZE * GENES_PER_INDIVIDUAL * sizeof(float));
if (err != hipSuccess) {
std::cerr << "Error allocating device memory for population: " << hipGetErrorString(err) << std::endl;
return -1;
}
err = hipMalloc((void**)&d_fitness, POPULATION_SIZE * sizeof(float));
if (err != hipSuccess) {
std::cerr << "Error allocating device memory for fitness: " << hipGetErrorString(err) << std::endl;
hipFree(d_population);
return -1;
}
// Copy population from host to device
err = hipMemcpy(d_population, h_population, POPULATION_SIZE * GENES_PER_INDIVIDUAL * sizeof(float), hipMemcpyHostToDevice);
if (err != hipSuccess) {
std::cerr << "Error copying population to device: " << hipGetErrorString(err) << std::endl;
hipFree(d_population);
hipFree(d_fitness);
return -1;
}
// Launch the genetic algorithm kernel
geneticAlgorithmKernel<<<NUM_BLOCKS, NUM_THREADS>>>(d_population, d_fitness);
// Check for errors in kernel launch
err = hipGetLastError();
if (err != hipSuccess) {
std::cerr << "Error launching kernel: " << hipGetErrorString(err) << std::endl;
hipFree(d_population);
hipFree(d_fitness);
return -1;
}
// Copy fitness from device to host
err = hipMemcpy(h_fitness, d_fitness, POPULATION_SIZE * sizeof(float), hipMemcpyDeviceToHost);
if (err != hipSuccess) {
std::cerr << "Error copying fitness to host: " << hipGetErrorString(err) << std::endl;
hipFree(d_population);
hipFree(d_fitness);
return -1;
}
// Free device memory
hipFree(d_population);
hipFree(d_fitness);
// Output the fitness of the first individual as a demonstration
std::cout << "Fitness of first individual: " << h_fitness[0] << std::endl;
// Free host memory
delete[] h_population;
delete[] h_fitness;
return 0;
}
|
Convert the following CUDA code to AMD GPU code:
cuda
#include <iostream>
#include <cuda_runtime.h>
// Kernel to perform parallel reduction using shared memory
__global__ void reduceProductKernel(int *g_idata, int *g_odata, int n) {
extern __shared__ int sdata[];
// Load shared mem from global mem
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x * blockDim.x + threadIdx.x;
sdata[tid] = (i < n) ? g_idata[i] : 1; // Initialize with 1 for multiplication
__syncthreads();
// Perform reduction in shared memory
for (unsigned int s = blockDim.x / 2; s > 0; s >>= 1) {
if (tid < s) {
sdata[tid] *= sdata[tid + s];
}
__syncthreads();
}
// Write result for this block to global mem
if (tid == 0) g_odata[blockIdx.x] = sdata[0];
}
int main(void) {
int N = 2048;
int blockSize = 256;
int numBlocks = (N + blockSize - 1) / blockSize;
// Allocate host memory
int *h_idata = (int *)malloc(N * sizeof(int));
int *h_odata = (int *)malloc(numBlocks * sizeof(int));
// Initialize host data
for (int i = 0; i < N; i++) {
h_idata[i] = i + 1; // Initialize with values 1 to 2048
}
// Allocate device memory
int *d_idata, *d_odata;
cudaError_t err = cudaMalloc((void **)&d_idata, N * sizeof(int));
if (err != cudaSuccess) {
std::cerr << "Error allocating d_idata: " << cudaGetErrorString(err) << std::endl;
return -1;
}
err = cudaMalloc((void **)&d_odata, numBlocks * sizeof(int));
if (err != cudaSuccess) {
std::cerr << "Error allocating d_odata: " << cudaGetErrorString(err) << std::endl;
return -1;
}
// Asynchronous memory copy to device
err = cudaMemcpyAsync(d_idata, h_idata, N * sizeof(int), cudaMemcpyHostToDevice, 0);
if (err != cudaSuccess) {
std::cerr << "Error copying data to device: " << cudaGetErrorString(err) << std::endl;
return -1;
}
// Launch kernel
reduceProductKernel<<<numBlocks, blockSize, blockSize * sizeof(int)>>>(d_idata, d_odata, N);
err = cudaGetLastError();
if (err != cudaSuccess) {
std::cerr << "Kernel launch error: " << cudaGetErrorString(err) << std::endl;
return -1;
}
// Allocate memory for the final reduction
int *d_finalOdata;
err = cudaMalloc((void **)&d_finalOdata, sizeof(int));
if (err != cudaSuccess) {
std::cerr << "Error allocating d_finalOdata: " << cudaGetErrorString(err) << std::endl;
return -1;
}
// Perform final reduction on the host if necessary
if (numBlocks > 1) {
// Copy results from device to host
err = cudaMemcpy(h_odata, d_odata, numBlocks * sizeof(int), cudaMemcpyDeviceToHost);
if (err != cudaSuccess) {
std::cerr << "Error copying data from device: " << cudaGetErrorString(err) << std::endl;
return -1;
}
// Perform final reduction on host
int finalProduct = 1;
for (int i = 0; i < numBlocks; i++) {
finalProduct *= h_odata[i];
}
std::cout << "Product of array elements: " << finalProduct << std::endl;
} else {
// Copy final result directly from device to host
err = cudaMemcpy(h_odata, d_odata, sizeof(int), cudaMemcpyDeviceToHost);
if (err != cudaSuccess) {
std::cerr << "Error copying data from device: " << cudaGetErrorString(err) << std::endl;
return -1;
}
std::cout << "Product of array elements: " << h_odata[0] << std::endl;
}
// Free device memory
cudaFree(d_idata);
cudaFree(d_odata);
cudaFree(d_finalOdata);
// Free host memory
free(h_idata);
free(h_odata);
return 0;
}
|
#include <iostream>
#include <hip/hip_runtime.h>
// Kernel to perform parallel reduction using shared memory
__global__ void reduceProductKernel(int *g_idata, int *g_odata, int n) {
extern __shared__ int sdata[];
// Load shared mem from global mem
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x * blockDim.x + threadIdx.x;
sdata[tid] = (i < n) ? g_idata[i] : 1; // Initialize with 1 for multiplication
__syncthreads();
// Perform reduction in shared memory
for (unsigned int s = blockDim.x / 2; s > 0; s >>= 1) {
if (tid < s) {
sdata[tid] *= sdata[tid + s];
}
__syncthreads();
}
// Write result for this block to global mem
if (tid == 0) g_odata[blockIdx.x] = sdata[0];
}
int main(void) {
int N = 2048;
int blockSize = 256;
int numBlocks = (N + blockSize - 1) / blockSize;
// Allocate host memory
int *h_idata = (int *)malloc(N * sizeof(int));
int *h_odata = (int *)malloc(numBlocks * sizeof(int));
// Initialize host data
for (int i = 0; i < N; i++) {
h_idata[i] = i + 1; // Initialize with values 1 to 2048
