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feat(multi-gpu): implement inject distribution modes (OneIsland/HalfIslands/AllIslands)
inject_check_kernel now respects MultiGpuInjectMode from SolverConfig instead of hardcoding OneIsland. HalfIslands uses LCG-based random island selection. Also fixes stale write_async calls in test_multi_gpu_b3.cu. Verified on 2×V100S: all 5 B3 tests pass, e5 (12 problem types) all optimal.
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2 changed files with 359 additions and 23 deletions
325
benchmark/experiments/e9_multi_gpu_b3/test_multi_gpu_b3.cu
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325
benchmark/experiments/e9_multi_gpu_b3/test_multi_gpu_b3.cu
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/**
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* test_multi_gpu_b3.cu - Functional tests for option B3 (passive injection)
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*
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* Coverage:
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* 1. InjectBuffer basics: allocate, write, read, destroy
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* 2. inject_check_kernel correctness: check and inject solutions correctly
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* 3. Coordinator thread: periodically collect and inject global_best
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* 4. End-to-end: full runs with 2 and 4 GPUs
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* 5. Performance: benefit of B3 vs simplified v5.0
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*/
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#include "core/multi_gpu_solver.cuh"
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#include "problems/tsp.cuh"
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#include "problems/vrp.cuh"
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#include <cstdio>
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#include <cmath>
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// ============================================================
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// Helpers: generate test data
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// ============================================================
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void generate_random_tsp(float* dist, int n, unsigned seed = 42) {
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srand(seed);
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for (int i = 0; i < n; i++) {
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dist[i * n + i] = 0.0f;
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for (int j = i + 1; j < n; j++) {
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float d = 10.0f + (rand() % 1000) / 10.0f;
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dist[i * n + j] = d;
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dist[j * n + i] = d;
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}
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}
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}
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void generate_random_vrp(float* dist, float* demand, int n, unsigned seed = 42) {
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srand(seed);
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int stride = n + 1;
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for (int i = 0; i < stride; i++) {
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dist[i * stride + i] = 0.0f;
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for (int j = i + 1; j < stride; j++) {
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float d = 10.0f + (rand() % 1000) / 10.0f;
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dist[i * stride + j] = d;
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dist[j * stride + i] = d;
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}
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}
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for (int i = 0; i < n; i++) {
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demand[i] = 5.0f + (rand() % 20);
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}
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}
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// ============================================================
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// Test 1: InjectBuffer basics
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// ============================================================
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void test_inject_buffer() {
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printf("\n=== Test 1: InjectBuffer Basic Functionality ===\n");
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using Sol = Solution<1, 32>;
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// Allocate InjectBuffer
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auto buf = InjectBuffer<Sol>::allocate(0);
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// Build a test solution
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Sol test_sol;
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test_sol.dim2_sizes[0] = 5;
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for (int i = 0; i < 5; i++) test_sol.data[0][i] = i + 10;
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test_sol.objectives[0] = 123.45f;
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test_sol.penalty = 0.0f;
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buf.write_sync(test_sol, 0);
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// Read flag (expect 1)
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int flag;
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cudaMemcpy(&flag, buf.d_flag, sizeof(int), cudaMemcpyDeviceToHost);
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printf(" Flag after write: %d (expected 1)\n", flag);
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// Read solution back
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Sol read_sol;
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cudaMemcpy(&read_sol, buf.d_solution, sizeof(Sol), cudaMemcpyDeviceToHost);
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printf(" Read solution: obj=%.2f, penalty=%.2f, data[0][0]=%d\n",
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read_sol.objectives[0], read_sol.penalty, read_sol.data[0][0]);
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// Verify data consistency
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bool ok = (fabs(read_sol.objectives[0] - 123.45f) < 1e-3) &&
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(read_sol.data[0][0] == 10) &&
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(flag == 1);
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printf(" Result: %s\n", ok ? "PASS" : "FAIL");
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// Cleanup
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buf.destroy();
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}
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// ============================================================
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// Test 2: inject_check_kernel correctness
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// ============================================================
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void test_inject_check_kernel() {
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printf("\n=== Test 2: inject_check_kernel Correctness ===\n");
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using Sol = Solution<1, 32>;
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const int pop_size = 64;
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const int island_size = 16;
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// Allocate population
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Sol* d_pop;
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cudaMalloc(&d_pop, sizeof(Sol) * pop_size);
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// Init population (all solutions obj=100.0)
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Sol* h_pop = new Sol[pop_size];
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for (int i = 0; i < pop_size; i++) {
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h_pop[i].objectives[0] = 100.0f;
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h_pop[i].penalty = 0.0f;
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}
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cudaMemcpy(d_pop, h_pop, sizeof(Sol) * pop_size, cudaMemcpyHostToDevice);
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// Create InjectBuffer and write a better solution (obj=50.0)
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auto buf = InjectBuffer<Sol>::allocate(0);
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Sol inject_sol;
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inject_sol.objectives[0] = 50.0f;
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inject_sol.penalty = 0.0f;
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buf.write_sync(inject_sol, 0);
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// Copy InjectBuffer struct to device
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InjectBuffer<Sol>* d_buf;
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cudaMalloc(&d_buf, sizeof(InjectBuffer<Sol>));
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cudaMemcpy(d_buf, &buf, sizeof(InjectBuffer<Sol>), cudaMemcpyHostToDevice);
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// Build ObjConfig
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ObjConfig oc;
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oc.num_obj = 1;
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oc.mode = CompareMode::Weighted;
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oc.dirs[0] = ObjDir::Minimize;
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oc.weights[0] = 1.0f;
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// Launch inject_check_kernel (OneIsland mode for this unit test)
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inject_check_kernel<<<1, 1>>>(d_pop, pop_size, island_size, d_buf, oc,
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MultiGpuInjectMode::OneIsland);
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cudaDeviceSynchronize();
