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Initial commit: cuGenOpt GPU optimization solver

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L-yang-yang 2026-03-20 00:33:45 +08:00
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benchmark/experiments/e10_large_scale/gpu.cu Normal file
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#include "solver.cuh"
#include "multi_gpu_solver.cuh"
#include "large_tsp_problem.cuh"
#include "large_vrp_problem.cuh"
#include <cstdio>
#include <cstdlib>
#include <ctime>
#include <vector>
#include <algorithm>
// 生成随机TSP实例
void generate_random_tsp(float* dist, int n, unsigned seed) {
srand(seed);
for (int i = 0; i < n; i++) {
dist[i * n + i] = 0.0f;
for (int j = i + 1; j < n; j++) {
float d = 10.0f + (rand() % 10000) / 10.0f;
dist[i * n + j] = d;
dist[j * n + i] = d;
}
}
}
// 生成随机VRP实例
void generate_random_vrp(float* dist, float* demand, int n, unsigned seed) {
srand(seed);
int stride = n + 1;
// 距离矩阵（包含depot）
for (int i = 0; i < stride; i++) {
dist[i * stride + i] = 0.0f;
for (int j = i + 1; j < stride; j++) {
float d = 10.0f + (rand() % 10000) / 10.0f;
dist[i * stride + j] = d;
dist[j * stride + i] = d;
}
}
// 需求
for (int i = 0; i < n; i++) {
demand[i] = 5.0f + (rand() % 20);
}
}
int main() {
printf("==============================================\n");
printf("E10: 大规模问题实验 (TSP & VRP)\n");
printf("==============================================\n\n");
// 检测可用GPU数量
int num_gpus;
cudaGetDeviceCount(&num_gpus);
printf("检测到 %d 个 GPU\n\n", num_gpus);
const int num_runs = 5;
// ========== TSP 大规模测试 ==========
printf("实验 1: TSP 大规模测试\n");
printf("----------------------------------------------\n");
std::vector<int> tsp_sizes = {100, 200, 300, 400, 500};
for (int n : tsp_sizes) {
printf("\n[TSP n=%d]\n", n);
// 生成实例
float* h_dist = new float[n * n];
generate_random_tsp(h_dist, n, 12345);
auto prob = LargeTSPProblem::create(h_dist, n);
// 配置
SolverConfig cfg;
cfg.pop_size = 0; // 自适应
cfg.max_gen = 10000;
cfg.verbose = false;
cfg.num_islands = 16;
cfg.use_aos = true;
cfg.sa_temp_init = 50.0f;
cfg.use_cuda_graph = true;
// 单GPU测试
printf(" 单GPU (5 runs): ");
std::vector<float> single_gpu_results;
for (int run = 0; run < num_runs; run++) {
cfg.seed = 42 + run * 100;
auto result = solve(prob, cfg);
single_gpu_results.push_back(result.best_solution.objectives[0]);
printf("%.1f ", result.best_solution.objectives[0]);
}
float avg_single = 0;
for (float v : single_gpu_results) avg_single += v;
avg_single /= num_runs;
printf(" → 平均: %.2f\n", avg_single);
// 多GPU测试（如果可用）
if (num_gpus >= 2) {
printf(" 多GPU (%d GPUs, 5 runs): ", num_gpus);
std::vector<float> multi_gpu_results;
cfg.num_gpus = num_gpus;
for (int run = 0; run < num_runs; run++) {
cfg.seed = 42 + run * 100;
auto result = solve_multi_gpu(prob, cfg);
multi_gpu_results.push_back(result.best_solution.objectives[0]);
printf("%.1f ", result.best_solution.objectives[0]);
}
float avg_multi = 0;
for (float v : multi_gpu_results) avg_multi += v;
avg_multi /= num_runs;
float improvement = (avg_single - avg_multi) / avg_single * 100;
printf(" → 平均: %.2f (%.2f%%)\n", avg_multi, improvement);
}
prob.destroy();
delete[] h_dist;
}
// ========== VRP 大规模测试 ==========
printf("\n\n实验 2: VRP 大规模测试\n");
printf("----------------------------------------------\n");
std::vector<int> vrp_sizes = {50, 100, 150, 200};
for (int n : vrp_sizes) {
printf("\n[VRP n=%d]\n", n);
// 生成实例
float* h_dist = new float[(n+1) * (n+1)];
float* h_demand = new float[n];
generate_random_vrp(h_dist, h_demand, n, 23456);
int num_vehicles = (n / 20) + 1; // 动态车辆数
float capacity = 150.0f;
auto prob = LargeVRPProblem::create(h_dist, h_demand, n, capacity, num_vehicles, num_vehicles + 4);
// 配置
SolverConfig cfg;
cfg.pop_size = 0; // 自适应
cfg.max_gen = 10000;
cfg.verbose = false;
cfg.num_islands = 16;
cfg.use_aos = true;
cfg.sa_temp_init = 50.0f;
cfg.use_cuda_graph = true;
// 单GPU测试
printf(" 单GPU (5 runs): ");
std::vector<float> single_gpu_results;
for (int run = 0; run < num_runs; run++) {
cfg.seed = 42 + run * 100;
auto result = solve(prob, cfg);
single_gpu_results.push_back(result.best_solution.objectives[0]);
printf("%.1f ", result.best_solution.objectives[0]);
}
float avg_single = 0;
for (float v : single_gpu_results) avg_single += v;
avg_single /= num_runs;
printf(" → 平均: %.2f\n", avg_single);
// 多GPU测试（如果可用）
if (num_gpus >= 2) {
printf(" 多GPU (%d GPUs, 5 runs): ", num_gpus);
std::vector<float> multi_gpu_results;
cfg.num_gpus = num_gpus;
for (int run = 0; run < num_runs; run++) {
cfg.seed = 42 + run * 100;
auto result = solve_multi_gpu(prob, cfg);
multi_gpu_results.push_back(result.best_solution.objectives[0]);
printf("%.1f ", result.best_solution.objectives[0]);
}
float avg_multi = 0;
for (float v : multi_gpu_results) avg_multi += v;
avg_multi /= num_runs;
float improvement = (avg_single - avg_multi) / avg_single * 100;
printf(" → 平均: %.2f (%.2f%%)\n", avg_multi, improvement);
}
prob.destroy();
delete[] h_dist;
delete[] h_demand;
}
printf("\n==============================================\n");
printf("实验完成！\n");
printf("==============================================\n");
return 0;
}