}
// Allocate device memory
int *d_idata, *d_odata;
hipError_t err = hipMalloc((void **)&d_idata, N * sizeof(int));
if (err != hipSuccess) {
std::cerr << "Error allocating d_idata: " << hipGetErrorString(err) << std::endl;
return -1;
}
err = hipMalloc((void **)&d_odata, numBlocks * sizeof(int));
if (err != hipSuccess) {
std::cerr << "Error allocating d_odata: " << hipGetErrorString(err) << std::endl;
return -1;
}
// Asynchronous memory copy to device
err = hipMemcpyAsync(d_idata, h_idata, N * sizeof(int), hipMemcpyHostToDevice, 0);
if (err != hipSuccess) {
std::cerr << "Error copying data to device: " << hipGetErrorString(err) << std::endl;
return -1;
}
// Launch kernel
reduceProductKernel<<<numBlocks, blockSize, blockSize * sizeof(int)>>>(d_idata, d_odata, N);
err = hipGetLastError();
if (err != hipSuccess) {
std::cerr << "Kernel launch error: " << hipGetErrorString(err) << std::endl;
return -1;
}
// Allocate memory for the final reduction
int *d_finalOdata;
err = hipMalloc((void **)&d_finalOdata, sizeof(int));
if (err != hipSuccess) {
std::cerr << "Error allocating d_finalOdata: " << hipGetErrorString(err) << std::endl;
return -1;
}
// Perform final reduction on the host if necessary
if (numBlocks > 1) {
// Copy results from device to host
err = hipMemcpy(h_odata, d_odata, numBlocks * sizeof(int), hipMemcpyDeviceToHost);
if (err != hipSuccess) {
std::cerr << "Error copying data from device: " << hipGetErrorString(err) << std::endl;
return -1;
}
// Perform final reduction on host
int finalProduct = 1;
for (int i = 0; i < numBlocks; i++) {
finalProduct *= h_odata[i];
}
std::cout << "Product of array elements: " << finalProduct << std::endl;
} else {
// Copy final result directly from device to host
err = hipMemcpy(h_odata, d_odata, sizeof(int), hipMemcpyDeviceToHost);
if (err != hipSuccess) {
std::cerr << "Error copying data from device: " << hipGetErrorString(err) << std::endl;
return -1;
}
std::cout << "Product of array elements: " << h_odata[0] << std::endl;
}
// Free device memory
hipFree(d_idata);
hipFree(d_odata);
hipFree(d_finalOdata);
// Free host memory
free(h_idata);
free(h_odata);
return 0;
}
|
Convert the following CUDA code to AMD GPU code:
cuda
#include <iostream>
#include <cuda_runtime.h>
// Define the sparse tensor structure
typedef struct {
int nnz; // Number of non-zero elements
int *row_indices; // Row indices for non-zero elements
int *col_indices; // Column indices for non-zero elements
float *values; // Values of non-zero elements
} SparseTensor;
// CUDA kernel for sparse tensor operations
__global__ void sparseTensorOperationKernel(SparseTensor d_sparseTensor, float *d_output) {
// Calculate the global thread index
int idx = blockIdx.x * blockDim.x + threadIdx.x;
// Check if the thread index is within the number of non-zero elements
if (idx < d_sparseTensor.nnz) {
int row = d_sparseTensor.row_indices[idx];
int col = d_sparseTensor.col_indices[idx];
float value = d_sparseTensor.values[idx];
// Example operation: accumulate the value into the output array at the row index
atomicAdd(&d_output[row], value);
}
}
// Function to initialize sparse tensor on the host
void initializeSparseTensor(SparseTensor &h_sparseTensor, int nnz) {
h_sparseTensor.nnz = nnz;
h_sparseTensor.row_indices = new int[nnz];
h_sparseTensor.col_indices = new int[nnz];
h_sparseTensor.values = new float[nnz];
// Initialize with some example data
for (int i = 0; i < nnz; ++i) {
h_sparseTensor.row_indices[i] = i % 10; // Example row index
h_sparseTensor.col_indices[i] = i % 5; // Example column index
h_sparseTensor.values[i] = 1.0f; // Example value
}
}
// Function to free sparse tensor memory
void freeSparseTensor(SparseTensor &h_sparseTensor) {
delete[] h_sparseTensor.row_indices;
delete[] h_sparseTensor.col_indices;
delete[] h_sparseTensor.values;
}
int main() {
// Number of non-zero elements
const int nnz = 32;
// Host sparse tensor
SparseTensor h_sparseTensor;
initializeSparseTensor(h_sparseTensor, nnz);
// Device sparse tensor
SparseTensor d_sparseTensor;
cudaMalloc(&d_sparseTensor.row_indices, nnz * sizeof(int));
cudaMalloc(&d_sparseTensor.col_indices, nnz * sizeof(int));
cudaMalloc(&d_sparseTensor.values, nnz * sizeof(float));
// Copy sparse tensor data to device
cudaMemcpy(d_sparseTensor.row_indices, h_sparseTensor.row_indices, nnz * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_sparseTensor.col_indices, h_sparseTensor.col_indices, nnz * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_sparseTensor.values, h_sparseTensor.values, nnz * sizeof(float), cudaMemcpyHostToDevice);
// Output array on host
float *h_output = new float[10]; // Assuming 10 rows for this example
for (int i = 0; i < 10; ++i) h_output[i] = 0.0f;
// Output array on device
float *d_output;
cudaMalloc(&d_output, 10 * sizeof(float));
cudaMemcpy(d_output, h_output, 10 * sizeof(float), cudaMemcpyHostToDevice);
// Define block and grid sizes
dim3 blockSize(32);
dim3 gridSize((nnz + blockSize.x - 1) / blockSize.x);
// Launch kernel
sparseTensorOperationKernel<<<gridSize, blockSize>>>(d_sparseTensor, d_output);
// Check for kernel launch errors
cudaError_t err = cudaGetLastError();
if (err != cudaSuccess) {
std::cerr << "Kernel launch failed: " << cudaGetErrorString(err) << std::endl;
return -1;
}
// Copy result back to host
cudaMemcpy(h_output, d_output, 10 * sizeof(float), cudaMemcpyDeviceToHost);
// Print the result
std::cout << "Resulting output array:" << std::endl;
for (int i = 0; i < 10; ++i) {
std::cout << "Output[" << i << "] = " << h_output[i] << std::endl;
}
// Free device memory
cudaFree(d_sparseTensor.row_indices);
cudaFree(d_sparseTensor.col_indices);
cudaFree(d_sparseTensor.values);
cudaFree(d_output);
// Free host memory
freeSparseTensor(h_sparseTensor);
delete[] h_output;
return 0;
}
|
#include <iostream>
#include <hip/hip_runtime.h>
// Define the sparse tensor structure
typedef struct {
