mirror of
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716 lines
25 KiB
Text
716 lines
25 KiB
Text
/**
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* E6: GPU 硬件对比
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*
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* 目的：验证 Memory-Bound 特性，量化不同 GPU 的加速效果
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*
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* 实验设计：
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* Part A — 固定代数 (gen=2000)：测量纯吞吐量差异
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* TSP eil51/kroA100/ch150, CVRP10, Schedule3x4
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* Part B — 固定时间 (30s)：测量相同时间下的解质量差异
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* QAP tai15a, JSP ft10, Knapsack100, VRPTW R101/C101/RC101
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*
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* Part B 的实例覆盖：
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* - Shared memory 内：QAP (2KB), JSP (800B), Knapsack (800B)
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* - Shared memory 溢出：VRPTW (40KB+, 超 T4 48KB 限制)
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* → 验证 V100 (96KB smem) 是否能让 VRPTW 回到 shared memory
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*
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* 用法：./gpu [data_dir]
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* 在不同 GPU 上分别运行，结果文件命名包含 GPU 型号
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*/
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#include "bench_common.cuh"
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#include <cstdlib>
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#include <cstdio>
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#include <vector>
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#include <fstream>
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#include <sstream>
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#include <string>
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#include <cmath>
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// ============================================================
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// 文件解析工具（与 E7 共用）
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// ============================================================
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struct QAPData {
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int n;
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std::vector<float> dist;
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std::vector<float> flow;
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};
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static QAPData parse_qaplib(const char* path) {
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QAPData d;
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std::ifstream f(path);
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if (!f.is_open()) { fprintf(stderr, "Cannot open %s\n", path); exit(1); }
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f >> d.n;
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int nn = d.n * d.n;
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d.dist.resize(nn);
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d.flow.resize(nn);
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for (int i = 0; i < nn; i++) f >> d.dist[i];
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for (int i = 0; i < nn; i++) f >> d.flow[i];
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return d;
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}
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struct JSPData {
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int num_jobs, num_machines;
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std::vector<int> machines;
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std::vector<float> durations;
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};
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static JSPData parse_jsp(const char* path) {
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JSPData d;
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std::ifstream f(path);
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if (!f.is_open()) { fprintf(stderr, "Cannot open %s\n", path); exit(1); }
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f >> d.num_jobs >> d.num_machines;
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int total = d.num_jobs * d.num_machines;
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d.machines.resize(total);
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d.durations.resize(total);
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for (int j = 0; j < d.num_jobs; j++) {
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for (int o = 0; o < d.num_machines; o++) {
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int m; float dur;
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f >> m >> dur;
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d.machines[j * d.num_machines + o] = m;
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d.durations[j * d.num_machines + o] = dur;
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}
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}
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return d;
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}
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struct KnapsackData {
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int n;
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float capacity;
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std::vector<float> values;
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std::vector<float> weights;
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};
