mirror of
https://github.com/L-yang-yang/cugenopt.git
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132 lines
4.4 KiB
Text
132 lines
4.4 KiB
Text
#pragma once
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#include "types.cuh"
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#include "cuda_utils.cuh"
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#include "operators.cuh"
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// 优化的大规模 VRP(最多 500 个客户,80 辆车)
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// D1=80 支持 80 辆车,D2=128 每车最多 128 个客户
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// Solution 大小 = 80×128×4 = 40 KB
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struct OptimizedVRPv2Problem : ProblemBase<OptimizedVRPv2Problem, 80, 128> {
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const float* d_dist;
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const float* d_demand;
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const float* h_dist;
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const float* h_demand;
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int n;
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float capacity;
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int num_vehicles;
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int max_vehicles;
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static constexpr ObjDef OBJ_DEFS[] = {
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{ObjDir::Minimize, 1.0f, 0.0f}
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};
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__device__ float compute_obj(int obj_idx, const Sol& s) const {
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float total = 0;
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for (int v = 0; v < num_vehicles; v++) {
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int route_len = s.dim2_sizes[v];
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if (route_len == 0) continue;
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int first_node = s.data[v][0] + 1;
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total += d_dist[0 * (n+1) + first_node];
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int prev = first_node;
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for (int i = 1; i < route_len; i++) {
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int node = s.data[v][i] + 1;
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total += d_dist[prev * (n+1) + node];
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prev = node;
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}
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total += d_dist[prev * (n+1) + 0];
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}
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return total;
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}
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__device__ float compute_penalty(const Sol& s) const {
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float penalty = 0;
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for (int v = 0; v < num_vehicles; v++) {
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float load = 0;
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for (int i = 0; i < s.dim2_sizes[v]; i++) {
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load += d_demand[s.data[v][i]];
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}
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if (load > capacity) {
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penalty += (load - capacity) * 100.0f;
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}
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}
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return penalty;
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}
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ProblemConfig config() const {
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ProblemConfig cfg;
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cfg.encoding = EncodingType::Permutation;
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cfg.dim1 = num_vehicles;
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cfg.dim2_default = 0;
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fill_obj_config(cfg);
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cfg.cross_row_prob = 0.3f;
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cfg.row_mode = RowMode::Partition;
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cfg.total_elements = n;
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return cfg;
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}
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size_t working_set_bytes() const {
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return (size_t)(n + 1) * (n + 1) * sizeof(float) + (size_t)n * sizeof(float);
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}
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static OptimizedVRPv2Problem create(const float* h_dist_matrix, const float* h_demand_array,
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int num_customers, float vehicle_capacity,
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int num_veh, int max_veh) {
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OptimizedVRPv2Problem prob;
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prob.n = num_customers;
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prob.capacity = vehicle_capacity;
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prob.num_vehicles = num_veh;
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prob.max_vehicles = max_veh;
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prob.h_dist = h_dist_matrix;
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prob.h_demand = h_demand_array;
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size_t dist_size = (size_t)(num_customers + 1) * (num_customers + 1) * sizeof(float);
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size_t demand_size = (size_t)num_customers * sizeof(float);
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CUDA_CHECK(cudaMalloc(&prob.d_dist, dist_size));
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CUDA_CHECK(cudaMalloc(&prob.d_demand, demand_size));
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CUDA_CHECK(cudaMemcpy((void*)prob.d_dist, h_dist_matrix, dist_size, cudaMemcpyHostToDevice));
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CUDA_CHECK(cudaMemcpy((void*)prob.d_demand, h_demand_array, demand_size, cudaMemcpyHostToDevice));
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return prob;
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}
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void destroy() {
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if (d_dist) cudaFree((void*)d_dist);
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if (d_demand) cudaFree((void*)d_demand);
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d_dist = nullptr;
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d_demand = nullptr;
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}
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OptimizedVRPv2Problem* clone_to_device(int target_gpu) const {
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int orig_device;
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CUDA_CHECK(cudaGetDevice(&orig_device));
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CUDA_CHECK(cudaSetDevice(target_gpu));
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float* dd;
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float* ddem;
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size_t dist_size = (size_t)(n + 1) * (n + 1) * sizeof(float);
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size_t demand_size = (size_t)n * sizeof(float);
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CUDA_CHECK(cudaMalloc(&dd, dist_size));
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CUDA_CHECK(cudaMalloc(&ddem, demand_size));
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CUDA_CHECK(cudaMemcpy(dd, h_dist, dist_size, cudaMemcpyHostToDevice));
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CUDA_CHECK(cudaMemcpy(ddem, h_demand, demand_size, cudaMemcpyHostToDevice));
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CUDA_CHECK(cudaSetDevice(orig_device));
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OptimizedVRPv2Problem* new_prob = new OptimizedVRPv2Problem();
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new_prob->n = n;
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new_prob->capacity = capacity;
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new_prob->num_vehicles = num_vehicles;
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new_prob->max_vehicles = max_vehicles;
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new_prob->h_dist = h_dist;
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new_prob->h_demand = h_demand;
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new_prob->d_dist = dd;
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new_prob->d_demand = ddem;
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return new_prob;
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}
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};
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