int nnz; // Number of non-zero elements
int *row_indices; // Row indices for non-zero elements
int *col_indices; // Column indices for non-zero elements
float *values; // Values of non-zero elements
} SparseTensor;
// CUDA kernel for sparse tensor operations
__global__ void sparseTensorOperationKernel(SparseTensor d_sparseTensor, float *d_output) {
// Calculate the global thread index
int idx = blockIdx.x * blockDim.x + threadIdx.x;
// Check if the thread index is within the number of non-zero elements
if (idx < d_sparseTensor.nnz) {
int row = d_sparseTensor.row_indices[idx];
int col = d_sparseTensor.col_indices[idx];
float value = d_sparseTensor.values[idx];
// Example operation: accumulate the value into the output array at the row index
atomicAdd(&d_output[row], value);
}
}
// Function to initialize sparse tensor on the host
void initializeSparseTensor(SparseTensor &h_sparseTensor, int nnz) {
h_sparseTensor.nnz = nnz;
h_sparseTensor.row_indices = new int[nnz];
h_sparseTensor.col_indices = new int[nnz];
h_sparseTensor.values = new float[nnz];
// Initialize with some example data
for (int i = 0; i < nnz; ++i) {
h_sparseTensor.row_indices[i] = i % 10; // Example row index
h_sparseTensor.col_indices[i] = i % 5; // Example column index
h_sparseTensor.values[i] = 1.0f; // Example value
}
}
// Function to free sparse tensor memory
void freeSparseTensor(SparseTensor &h_sparseTensor) {
delete[] h_sparseTensor.row_indices;
delete[] h_sparseTensor.col_indices;
delete[] h_sparseTensor.values;
}
int main() {
// Number of non-zero elements
const int nnz = 32;
// Host sparse tensor
SparseTensor h_sparseTensor;
initializeSparseTensor(h_sparseTensor, nnz);
// Device sparse tensor
SparseTensor d_sparseTensor;
hipMalloc(&d_sparseTensor.row_indices, nnz * sizeof(int));
hipMalloc(&d_sparseTensor.col_indices, nnz * sizeof(int));
hipMalloc(&d_sparseTensor.values, nnz * sizeof(float));
// Copy sparse tensor data to device
hipMemcpy(d_sparseTensor.row_indices, h_sparseTensor.row_indices, nnz * sizeof(int), hipMemcpyHostToDevice);
hipMemcpy(d_sparseTensor.col_indices, h_sparseTensor.col_indices, nnz * sizeof(int), hipMemcpyHostToDevice);
hipMemcpy(d_sparseTensor.values, h_sparseTensor.values, nnz * sizeof(float), hipMemcpyHostToDevice);
// Output array on host
float *h_output = new float[10]; // Assuming 10 rows for this example
for (int i = 0; i < 10; ++i) h_output[i] = 0.0f;
// Output array on device
float *d_output;
hipMalloc(&d_output, 10 * sizeof(float));
hipMemcpy(d_output, h_output, 10 * sizeof(float), hipMemcpyHostToDevice);
// Define block and grid sizes
dim3 blockSize(32);
dim3 gridSize((nnz + blockSize.x - 1) / blockSize.x);
// Launch kernel
sparseTensorOperationKernel<<<gridSize, blockSize>>>(d_sparseTensor, d_output);
// Check for kernel launch errors
hipError_t err = hipGetLastError();
if (err != hipSuccess) {
std::cerr << "Kernel launch failed: " << hipGetErrorString(err) << std::endl;
return -1;
}
// Copy result back to host
hipMemcpy(h_output, d_output, 10 * sizeof(float), hipMemcpyDeviceToHost);
// Print the result
std::cout << "Resulting output array:" << std::endl;
for (int i = 0; i < 10; ++i) {
std::cout << "Output[" << i << "] = " << h_output[i] << std::endl;
}
// Free device memory
hipFree(d_sparseTensor.row_indices);
hipFree(d_sparseTensor.col_indices);
hipFree(d_sparseTensor.values);
hipFree(d_output);
// Free host memory
freeSparseTensor(h_sparseTensor);
delete[] h_output;
return 0;
}
|
Convert the following CUDA code to AMD GPU code:
cuda
// hartley_transform.cu
#include <iostream>
#include <cuda_runtime.h>
#include <cmath>
#define N 4096
#define BLOCK_SIZE 256
__device__ float2 ComplexMul(float2 a, float2 b) {
return make_float2(a.x * b.x - a.y * b.y, a.x * b.y + a.y * b.x);
}
__device__ float2 ComplexAdd(float2 a, float2 b) {
return make_float2(a.x + b.x, a.y + b.y);
}
__device__ float2 ComplexSub(float2 a, float2 b) {
return make_float2(a.x - b.x, a.y - b.y);
}
__global__ void hartley_transform(float2 *data) {
extern __shared__ float2 shared_data[];
int tid = threadIdx.x;
int block_offset = blockIdx.x * blockDim.x;
int index = block_offset + tid;
// Load data into shared memory
shared_data[tid] = data[index];
__syncthreads();
// Bit-reversal permutation
for (int s = blockDim.x / 2; s > 0; s >>= 1) {
int bit = tid & (s - 1);
int j = tid - bit + (s - bit - 1);
if (tid < s) {
float2 temp = shared_data[tid];
shared_data[tid] = shared_data[j];
shared_data[j] = temp;
}
__syncthreads();
}
// Cooley-Tukey FFT-like computation
for (int s = 2; s <= blockDim.x; s <<= 1) {
int m = s >> 1;
int j = tid % m;
float2 w = make_float2(cos(2 * M_PI * j / s), sin(2 * M_PI * j / s));
if (tid < m) w = make_float2(1.0f, 0.0f);
for (int t = 0; t < N / s; ++t) {
int i = t * s + tid;
int ip = i + m;
float2 temp = ComplexMul(w, shared_data[ip]);
shared_data[ip] = ComplexSub(shared_data[i], temp);
shared_data[i] = ComplexAdd(shared_data[i], temp);
}
__syncthreads();
}
// Store results back to global memory
data[index] = shared_data[tid];
}
int main() {
float2 *h_data = new float2[N];
float2 *d_data;
// Initialize input data
for (int i = 0; i < N; ++i) {
h_data[i].x = sin(2 * M_PI * i / N);
h_data[i].y = 0.0f;
}
// Allocate device memory
cudaError_t err = cudaMalloc((void**)&d_data, N * sizeof(float2));
if (err != cudaSuccess) {
std::cerr << "Error allocating device memory: " << cudaGetErrorString(err) << std::endl;
return -1;
}
// Copy data from host to device
err = cudaMemcpy(d_data, h_data, N * sizeof(float2), cudaMemcpyHostToDevice);
if (err != cudaSuccess) {
std::cerr << "Error copying data to device: " << cudaGetErrorString(err) << std::endl;
cudaFree(d_data);
delete[] h_data;
return -1;
}
// Launch kernel
int numBlocks = N / BLOCK_SIZE;
hartley_transform<<<numBlocks, BLOCK_SIZE, BLOCK_SIZE * sizeof(float2)>>>(d_data);
err = cudaGetLastError();
if (err != cudaSuccess) {
std::cerr << "Error launching kernel: " << cudaGetErrorString(err) << std::endl;