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static KnapsackData parse_knapsack(const char* path) {
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KnapsackData d;
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std::ifstream f(path);
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if (!f.is_open()) { fprintf(stderr, "Cannot open %s\n", path); exit(1); }
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int cap;
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f >> d.n >> cap;
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d.capacity = (float)cap;
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d.values.resize(d.n);
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d.weights.resize(d.n);
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for (int i = 0; i < d.n; i++) {
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int v, w;
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f >> v >> w;
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d.values[i] = (float)v;
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d.weights[i] = (float)w;
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}
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return d;
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}
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static int knapsack_dp_optimal(const KnapsackData& d) {
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int cap = (int)d.capacity;
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std::vector<int> dp(cap + 1, 0);
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for (int i = 0; i < d.n; i++) {
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int w = (int)d.weights[i], v = (int)d.values[i];
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for (int c = cap; c >= w; c--)
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if (dp[c - w] + v > dp[c])
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dp[c] = dp[c - w] + v;
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}
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return dp[cap];
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}
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struct SolomonNode {
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int id;
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float x, y;
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float demand;
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float ready, due, service;
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};
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struct SolomonData {
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int num_vehicles;
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float capacity;
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std::vector<SolomonNode> nodes;
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int num_customers;
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std::vector<float> dist;
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};
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static SolomonData parse_solomon(const char* path) {
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SolomonData d;
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std::ifstream f(path);
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if (!f.is_open()) { fprintf(stderr, "Cannot open %s\n", path); exit(1); }
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std::string line;
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std::getline(f, line);
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while (std::getline(f, line)) {
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if (line.find("NUMBER") != std::string::npos && line.find("CAPACITY") != std::string::npos)
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break;
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}
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f >> d.num_vehicles >> d.capacity;
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while (std::getline(f, line)) {
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if (line.find("CUST") != std::string::npos) break;
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}
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std::getline(f, line);
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SolomonNode node;
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while (f >> node.id >> node.x >> node.y >> node.demand
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>> node.ready >> node.due >> node.service) {
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d.nodes.push_back(node);
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}
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d.num_customers = (int)d.nodes.size() - 1;
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int nn = (int)d.nodes.size();
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d.dist.resize(nn * nn);
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for (int i = 0; i < nn; i++)
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for (int j = 0; j < nn; j++) {
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float dx = d.nodes[i].x - d.nodes[j].x;
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float dy = d.nodes[i].y - d.nodes[j].y;
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d.dist[i * nn + j] = sqrtf(dx * dx + dy * dy);
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}
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return d;
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}
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// ============================================================
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// QAP Problem (D2=16, N<=16)
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// ============================================================
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struct QAPMedium : ProblemBase<QAPMedium, 1, 16> {