cudaFree(d_data);
delete[] h_data;
return -1;
}
// Copy result back to host
err = cudaMemcpy(h_data, d_data, N * sizeof(float2), cudaMemcpyDeviceToHost);
if (err != cudaSuccess) {
std::cerr << "Error copying data from device: " << cudaGetErrorString(err) << std::endl;
cudaFree(d_data);
delete[] h_data;
return -1;
}
// Free device memory
cudaFree(d_data);
// Output first 10 results for verification
for (int i = 0; i < 10; ++i) {
std::cout << "H[" << i << "] = (" << h_data[i].x << ", " << h_data[i].y << ")" << std::endl;
}
// Clean up
delete[] h_data;
return 0;
}
|
// hartley_transform.cu
#include <iostream>
#include <hip/hip_runtime.h>
#include <cmath>
#define N 4096
#define BLOCK_SIZE 256
__device__ float2 ComplexMul(float2 a, float2 b) {
return make_float2(a.x * b.x - a.y * b.y, a.x * b.y + a.y * b.x);
}
__device__ float2 ComplexAdd(float2 a, float2 b) {
return make_float2(a.x + b.x, a.y + b.y);
}
__device__ float2 ComplexSub(float2 a, float2 b) {
return make_float2(a.x - b.x, a.y - b.y);
}
__global__ void hartley_transform(float2 *data) {
extern __shared__ float2 shared_data[];
int tid = threadIdx.x;
int block_offset = blockIdx.x * blockDim.x;
int index = block_offset + tid;
// Load data into shared memory
shared_data[tid] = data[index];
__syncthreads();
// Bit-reversal permutation
for (int s = blockDim.x / 2; s > 0; s >>= 1) {
int bit = tid & (s - 1);
int j = tid - bit + (s - bit - 1);
if (tid < s) {
float2 temp = shared_data[tid];
shared_data[tid] = shared_data[j];
shared_data[j] = temp;
}
__syncthreads();
}
// Cooley-Tukey FFT-like computation
for (int s = 2; s <= blockDim.x; s <<= 1) {
int m = s >> 1;
int j = tid % m;
float2 w = make_float2(cos(2 * M_PI * j / s), sin(2 * M_PI * j / s));
if (tid < m) w = make_float2(1.0f, 0.0f);
for (int t = 0; t < N / s; ++t) {
int i = t * s + tid;
int ip = i + m;
float2 temp = ComplexMul(w, shared_data[ip]);
shared_data[ip] = ComplexSub(shared_data[i], temp);
shared_data[i] = ComplexAdd(shared_data[i], temp);
}
__syncthreads();
}
// Store results back to global memory
data[index] = shared_data[tid];
}
int main() {
float2 *h_data = new float2[N];
float2 *d_data;
// Initialize input data
for (int i = 0; i < N; ++i) {
h_data[i].x = sin(2 * M_PI * i / N);
h_data[i].y = 0.0f;
}
// Allocate device memory
hipError_t err = hipMalloc((void**)&d_data, N * sizeof(float2));
if (err != hipSuccess) {
std::cerr << "Error allocating device memory: " << hipGetErrorString(err) << std::endl;
return -1;
}
// Copy data from host to device
err = hipMemcpy(d_data, h_data, N * sizeof(float2), hipMemcpyHostToDevice);
if (err != hipSuccess) {
std::cerr << "Error copying data to device: " << hipGetErrorString(err) << std::endl;
hipFree(d_data);
delete[] h_data;
return -1;
}
// Launch kernel
int numBlocks = N / BLOCK_SIZE;
hartley_transform<<<numBlocks, BLOCK_SIZE, BLOCK_SIZE * sizeof(float2)>>>(d_data);
err = hipGetLastError();
if (err != hipSuccess) {
std::cerr << "Error launching kernel: " << hipGetErrorString(err) << std::endl;
hipFree(d_data);
delete[] h_data;
return -1;
}
// Copy result back to host
err = hipMemcpy(h_data, d_data, N * sizeof(float2), hipMemcpyDeviceToHost);
if (err != hipSuccess) {
std::cerr << "Error copying data from device: " << hipGetErrorString(err) << std::endl;
hipFree(d_data);
delete[] h_data;
return -1;
}
// Free device memory
hipFree(d_data);
// Output first 10 results for verification
for (int i = 0; i < 10; ++i) {
std::cout << "H[" << i << "] = (" << h_data[i].x << ", " << h_data[i].y << ")" << std::endl;
}
// Clean up
delete[] h_data;
return 0;
}
|
Convert the following CUDA code to AMD GPU code:
cuda
#include <iostream>
#include <cuda_runtime.h>
#include <cuComplex.h>
// Define a simple sparse FFT kernel using shared memory and atomics
// This example assumes a very simple sparse matrix structure for demonstration purposes
// In practice, the sparsity pattern and FFT algorithm would be more complex
#define BLOCK_SIZE 256
// Kernel to perform sparse FFT using shared memory and atomics
__global__ void sparseFFTKernel(cuComplex *d_input, cuComplex *d_output, int *d_indices, int N) {
__shared__ cuComplex sharedMem[BLOCK_SIZE];
// Load data into shared memory
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < N) {
int sparseIdx = d_indices[idx];
sharedMem[threadIdx.x] = d_input[sparseIdx];
} else {
sharedMem[threadIdx.x] = make_cuComplex(0.0f, 0.0f);
}
__syncthreads();
// Perform a simple FFT transformation in shared memory
// This is a placeholder for a more complex FFT algorithm
cuComplex sum = make_cuComplex(0.0f, 0.0f);
for (int i = 0; i < BLOCK_SIZE; i++) {
sum.x += sharedMem[i].x;
sum.y += sharedMem[i].y;
}
// Use atomic operations to write back results
// This is a simplified example; in practice, atomic operations should be used carefully
if (idx < N) {
atomicAdd(&d_output[idx].x, sum.x);
atomicAdd(&d_output[idx].y, sum.y);
}
}
// Error checking macro
#define CUDA_CHECK(call) \
{ \
cudaError_t err = call; \
if (err != cudaSuccess) { \
std::cerr << "CUDA error at " << __FILE__ << ":" << __LINE__ << " - " \
<< cudaGetErrorString(err) << std::endl; \
exit(EXIT_FAILURE); \
} \
}
int main() {
const int N = 1024; // Size of the input data
const int numBlocks = (N + BLOCK_SIZE - 1) / BLOCK_SIZE;
// Allocate host memory
cuComplex *h_input = new cuComplex[N];
cuComplex *h_output = new cuComplex[N];
int *h_indices = new int[N];
// Initialize input data and indices for sparsity
for (int i = 0; i < N; i++) {
h_input[i] = make_cuComplex(static_cast<float>(i), 0.0f);
h_indices[i] = i; // Simple identity mapping for demonstration
h_output[i] = make_cuComplex(0.0f, 0.0f);
}
// Allocate device memory
cuComplex *d_input, *d_output;
int *d_indices;
CUDA_CHECK(cudaMalloc(&d_input, N * sizeof(cuComplex)));
CUDA_CHECK(cudaMalloc(&d_output, N * sizeof(cuComplex)));
CUDA_CHECK(cudaMalloc(&d_indices, N * sizeof(int)));
// Copy data to device
CUDA_CHECK(cudaMemcpy(d_input, h_input, N * sizeof(cuComplex), cudaMemcpyHostToDevice));
CUDA_CHECK(cudaMemcpy(d_indices, h_indices, N * sizeof(int), cudaMemcpyHostToDevice));
CUDA_CHECK(cudaMemset(d_output, 0, N * sizeof(cuComplex)));
// Launch kernel
sparseFFTKernel<<<numBlocks, BLOCK_SIZE>>>(d_input, d_output, d_indices, N);
CUDA_CHECK(cudaGetLastError());
CUDA_CHECK(cudaDeviceSynchronize());
// Copy results back to host
CUDA_CHECK(cudaMemcpy(h_output, d_output, N * sizeof(cuComplex), cudaMemcpyDeviceToHost));
// Print first few results for verification
for (int i = 0; i < 10; i++) {
std::cout << "Output[" << i << "] = (" << h_output[i].x << ", " << h_output[i].y << ")" << std::endl;
}
// Free device memory
CUDA_CHECK(cudaFree(d_input));
CUDA_CHECK(cudaFree(d_output));
CUDA_CHECK(cudaFree(d_indices));
// Free host memory
delete[] h_input;
delete[] h_output;
delete[] h_indices;
return 0;
}
|
#include <iostream>
#include <hip/hip_runtime.h>
#include <hip/hip_complex.h>
// Define a simple sparse FFT kernel using shared memory and atomics
// This example assumes a very simple sparse matrix structure for demonstration purposes
// In practice, the sparsity pattern and FFT algorithm would be more complex
#define BLOCK_SIZE 256
// Kernel to perform sparse FFT using shared memory and atomics
__global__ void sparseFFTKernel(hipComplex *d_input, hipComplex *d_output, int *d_indices, int N) {
__shared__ hipComplex sharedMem[BLOCK_SIZE];
// Load data into shared memory
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < N) {
int sparseIdx = d_indices[idx];
sharedMem[threadIdx.x] = d_input[sparseIdx];
} else {
sharedMem[threadIdx.x] = make_hipComplex(0.0f, 0.0f);
}
__syncthreads();
// Perform a simple FFT transformation in shared memory
// This is a placeholder for a more complex FFT algorithm
hipComplex sum = make_hipComplex(0.0f, 0.0f);
for (int i = 0; i < BLOCK_SIZE; i++) {
sum.x += sharedMem[i].x;
sum.y += sharedMem[i].y;
}
// Use atomic operations to write back results
// This is a simplified example; in practice, atomic operations should be used carefully
if (idx < N) {
atomicAdd(&d_output[idx].x, sum.x);
atomicAdd(&d_output[idx].y, sum.y);
}
}
// Error checking macro
#define CUDA_CHECK(call) \
{ \
hipError_t err = call; \
if (err != hipSuccess) { \
std::cerr << "CUDA error at " << __FILE__ << ":" << __LINE__ << " - " \
<< hipGetErrorString(err) << std::endl; \
exit(EXIT_FAILURE); \
} \
}
int main() {
const int N = 1024; // Size of the input data
const int numBlocks = (N + BLOCK_SIZE - 1) / BLOCK_SIZE;
// Allocate host memory
hipComplex *h_input = new hipComplex[N];
hipComplex *h_output = new hipComplex[N];
int *h_indices = new int[N];
// Initialize input data and indices for sparsity
for (int i = 0; i < N; i++) {
h_input[i] = make_hipComplex(static_cast<float>(i), 0.0f);
h_indices[i] = i; // Simple identity mapping for demonstration
h_output[i] = make_hipComplex(0.0f, 0.0f);
}
// Allocate device memory
hipComplex *d_input, *d_output;
int *d_indices;
CUDA_CHECK(hipMalloc(&d_input, N * sizeof(hipComplex)));
CUDA_CHECK(hipMalloc(&d_output, N * sizeof(hipComplex)));
CUDA_CHECK(hipMalloc(&d_indices, N * sizeof(int)));
// Copy data to device
CUDA_CHECK(hipMemcpy(d_input, h_input, N * sizeof(hipComplex), hipMemcpyHostToDevice));
CUDA_CHECK(hipMemcpy(d_indices, h_indices, N * sizeof(int), hipMemcpyHostToDevice));
CUDA_CHECK(hipMemset(d_output, 0, N * sizeof(hipComplex)));
// Launch kernel
sparseFFTKernel<<<numBlocks, BLOCK_SIZE>>>(d_input, d_output, d_indices, N);
CUDA_CHECK(hipGetLastError());
CUDA_CHECK(hipDeviceSynchronize());
// Copy results back to host
CUDA_CHECK(hipMemcpy(h_output, d_output, N * sizeof(hipComplex), hipMemcpyDeviceToHost));
// Print first few results for verification
for (int i = 0; i < 10; i++) {
std::cout << "Output[" << i << "] = (" << h_output[i].x << ", " << h_output[i].y << ")" << std::endl;
}
// Free device memory
CUDA_CHECK(hipFree(d_input));
CUDA_CHECK(hipFree(d_output));
CUDA_CHECK(hipFree(d_indices));
// Free host memory
delete[] h_input;
delete[] h_output;
delete[] h_indices;
return 0;
}
|
Convert the following CUDA code to AMD GPU code:
cuda
#include <iostream>
#include <cuda_runtime.h>
#include <curand_kernel.h>
#define N 512
#define BLOCK_SIZE 16
__global__ void initializeParticles(curandState *states, float4 *positions, float4 *velocities, int seed) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < N) {
curandState localState = states[idx];
curand_init(seed, idx, 0, &localState);
positions[idx] = make_float4(curand_uniform(&localState) * N, curand_uniform(&localState) * N, curand_uniform(&localState) * N, 0.0f);
velocities[idx] = make_float4(curand_uniform(&localState) * 2.0f - 1.0f, curand_uniform(&localState) * 2.0f - 1.0f, curand_uniform(&localState) * 2.0f - 1.0f, 0.0f);
states[idx] = localState;
}
}
__global__ void updatePositions(float4 *positions, float4 *velocities, float dt) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < N) {
positions[idx].x += velocities[idx].x * dt;
positions[idx].y += velocities[idx].y * dt;
positions[idx].z += velocities[idx].z * dt;
}
}
__global__ void computeForces(float4 *positions, float4 *forces, float smoothingLength) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < N) {
forces[idx] = make_float4(0.0f, 0.0f, 0.0f, 0.0f);
for (int j = 0; j < N; ++j) {
if (idx != j) {
float3 r = make_float3(positions[idx].x - positions[j].x, positions[idx].y - positions[j].y, positions[idx].z - positions[j].z);
float dist = sqrtf(r.x * r.x + r.y * r.y + r.z * r.z);
if (dist < smoothingLength) {
float3 force = make_float3(0.01f * r.x / dist, 0.01f * r.y / dist, 0.01f * r.z / dist);
forces[idx] = make_float4(forces[idx].x + force.x, forces[idx].y + force.y, forces[idx].z + force.z, 0.0f);
}
}
}
}
}
__global__ void updateVelocities(float4 *velocities, float4 *forces, float dt, float mass) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < N) {
velocities[idx].x += (forces[idx].x / mass) * dt;