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const float* d_flow;
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const float* d_dist;
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int n;
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__device__ float calc_cost(const Sol& s) const {
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float cost = 0.0f;
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int sz = s.dim2_sizes[0];
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for (int i = 0; i < sz; i++)
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for (int j = 0; j < sz; j++)
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cost += d_flow[i * n + j] * d_dist[s.data[0][i] * n + s.data[0][j]];
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return cost;
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}
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static constexpr ObjDef OBJ_DEFS[] = { {ObjDir::Minimize, 1.0f, 0.0f} };
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__device__ float compute_obj(int, const Sol& s) const { return calc_cost(s); }
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__device__ float compute_penalty(const Sol&) const { return 0.0f; }
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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 = 1; cfg.dim2_default = n;
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fill_obj_config(cfg);
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return cfg;
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}
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size_t shared_mem_bytes() const { return 2 * (size_t)n * n * sizeof(float); }
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__device__ void load_shared(char* smem, int tid, int bsz) {
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float* sf = reinterpret_cast<float*>(smem);
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float* sd = sf + n * n;
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int total = n * n;
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for (int i = tid; i < total; i += bsz) { sf[i] = d_flow[i]; sd[i] = d_dist[i]; }
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d_flow = sf; d_dist = sd;
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}
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static QAPMedium create(const float* h_flow, const float* h_dist, int n) {
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QAPMedium p;
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p.n = n;
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float *df, *dd;
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CUDA_CHECK(cudaMalloc(&df, sizeof(float) * n * n));
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CUDA_CHECK(cudaMalloc(&dd, sizeof(float) * n * n));
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CUDA_CHECK(cudaMemcpy(df, h_flow, sizeof(float) * n * n, cudaMemcpyHostToDevice));
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CUDA_CHECK(cudaMemcpy(dd, h_dist, sizeof(float) * n * n, cudaMemcpyHostToDevice));
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p.d_flow = df; p.d_dist = dd;
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return p;
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}
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void destroy() {
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if (d_flow) cudaFree(const_cast<float*>(d_flow));
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if (d_dist) cudaFree(const_cast<float*>(d_dist));
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d_flow = nullptr; d_dist = nullptr;
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}
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};
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// ============================================================
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// JSP Perm Problem (D2=128, J*O<=128, J/M<=16)
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// ============================================================
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struct JSPPermMedium : ProblemBase<JSPPermMedium, 1, 128> {
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const int* d_machine;
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const float* d_duration;
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int num_jobs, num_ops, num_machines;
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__device__ float decode_and_makespan(const Sol& s) const {
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int total = num_jobs * num_ops;
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int size = s.dim2_sizes[0];
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if (size < total) return 1e9f;
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float job_avail[16] = {};
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float mach_avail[16] = {};
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int job_next_op[16] = {};
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float makespan = 0.0f;
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for (int k = 0; k < total; k++) {
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int j = s.data[0][k];
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if (j < 0 || j >= num_jobs) return 1e9f;
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int op = job_next_op[j];
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if (op >= num_ops) continue;
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int flat = j * num_ops + op;
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int m = d_machine[flat];
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float dur = d_duration[flat];
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float start = fmaxf(job_avail[j], mach_avail[m]);