velocities[idx].y += (forces[idx].y / mass) * dt;
velocities[idx].z += (forces[idx].z / mass) * dt;
}
}
__global__ void simulateParticles(float4 *positions, float4 *velocities, float dt, float smoothingLength, float mass) {
extern __shared__ float4 sharedForces[];
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < N) {
// Initialize shared memory
sharedForces[threadIdx.x] = make_float4(0.0f, 0.0f, 0.0f, 0.0f);
__syncthreads();
// Compute forces
for (int j = 0; j < N; ++j) {
if (idx != j) {
float3 r = make_float3(positions[idx].x - positions[j].x, positions[idx].y - positions[j].y, positions[idx].z - positions[j].z);
float dist = sqrtf(r.x * r.x + r.y * r.y + r.z * r.z);
if (dist < smoothingLength) {
float3 force = make_float3(0.01f * r.x / dist, 0.01f * r.y / dist, 0.01f * r.z / dist);
atomicAdd(&sharedForces[threadIdx.x].x, force.x);
atomicAdd(&sharedForces[threadIdx.x].y, force.y);
atomicAdd(&sharedForces[threadIdx.x].z, force.z);
}
}
}
__syncthreads();
// Update velocities
velocities[idx].x += (sharedForces[threadIdx.x].x / mass) * dt;
velocities[idx].y += (sharedForces[threadIdx.x].y / mass) * dt;
velocities[idx].z += (sharedForces[threadIdx.x].z / mass) * dt;
// Update positions
positions[idx].x += velocities[idx].x * dt;
positions[idx].y += velocities[idx].y * dt;
positions[idx].z += velocities[idx].z * dt;
}
}
int main() {
float4 *d_positions, *d_velocities;
curandState *d_states;
int numBytes = N * sizeof(float4);
int numStatesBytes = N * sizeof(curandState);
cudaError_t err = cudaMalloc((void **)&d_positions, numBytes);
if (err != cudaSuccess) {
std::cerr << "Failed to allocate d_positions" << std::endl;
return -1;
}
err = cudaMalloc((void **)&d_velocities, numBytes);
if (err != cudaSuccess) {
std::cerr << "Failed to allocate d_velocities" << std::endl;
cudaFree(d_positions);
return -1;
}
err = cudaMalloc((void **)&d_states, numStatesBytes);
if (err != cudaSuccess) {
std::cerr << "Failed to allocate d_states" << std::endl;
cudaFree(d_positions);
cudaFree(d_velocities);
return -1;
}
dim3 blockSize(BLOCK_SIZE);
dim3 gridSize((N + blockSize.x - 1) / blockSize.x);
// Initialize particles
initializeParticles<<<gridSize, blockSize>>>(d_states, d_positions, d_velocities, 1234);
cudaDeviceSynchronize();
err = cudaGetLastError();
if (err != cudaSuccess) {
std::cerr << "Failed to launch initializeParticles kernel" << std::endl;
cudaFree(d_positions);
cudaFree(d_velocities);
cudaFree(d_states);
return -1;
}
// Simulation parameters
float dt = 0.01f;
float smoothingLength = 2.0f;
float mass = 1.0f;
// Simulate particles
for (int step = 0; step < 100; ++step) {
simulateParticles<<<gridSize, blockSize, blockSize.x * sizeof(float4)>>>(d_positions, d_velocities, dt, smoothingLength, mass);
cudaDeviceSynchronize();
err = cudaGetLastError();
if (err != cudaSuccess) {
std::cerr << "Failed to launch simulateParticles kernel" << std::endl;
cudaFree(d_positions);
cudaFree(d_velocities);
cudaFree(d_states);
return -1;
}
}
cudaFree(d_positions);
cudaFree(d_velocities);
cudaFree(d_states);
std::cout << "Simulation completed successfully" << std::endl;
return 0;
}
|
#include <iostream>
#include <hip/hip_runtime.h>
#include <hiprand/hiprand_kernel.h>
#define N 512
#define BLOCK_SIZE 16
__global__ void initializeParticles(hiprandState *states, float4 *positions, float4 *velocities, int seed) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < N) {
hiprandState localState = states[idx];
hiprand_init(seed, idx, 0, &localState);
positions[idx] = make_float4(hiprand_uniform(&localState) * N, hiprand_uniform(&localState) * N, hiprand_uniform(&localState) * N, 0.0f);
velocities[idx] = make_float4(hiprand_uniform(&localState) * 2.0f - 1.0f, hiprand_uniform(&localState) * 2.0f - 1.0f, hiprand_uniform(&localState) * 2.0f - 1.0f, 0.0f);
states[idx] = localState;
}
}
__global__ void updatePositions(float4 *positions, float4 *velocities, float dt) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < N) {
positions[idx].x += velocities[idx].x * dt;
positions[idx].y += velocities[idx].y * dt;
positions[idx].z += velocities[idx].z * dt;
}
}
__global__ void computeForces(float4 *positions, float4 *forces, float smoothingLength) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < N) {
forces[idx] = make_float4(0.0f, 0.0f, 0.0f, 0.0f);
for (int j = 0; j < N; ++j) {
if (idx != j) {
float3 r = make_float3(positions[idx].x - positions[j].x, positions[idx].y - positions[j].y, positions[idx].z - positions[j].z);
float dist = sqrtf(r.x * r.x + r.y * r.y + r.z * r.z);
if (dist < smoothingLength) {
float3 force = make_float3(0.01f * r.x / dist, 0.01f * r.y / dist, 0.01f * r.z / dist);
forces[idx] = make_float4(forces[idx].x + force.x, forces[idx].y + force.y, forces[idx].z + force.z, 0.0f);
}
}
}
}
}
__global__ void updateVelocities(float4 *velocities, float4 *forces, float dt, float mass) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < N) {
velocities[idx].x += (forces[idx].x / mass) * dt;
velocities[idx].y += (forces[idx].y / mass) * dt;
velocities[idx].z += (forces[idx].z / mass) * dt;
}
}
__global__ void simulateParticles(float4 *positions, float4 *velocities, float dt, float smoothingLength, float mass) {
extern __shared__ float4 sharedForces[];
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < N) {
// Initialize shared memory
sharedForces[threadIdx.x] = make_float4(0.0f, 0.0f, 0.0f, 0.0f);
__syncthreads();
// Compute forces
for (int j = 0; j < N; ++j) {
if (idx != j) {
float3 r = make_float3(positions[idx].x - positions[j].x, positions[idx].y - positions[j].y, positions[idx].z - positions[j].z);
float dist = sqrtf(r.x * r.x + r.y * r.y + r.z * r.z);
if (dist < smoothingLength) {
float3 force = make_float3(0.01f * r.x / dist, 0.01f * r.y / dist, 0.01f * r.z / dist);
atomicAdd(&sharedForces[threadIdx.x].x, force.x);
atomicAdd(&sharedForces[threadIdx.x].y, force.y);