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float end = start + dur;
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job_avail[j] = end;
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mach_avail[m] = end;
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job_next_op[j] = op + 1;
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if (end > makespan) makespan = end;
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}
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return makespan;
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}
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static constexpr ObjDef OBJ_DEFS[] = { {ObjDir::Minimize, 1.0f, 0.0f} };
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__device__ float compute_obj(int, const Sol& s) const { return decode_and_makespan(s); }
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__device__ float compute_penalty(const Sol&) const { return 0.0f; }
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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 = 1;
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cfg.dim2_default = num_jobs * num_ops;
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cfg.perm_repeat_count = num_ops;
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fill_obj_config(cfg);
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return cfg;
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}
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size_t shared_mem_bytes() const {
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int total = num_jobs * num_ops;
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return (size_t)total * (sizeof(int) + sizeof(float));
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}
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__device__ void load_shared(char* smem, int tid, int bsz) {
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int total = num_jobs * num_ops;
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int* sm = reinterpret_cast<int*>(smem);
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for (int i = tid; i < total; i += bsz) sm[i] = d_machine[i];
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d_machine = sm;
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float* sd = reinterpret_cast<float*>(sm + total);
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for (int i = tid; i < total; i += bsz) sd[i] = d_duration[i];
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d_duration = sd;
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}
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static JSPPermMedium create(const int* h_machine, const float* h_duration,
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int nj, int no, int nm) {
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JSPPermMedium p;
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p.num_jobs = nj; p.num_ops = no; p.num_machines = nm;
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int total = nj * no;
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int* dm; float* dd;
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CUDA_CHECK(cudaMalloc(&dm, sizeof(int) * total));
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CUDA_CHECK(cudaMalloc(&dd, sizeof(float) * total));
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CUDA_CHECK(cudaMemcpy(dm, h_machine, sizeof(int) * total, cudaMemcpyHostToDevice));
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CUDA_CHECK(cudaMemcpy(dd, h_duration, sizeof(float) * total, cudaMemcpyHostToDevice));
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p.d_machine = dm; p.d_duration = dd;
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return p;
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}
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void destroy() {
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if (d_machine) { cudaFree(const_cast<int*>(d_machine)); d_machine = nullptr; }
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if (d_duration) { cudaFree(const_cast<float*>(d_duration)); d_duration = nullptr; }
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}
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};
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// ============================================================
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// Knapsack Problem (D2=128, N<=128)
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// ============================================================
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struct KnapsackMedium : ProblemBase<KnapsackMedium, 1, 128> {
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const float* d_weights;
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const float* d_values;
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float capacity;
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int n;
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__device__ float calc_total_value(const Sol& s) const {
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float tv = 0.0f;
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int size = s.dim2_sizes[0];
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for (int i = 0; i < size; i++)
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if (s.data[0][i]) tv += d_values[i];
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return tv;
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}
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static constexpr ObjDef OBJ_DEFS[] = { {ObjDir::Maximize, 1.0f, 0.0f} };
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__device__ float compute_obj(int, const Sol& s) const { return calc_total_value(s); }
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__device__ float compute_penalty(const Sol& s) const {
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float tw = 0.0f;
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int size = s.dim2_sizes[0];
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for (int i = 0; i < size; i++)
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if (s.data[0][i]) tw += d_weights[i];
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float over = tw - capacity;
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return (over > 0.0f) ? over : 0.0f;
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}
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ProblemConfig config() const {
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ProblemConfig cfg;
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cfg.encoding = EncodingType::Binary;
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cfg.dim1 = 1; cfg.dim2_default = n;
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fill_obj_config(cfg);
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return cfg;
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}
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size_t shared_mem_bytes() const { return 2 * (size_t)n * sizeof(float); }
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__device__ void load_shared(char* smem, int tid, int bsz) {
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float* sw = reinterpret_cast<float*>(smem);
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float* sv = sw + n;
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for (int i = tid; i < n; i += bsz) { sw[i] = d_weights[i]; sv[i] = d_values[i]; }
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d_weights = sw; d_values = sv;
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}
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static KnapsackMedium create(const float* hw, const float* hv, int n, float cap) {
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KnapsackMedium p;
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p.n = n; p.capacity = cap;
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float *dw, *dv;
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CUDA_CHECK(cudaMalloc(&dw, sizeof(float) * n));
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CUDA_CHECK(cudaMalloc(&dv, sizeof(float) * n));
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CUDA_CHECK(cudaMemcpy(dw, hw, sizeof(float) * n, cudaMemcpyHostToDevice));
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CUDA_CHECK(cudaMemcpy(dv, hv, sizeof(float) * n, cudaMemcpyHostToDevice));
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p.d_weights = dw; p.d_values = dv;
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return p;
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}
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void destroy() {
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if (d_weights) cudaFree(const_cast<float*>(d_weights));
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if (d_values) cudaFree(const_cast<float*>(d_values));
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d_weights = nullptr; d_values = nullptr;
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}
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};
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// ============================================================
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// VRPTW Problem (D1=25, D2=128, N<=100 customers, <=25 vehicles)
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// ============================================================
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struct VRPTWMedium : ProblemBase<VRPTWMedium, 25, 128> {
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const float* d_dist;
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const float* d_demand;
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const float* d_earliest;
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const float* d_latest;
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const float* d_service;
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const float* h_dist;
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int n;
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int stride;
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float capacity;
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int num_vehicles;
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int max_vehicles;
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__device__ float compute_route_dist(const int* route, int size) const {
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if (size == 0) return 0.0f;
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float dist = 0.0f;
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int prev = 0;
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for (int j = 0; j < size; j++) {
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int node = route[j] + 1;
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dist += d_dist[prev * stride + node];
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prev = node;
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}
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dist += d_dist[prev * stride + 0];
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return dist;
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}
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__device__ float calc_total_distance(const Sol& sol) const {
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float total = 0.0f;
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for (int r = 0; r < num_vehicles; r++)
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total += compute_route_dist(sol.data[r], sol.dim2_sizes[r]);
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return total;
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}
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static constexpr ObjDef OBJ_DEFS[] = { {ObjDir::Minimize, 1.0f, 0.0f} };