atomicAdd(&sharedForces[threadIdx.x].z, force.z);
}
}
}
__syncthreads();
// Update velocities
velocities[idx].x += (sharedForces[threadIdx.x].x / mass) * dt;
velocities[idx].y += (sharedForces[threadIdx.x].y / mass) * dt;
velocities[idx].z += (sharedForces[threadIdx.x].z / mass) * dt;
// Update positions
positions[idx].x += velocities[idx].x * dt;
positions[idx].y += velocities[idx].y * dt;
positions[idx].z += velocities[idx].z * dt;
}
}
int main() {
float4 *d_positions, *d_velocities;
hiprandState *d_states;
int numBytes = N * sizeof(float4);
int numStatesBytes = N * sizeof(hiprandState);
hipError_t err = hipMalloc((void **)&d_positions, numBytes);
if (err != hipSuccess) {
std::cerr << "Failed to allocate d_positions" << std::endl;
return -1;
}
err = hipMalloc((void **)&d_velocities, numBytes);
if (err != hipSuccess) {
std::cerr << "Failed to allocate d_velocities" << std::endl;
hipFree(d_positions);
return -1;
}
err = hipMalloc((void **)&d_states, numStatesBytes);
if (err != hipSuccess) {
std::cerr << "Failed to allocate d_states" << std::endl;
hipFree(d_positions);
hipFree(d_velocities);
return -1;
}
dim3 blockSize(BLOCK_SIZE);
dim3 gridSize((N + blockSize.x - 1) / blockSize.x);
// Initialize particles
initializeParticles<<<gridSize, blockSize>>>(d_states, d_positions, d_velocities, 1234);
hipDeviceSynchronize();
err = hipGetLastError();
if (err != hipSuccess) {
std::cerr << "Failed to launch initializeParticles kernel" << std::endl;
hipFree(d_positions);
hipFree(d_velocities);
hipFree(d_states);
return -1;
}
// Simulation parameters
float dt = 0.01f;
float smoothingLength = 2.0f;
float mass = 1.0f;
// Simulate particles
for (int step = 0; step < 100; ++step) {
simulateParticles<<<gridSize, blockSize, blockSize.x * sizeof(float4)>>>(d_positions, d_velocities, dt, smoothingLength, mass);
hipDeviceSynchronize();
err = hipGetLastError();
if (err != hipSuccess) {
std::cerr << "Failed to launch simulateParticles kernel" << std::endl;
hipFree(d_positions);
hipFree(d_velocities);
hipFree(d_states);
return -1;
}
}
hipFree(d_positions);
hipFree(d_velocities);
hipFree(d_states);
std::cout << "Simulation completed successfully" << std::endl;
return 0;
}
|
Convert the following CUDA code to AMD GPU code:
cuda
#include <iostream>
#include <cuda_runtime.h>
#include <curand_kernel.h>
#define NUM_PARTICLES 128
#define DIMENSIONS 6
// Structure to hold particle data
struct Particle {
float position[DIMENSIONS];
float velocity[DIMENSIONS];
float pBestPosition[DIMENSIONS];
float pBestValue;
};
// Kernel to initialize particles
__global__ void initParticles(Particle* particles, curandState* states, float lowerBound, float upperBound) {
int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < NUM_PARTICLES) {
curandState localState = states[idx];
for (int d = 0; d < DIMENSIONS; ++d) {
particles[idx].position[d] = curand_uniform(&localState) * (upperBound - lowerBound) + lowerBound;
particles[idx].velocity[d] = 0.0f;
particles[idx].pBestPosition[d] = particles[idx].position[d];
}
particles[idx].pBestValue = 1e38f; // Initialize to a large number
states[idx] = localState;
}
}
// Kernel to update particles' positions and velocities
__global__ void updateParticles(Particle* particles, float w, float c1, float c2, Particle gBest, float lowerBound, float upperBound) {
int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < NUM_PARTICLES) {
curandState localState = curandState();
curand_init(idx, 0, 0, &localState);
float r1 = curand_uniform(&localState);
float r2 = curand_uniform(&localState);
for (int d = 0; d < DIMENSIONS; ++d) {
// Update velocity
particles[idx].velocity[d] = w * particles[idx].velocity[d] +
c1 * r1 * (particles[idx].pBestPosition[d] - particles[idx].position[d]) +
c2 * r2 * (gBest.position[d] - particles[idx].position[d]);
// Update position
particles[idx].position[d] += particles[idx].velocity[d];
// Apply bounds
particles[idx].position[d] = fmaxf(lowerBound, fminf(upperBound, particles[idx].position[d]));
}
}
}
// Function to evaluate fitness (simple sphere function)
__device__ float evaluateFitness(float* position) {
float sum = 0.0f;
for (int d = 0; d < DIMENSIONS; ++d) {
sum += position[d] * position[d];
}
return sum;
}
// Kernel to find the global best particle
__global__ void findGlobalBest(Particle* particles, Particle* gBest) {
__shared__ Particle sharedParticles[NUM_PARTICLES];
int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < NUM_PARTICLES) {
sharedParticles[threadIdx.x] = particles[idx];
__syncthreads();
// Find local best in shared memory
Particle localBest = sharedParticles[threadIdx.x];
for (int i = 0; i < blockDim.x; ++i) {
if (sharedParticles[i].pBestValue < localBest.pBestValue) {
localBest = sharedParticles[i];
}
}
// Update global best
if (threadIdx.x == 0) {
atomicMin((unsigned int*)&gBest->pBestValue, __float_as_uint(localBest.pBestValue));
if (gBest->pBestValue == localBest.pBestValue) {
for (int d = 0; d < DIMENSIONS; ++d) {
gBest->position[d] = localBest.position[d];
}
}
}
}
}
// Kernel to update personal bests
__global__ void updatePersonalBests(Particle* particles) {
int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < NUM_PARTICLES) {
float fitness = evaluateFitness(particles[idx].position);
if (fitness < particles[idx].pBestValue) {
particles[idx].pBestValue = fitness;
for (int d = 0; d < DIMENSIONS; ++d) {
particles[idx].pBestPosition[d] = particles[idx].position[d];
}
}
}
}
int main() {
const int numBlocks = (NUM_PARTICLES + 255) / 256;
const int blockSize = 256;
Particle* d_particles;
Particle* d_gBest;
curandState* d_states;
// Allocate memory on device
cudaMalloc((void**)&d_particles, NUM_PARTICLES * sizeof(Particle));
cudaMalloc((void**)&d_gBest, sizeof(Particle));
cudaMalloc((void**)&d_states, NUM_PARTICLES * sizeof(curandState));
// Initialize particles
initParticles<<<numBlocks, blockSize>>>(d_particles, d_states, -5.0f, 5.0f);
cudaDeviceSynchronize();
// Initialize global best
Particle h_gBest;
h_gBest.pBestValue = 1e38f;
cudaMemcpy(d_gBest, &h_gBest, sizeof(Particle), cudaMemcpyHostToDevice);
// Parameters for PSO
float w = 0.729f; // Inertia weight
float c1 = 1.49445f; // Cognitive coefficient
float c2 = 1.49445f; // Social coefficient
// Run PSO for a number of iterations
int iterations = 100;
for (int i = 0; i < iterations; ++i) {
updatePersonalBests<<<numBlocks, blockSize>>>(d_particles);
cudaDeviceSynchronize();
findGlobalBest<<<1, NUM_PARTICLES>>>(d_particles, d_gBest);
cudaDeviceSynchronize();
updateParticles<<<numBlocks, blockSize>>>(d_particles, w, c1, c2, *d_gBest, -5.0f, 5.0f);
cudaDeviceSynchronize();
}
// Copy global best back to host
cudaMemcpy(&h_gBest, d_gBest, sizeof(Particle), cudaMemcpyDeviceToHost);
// Output the result
std::cout << "Global Best Position: ";
for (int d = 0; d < DIMENSIONS; ++d) {
std::cout << h_gBest.position[d] << " ";
}
std::cout << "\nGlobal Best Value: " << h_gBest.pBestValue << std::endl;
// Free device memory
cudaFree(d_particles);
cudaFree(d_gBest);
cudaFree(d_states);
return 0;
}
|
#include <iostream>
#include <hip/hip_runtime.h>
#include <hiprand/hiprand_kernel.h>
#define NUM_PARTICLES 128
#define DIMENSIONS 6
// Structure to hold particle data
struct Particle {
float position[DIMENSIONS];
float velocity[DIMENSIONS];
float pBestPosition[DIMENSIONS];
float pBestValue;
};
// Kernel to initialize particles
__global__ void initParticles(Particle* particles, hiprandState* states, float lowerBound, float upperBound) {
int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < NUM_PARTICLES) {
hiprandState localState = states[idx];
for (int d = 0; d < DIMENSIONS; ++d) {
particles[idx].position[d] = hiprand_uniform(&localState) * (upperBound - lowerBound) + lowerBound;
particles[idx].velocity[d] = 0.0f;
particles[idx].pBestPosition[d] = particles[idx].position[d];
}
particles[idx].pBestValue = 1e38f; // Initialize to a large number
states[idx] = localState;
}
}
// Kernel to update particles' positions and velocities
__global__ void updateParticles(Particle* particles, float w, float c1, float c2, Particle gBest, float lowerBound, float upperBound) {
int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < NUM_PARTICLES) {
hiprandState localState = hiprandState();
hiprand_init(idx, 0, 0, &localState);
float r1 = hiprand_uniform(&localState);
float r2 = hiprand_uniform(&localState);
for (int d = 0; d < DIMENSIONS; ++d) {
// Update velocity
particles[idx].velocity[d] = w * particles[idx].velocity[d] +
c1 * r1 * (particles[idx].pBestPosition[d] - particles[idx].position[d]) +
c2 * r2 * (gBest.position[d] - particles[idx].position[d]);
// Update position
particles[idx].position[d] += particles[idx].velocity[d];
// Apply bounds
particles[idx].position[d] = fmaxf(lowerBound, fminf(upperBound, particles[idx].position[d]));
}
}
}
// Function to evaluate fitness (simple sphere function)
__device__ float evaluateFitness(float* position) {
float sum = 0.0f;
for (int d = 0; d < DIMENSIONS; ++d) {
sum += position[d] * position[d];
}
return sum;
}
// Kernel to find the global best particle
__global__ void findGlobalBest(Particle* particles, Particle* gBest) {
__shared__ Particle sharedParticles[NUM_PARTICLES];
int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < NUM_PARTICLES) {
sharedParticles[threadIdx.x] = particles[idx];
__syncthreads();
// Find local best in shared memory
Particle localBest = sharedParticles[threadIdx.x];
for (int i = 0; i < blockDim.x; ++i) {
if (sharedParticles[i].pBestValue < localBest.pBestValue) {
localBest = sharedParticles[i];
}
}
// Update global best
if (threadIdx.x == 0) {
atomicMin((unsigned int*)&gBest->pBestValue, __float_as_uint(localBest.pBestValue));
if (gBest->pBestValue == localBest.pBestValue) {
for (int d = 0; d < DIMENSIONS; ++d) {
gBest->position[d] = localBest.position[d];
}
}
}
}
}
// Kernel to update personal bests
__global__ void updatePersonalBests(Particle* particles) {
int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < NUM_PARTICLES) {
float fitness = evaluateFitness(particles[idx].position);
if (fitness < particles[idx].pBestValue) {
particles[idx].pBestValue = fitness;
for (int d = 0; d < DIMENSIONS; ++d) {
particles[idx].pBestPosition[d] = particles[idx].position[d];
}
}
}
}
int main() {
const int numBlocks = (NUM_PARTICLES + 255) / 256;
const int blockSize = 256;
Particle* d_particles;
Particle* d_gBest;
hiprandState* d_states;
// Allocate memory on device
hipMalloc((void**)&d_particles, NUM_PARTICLES * sizeof(Particle));
hipMalloc((void**)&d_gBest, sizeof(Particle));
hipMalloc((void**)&d_states, NUM_PARTICLES * sizeof(hiprandState));
// Initialize particles
initParticles<<<numBlocks, blockSize>>>(d_particles, d_states, -5.0f, 5.0f);
hipDeviceSynchronize();
// Initialize global best
Particle h_gBest;
h_gBest.pBestValue = 1e38f;
hipMemcpy(d_gBest, &h_gBest, sizeof(Particle), hipMemcpyHostToDevice);
// Parameters for PSO
float w = 0.729f; // Inertia weight
float c1 = 1.49445f; // Cognitive coefficient
float c2 = 1.49445f; // Social coefficient
// Run PSO for a number of iterations
int iterations = 100;
for (int i = 0; i < iterations; ++i) {
updatePersonalBests<<<numBlocks, blockSize>>>(d_particles);
hipDeviceSynchronize();
findGlobalBest<<<1, NUM_PARTICLES>>>(d_particles, d_gBest);
hipDeviceSynchronize();
updateParticles<<<numBlocks, blockSize>>>(d_particles, w, c1, c2, *d_gBest, -5.0f, 5.0f);
hipDeviceSynchronize();
}
// Copy global best back to host
hipMemcpy(&h_gBest, d_gBest, sizeof(Particle), hipMemcpyDeviceToHost);
// Output the result
std::cout << "Global Best Position: ";
for (int d = 0; d < DIMENSIONS; ++d) {
std::cout << h_gBest.position[d] << " ";
}
std::cout << "\nGlobal Best Value: " << h_gBest.pBestValue << std::endl;
// Free device memory
hipFree(d_particles);
hipFree(d_gBest);
hipFree(d_states);
return 0;
}
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