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__device__ float compute_obj(int, const Sol& sol) const { return calc_total_distance(sol); }
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__device__ float compute_penalty(const Sol& sol) const {
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float penalty = 0.0f;
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int active = 0;
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for (int r = 0; r < num_vehicles; r++) {
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int size = sol.dim2_sizes[r];
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if (size == 0) continue;
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active++;
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float load = 0.0f;
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for (int j = 0; j < size; j++)
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load += d_demand[sol.data[r][j]];
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if (load > capacity)
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penalty += (load - capacity) * 100.0f;
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float time = 0.0f;
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int prev = 0;
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for (int j = 0; j < size; j++) {
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int node = sol.data[r][j] + 1;
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float travel = d_dist[prev * stride + node];
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time += travel;
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if (time < d_earliest[node])
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time = d_earliest[node];
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if (time > d_latest[node])
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penalty += (time - d_latest[node]) * 50.0f;
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time += d_service[node];
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prev = node;
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}
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float return_time = time + d_dist[prev * stride + 0];
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if (return_time > d_latest[0])
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penalty += (return_time - d_latest[0]) * 50.0f;
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}
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if (active > max_vehicles)
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penalty += (float)(active - max_vehicles) * 1000.0f;
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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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int heuristic_matrices(HeuristicMatrix* out, int max_count) const {
|
||
if (max_count < 1 || !h_dist) return 0;
|
||
out[0] = {h_dist, stride};
|
||
return 1;
|
||
}
|
||
|
||
size_t shared_mem_bytes() const {
|
||
size_t dist_bytes = (size_t)stride * stride * sizeof(float);
|
||
size_t aux_bytes = (size_t)(n + 1) * 4 * sizeof(float);
|
||
return dist_bytes + aux_bytes;
|
||
}
|
||
|
||
size_t working_set_bytes() const {
|
||
return (size_t)stride * stride * sizeof(float) + (size_t)(n + 1) * 4 * sizeof(float);
|
||
}
|
||
|
||
__device__ void load_shared(char* smem, int tid, int bsz) {
|
||
float* sd = reinterpret_cast<float*>(smem);
|
||
int dist_size = stride * stride;
|
||
for (int i = tid; i < dist_size; i += bsz) sd[i] = d_dist[i];
|
||
d_dist = sd;
|
||
|
||
float* sdem = sd + dist_size;
|
||
for (int i = tid; i < n; i += bsz) sdem[i] = d_demand[i];
|
||
d_demand = sdem;
|
||
|
||
float* se = sdem + n;
|
||
int nn = n + 1;
|
||
for (int i = tid; i < nn; i += bsz) se[i] = d_earliest[i];
|
||
d_earliest = se;
|
||
|
||
float* sl = se + nn;
|
||
for (int i = tid; i < nn; i += bsz) sl[i] = d_latest[i];
|
||
d_latest = sl;
|
||
|
||
float* ss = sl + nn;
|
||
for (int i = tid; i < nn; i += bsz) ss[i] = d_service[i];
|
||
d_service = ss;
|
||
}
|
||
|
||
static VRPTWMedium create(const SolomonData& sd) {
|
||
VRPTWMedium p;
|
||
p.n = sd.num_customers;
|
||
p.stride = sd.num_customers + 1;
|
||
p.capacity = sd.capacity;
|
||
p.num_vehicles = sd.num_vehicles;
|
||
p.max_vehicles = sd.num_vehicles;
|
||
p.h_dist = sd.dist.data();
|
||
|
||
int nn = p.stride;
|
||
float *dd, *ddem, *de, *dl, *ds;
|
||
CUDA_CHECK(cudaMalloc(&dd, sizeof(float) * nn * nn));
|
||
CUDA_CHECK(cudaMemcpy(dd, sd.dist.data(), sizeof(float) * nn * nn, cudaMemcpyHostToDevice));
|
||
p.d_dist = dd;
|
||
|
||
std::vector<float> demand(p.n), earliest(nn), latest(nn), service(nn);
|
||
for (int i = 0; i < p.n; i++)
|
||
demand[i] = sd.nodes[i + 1].demand;
|
||
for (int i = 0; i < nn; i++) {
|
||
earliest[i] = sd.nodes[i].ready;
|
||
latest[i] = sd.nodes[i].due;
|
||
service[i] = sd.nodes[i].service;
|
||
}
|
||
|
||
CUDA_CHECK(cudaMalloc(&ddem, sizeof(float) * p.n));
|
||
CUDA_CHECK(cudaMemcpy(ddem, demand.data(), sizeof(float) * p.n, cudaMemcpyHostToDevice));
|
||
p.d_demand = ddem;
|
||
|
||
CUDA_CHECK(cudaMalloc(&de, sizeof(float) * nn));
|
||
CUDA_CHECK(cudaMemcpy(de, earliest.data(), sizeof(float) * nn, cudaMemcpyHostToDevice));
|
||
p.d_earliest = de;
|
||
|
||
CUDA_CHECK(cudaMalloc(&dl, sizeof(float) * nn));
|
||
CUDA_CHECK(cudaMemcpy(dl, latest.data(), sizeof(float) * nn, cudaMemcpyHostToDevice));
|
||
p.d_latest = dl;
|
||
|
||
CUDA_CHECK(cudaMalloc(&ds, sizeof(float) * nn));
|
||
CUDA_CHECK(cudaMemcpy(ds, service.data(), sizeof(float) * nn, cudaMemcpyHostToDevice));
|
||
p.d_service = ds;
|
||
|
||
return p;
|
||
}
|
||
|
||
void destroy() {
|
||
if (d_dist) { cudaFree(const_cast<float*>(d_dist)); d_dist = nullptr; }
|
||
if (d_demand) { cudaFree(const_cast<float*>(d_demand)); d_demand = nullptr; }
|
||
if (d_earliest) { cudaFree(const_cast<float*>(d_earliest)); d_earliest = nullptr; }
|
||
if (d_latest) { cudaFree(const_cast<float*>(d_latest)); d_latest = nullptr; }
|
||
if (d_service) { cudaFree(const_cast<float*>(d_service)); d_service = nullptr; }
|
||
}
|
||
};
|
||
|
||
// ============================================================
|
||
// Main
|
||
// ============================================================
|
||
|
||
int main(int argc, char** argv) {
|
||
bench_init();
|
||
bench_csv_header();
|
||
|
||
const char* data_dir = "../../data";
|
||
if (argc > 1) data_dir = argv[1];
|
||
|
||
// ========================================================
|
||
// Part A: 固定代数 — 测量纯吞吐量 (gens/s)
|
||
// ========================================================
|
||
fprintf(stderr, "\n=== Part A: Fixed generations (gen=2000) ===\n");
|
||
{
|
||
const int GEN = 2000;
|
||
const int REPEATS = 3;
|
||
|
||
// TSP 实例
|
||
TSPInstance instances[] = {
|
||
{"eil51", eil51_coords, EIL51_N, 426.0f},
|
||
{"kroA100", kroA100_coords, KROA100_N, 21282.0f},
|
||
{"ch150", CH150_coords, CH150_N, 6528.0f},
|
||
};
|
||
for (auto& inst : instances) {
|
||
fprintf(stderr, " [e6-A] TSP %s (n=%d)\n", inst.name, inst.n);
|
||
float* dist = new float[inst.n * inst.n];
|
||
compute_euc2d_dist(dist, inst.coords, inst.n);
|
||
SolverConfig c = make_default_config(GEN);
|
||
bench_run_tsp<void>(inst.name, "A_gen2000", inst.n, dist, c, inst.optimal, REPEATS);
|
||
delete[] dist;
|
||
}
|
||
|
||
// CVRP10
|
||
{
|
||
fprintf(stderr, " [e6-A] CVRP10\n");
|
||
const int N = 10, NN = N + 1;
|
||
float coords[NN][2] = {
|
||
{50,50},{60,50},{70,50},{80,50},{50,60},{50,70},{50,80},{40,50},{30,50},{50,40},{50,30}
|
||
};
|
||
float demands[N] = {5,4,6,5,4,6,5,4,5,6};
|
||
float dist[NN * NN];
|
||
for (int i = 0; i < NN; i++)
|
||
for (int j = 0; j < NN; j++) {
|
||
float dx = coords[i][0] - coords[j][0];
|
||
float dy = coords[i][1] - coords[j][1];
|
||
dist[i * NN + j] = roundf(sqrtf(dx * dx + dy * dy));
|
||
}
|
||
auto p = VRPProblem::create(dist, demands, N, 15.0f, 4, 4);
|
||
SolverConfig c = make_default_config(GEN);
|
||
bench_run("CVRP10", "A_gen2000", p, c, 200.0f, REPEATS);
|
||
p.destroy();
|
||
}
|
||
|
||
// Schedule3x4
|
||
{
|
||
fprintf(stderr, " [e6-A] Schedule3x4\n");
|
||
float cost[12] = {5,3,8,4, 6,2,7,5, 4,6,3,7};
|
||
auto p = ScheduleProblem::create(cost, 3, 4, 2);
|
||
SolverConfig c = make_default_config(GEN);
|
||
bench_run("Schedule3x4", "A_gen2000", p, c, 0.0f, REPEATS);
|
||
p.destroy();
|
||
}
|
||
}
|
||
|
||
// ========================================================
|
||
// Part B: 固定时间 — 测量解质量 + gens/s
|
||
// ========================================================
|
||
fprintf(stderr, "\n=== Part B: Fixed time (30s) ===\n");
|
||
{
|
||
const float TIME = 30.0f;
|
||
|
||
// QAP tai15a (smem: 2*15*15*4 = 1.8KB, 完全在 shared memory 内)
|
||
{
|
||
char path[512];
|
||
snprintf(path, sizeof(path), "%s/qaplib/tai15a.dat", data_dir);
|
||
QAPData d = parse_qaplib(path);
|
||
fprintf(stderr, " [e6-B] QAP tai15a: N=%d, smem=%.1fKB\n",
|
||
d.n, 2.0f * d.n * d.n * 4 / 1024.0f);
|
||
auto p = QAPMedium::create(d.flow.data(), d.dist.data(), d.n);
|
||
SolverConfig c = make_timed_config(TIME);
|
||
bench_run("QAP_tai15a", "B_t30s", p, c, 388214.0f);
|
||
p.destroy();
|
||
}
|
||
|
||
// JSP ft10 (smem: 100*(4+4) = 800B)
|
||
{
|
||
char path[512];
|
||
snprintf(path, sizeof(path), "%s/jsp/ft10.txt", data_dir);
|
||
JSPData d = parse_jsp(path);
|
||
fprintf(stderr, " [e6-B] JSP ft10: %dx%d, smem=%.1fKB\n",
|
||
d.num_jobs, d.num_machines,
|
||
(float)(d.num_jobs * d.num_machines) * 8 / 1024.0f);
|
||
auto p = JSPPermMedium::create(d.machines.data(), d.durations.data(),
|
||
d.num_jobs, d.num_machines, d.num_machines);
|
||
SolverConfig c = make_timed_config(TIME);
|
||
bench_run("JSP_ft10", "B_t30s", p, c, 930.0f);
|
||
p.destroy();
|
||
}
|
||
|
||
// Knapsack100 (smem: 2*100*4 = 800B)
|
||
{
|
||
char path[512];
|
||
snprintf(path, sizeof(path), "%s/knapsack/knapPI_1_100.txt", data_dir);
|
||
KnapsackData d = parse_knapsack(path);
|
||
int opt = knapsack_dp_optimal(d);
|
||
fprintf(stderr, " [e6-B] Knapsack N=%d, smem=%.1fKB, DP opt=%d\n",
|
||
d.n, 2.0f * d.n * 4 / 1024.0f, opt);
|
||
auto p = KnapsackMedium::create(d.weights.data(), d.values.data(), d.n, d.capacity);
|
||
SolverConfig c = make_timed_config(TIME);
|
||
bench_run("Knapsack100", "B_t30s", p, c, (float)opt);
|
||
p.destroy();
|
||
}
|
||
|
||
// VRPTW R101 (smem: 101*101*4 + 101*4*4 = ~42KB → T4 溢出, V100 可能放得下)
|
||
{
|
||
char path[512];
|
||
snprintf(path, sizeof(path), "%s/solomon/R101.txt", data_dir);
|
||
SolomonData sd = parse_solomon(path);
|
||
size_t dist_bytes = (size_t)(sd.num_customers+1) * (sd.num_customers+1) * sizeof(float);
|
||
size_t aux_bytes = (size_t)(sd.num_customers+1) * 4 * sizeof(float);
|
||
fprintf(stderr, " [e6-B] VRPTW R101: N=%d, data=%.1fKB (dist=%.1fKB + aux=%.1fKB)\n",
|
||
sd.num_customers,
|
||
(dist_bytes + aux_bytes) / 1024.0f,
|
||
dist_bytes / 1024.0f, aux_bytes / 1024.0f);
|
||
auto p = VRPTWMedium::create(sd);
|
||
SolverConfig c = make_timed_config(TIME);
|
||
bench_run("VRPTW_R101", "B_t30s", p, c, 1637.7f);
|
||
p.destroy();
|
||
}
|
||
|
||
// VRPTW C101
|
||
{
|
||
char path[512];
|
||
snprintf(path, sizeof(path), "%s/solomon/C101.txt", data_dir);
|
||
SolomonData sd = parse_solomon(path);
|
||
fprintf(stderr, " [e6-B] VRPTW C101: N=%d\n", sd.num_customers);
|
||
auto p = VRPTWMedium::create(sd);
|
||
SolverConfig c = make_timed_config(TIME);
|
||
bench_run("VRPTW_C101", "B_t30s", p, c, 827.3f);
|
||
p.destroy();
|
||
}
|
||
|
||
// VRPTW RC101
|
||
{
|
||
char path[512];
|
||
snprintf(path, sizeof(path), "%s/solomon/RC101.txt", data_dir);
|
||
SolomonData sd = parse_solomon(path);
|
||
fprintf(stderr, " [e6-B] VRPTW RC101: N=%d\n", sd.num_customers);
|
||
auto p = VRPTWMedium::create(sd);
|
||
SolverConfig c = make_timed_config(TIME);
|
||
bench_run("VRPTW_RC101", "B_t30s", p, c, 1619.8f);
|
||
p.destroy();
|
||
}
|
||
}
|
||
|
||
fprintf(stderr, "\n[e6] GPU hardware comparison completed.\n");
|
||
return 0;
|
||
}
|