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// Read population; check whether first island worst was replaced
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cudaMemcpy(h_pop, d_pop, sizeof(Sol) * pop_size, cudaMemcpyDeviceToHost);
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int replaced_count = 0;
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for (int i = 0; i < island_size; i++) {
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if (fabs(h_pop[i].objectives[0] - 50.0f) < 1e-3) {
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replaced_count++;
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}
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}
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printf(" Replaced count in first island: %d (expected 1)\n", replaced_count);
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// Check flag was cleared
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int flag;
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cudaMemcpy(&flag, buf.d_flag, sizeof(int), cudaMemcpyDeviceToHost);
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printf(" Flag after inject_check: %d (expected 0)\n", flag);
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bool ok = (replaced_count == 1) && (flag == 0);
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printf(" Result: %s\n", ok ? "PASS" : "FAIL");
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// Cleanup
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buf.destroy();
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cudaFree(d_buf);
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cudaFree(d_pop);
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delete[] h_pop;
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}
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// ============================================================
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// Test 3: 2-GPU end-to-end (small scale)
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// ============================================================
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void test_2gpu_tsp_small() {
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printf("\n=== Test 3: 2 GPU TSP (n=30) ===\n");
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int device_count;
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cudaGetDeviceCount(&device_count);
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if (device_count < 2) {
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printf(" SKIP: Need at least 2 GPUs\n");
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return;
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}
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const int n = 30;
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float* h_dist = new float[n * n];
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generate_random_tsp(h_dist, n, 12345);
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auto prob = TSPProblem::create(h_dist, n);
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SolverConfig cfg;
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cfg.pop_size = 256;
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cfg.max_gen = 2000;
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cfg.verbose = true;
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cfg.seed = 42;
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cfg.num_islands = 4;
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cfg.use_aos = true;
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cfg.sa_temp_init = 10.0f;
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cfg.use_cuda_graph = true;
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// Option B3: 2 GPUs with exchange
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cfg.num_gpus = 2;
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cfg.multi_gpu_interval_sec = 2.0f; // exchange every 2 seconds
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cfg.multi_gpu_inject_mode = MultiGpuInjectMode::OneIsland;
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auto result = solve_multi_gpu(prob, cfg);
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printf(" Result: obj=%.2f, penalty=%.2f\n",
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result.best_solution.objectives[0],
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result.best_solution.penalty);
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delete[] h_dist;
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}
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// ============================================================
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// Test 4: VRP with 2 GPUs (medium scale)
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// ============================================================
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void test_2gpu_vrp_medium() {
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printf("\n=== Test 4: 2 GPU VRP (n=50) ===\n");
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int device_count;
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cudaGetDeviceCount(&device_count);
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if (device_count < 2) {
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printf(" SKIP: Need at least 2 GPUs (have %d)\n", device_count);
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return;
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}
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const int n = 50;
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float* h_dist = new float[(n+1) * (n+1)];
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float* h_demand = new float[n];
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generate_random_vrp(h_dist, h_demand, n, 23456);
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auto prob = VRPProblem::create(h_dist, h_demand, n, 150.0f, 8, 16);
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SolverConfig cfg;
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cfg.pop_size = 512;
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cfg.max_gen = 3000;
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cfg.verbose = true;
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cfg.seed = 42;
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cfg.num_islands = 8;
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cfg.use_aos = true;
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cfg.sa_temp_init = 15.0f;
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cfg.use_cuda_graph = true;
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// Option B3: 2 GPUs with exchange
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cfg.num_gpus = 2;
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cfg.multi_gpu_interval_sec = 3.0f; // exchange every 3 seconds
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cfg.multi_gpu_inject_mode = MultiGpuInjectMode::HalfIslands;
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auto result = solve_multi_gpu(prob, cfg);
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printf(" Result: obj=%.2f, penalty=%.2f\n",
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result.best_solution.objectives[0],
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result.best_solution.penalty);
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delete[] h_dist;
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delete[] h_demand;
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}
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// ============================================================
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// Test 5: Performance comparison (B3 vs simplified)
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// ============================================================
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void test_performance_comparison() {
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printf("\n=== Test 5: Performance Comparison (B3 vs Simplified) ===\n");
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int device_count;
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cudaGetDeviceCount(&device_count);
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if (device_count < 2) {
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printf(" SKIP: Need at least 2 GPUs\n");
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return;
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}
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const int n = 40;
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float* h_dist = new float[n * n];
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generate_random_tsp(h_dist, n, 34567);
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auto prob = TSPProblem::create(h_dist, n);
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SolverConfig cfg;
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cfg.pop_size = 512;
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cfg.max_gen = 5000;
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cfg.verbose = false;
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cfg.seed = 42;
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cfg.num_islands = 8;
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cfg.use_aos = true;
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cfg.sa_temp_init = 20.0f;
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cfg.use_cuda_graph = true;
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// Multiple runs for averaging
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const int num_runs = 5;
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printf("\n Running %d times with 2 GPUs...\n", num_runs);
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// Option B3: with exchange
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float b3_sum = 0.0f;
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cfg.num_gpus = 2;
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cfg.multi_gpu_interval_sec = 2.0f;
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for (int run = 0; run < num_runs; run++) {
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cfg.seed = 42 + run * 100;
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auto result = solve_multi_gpu(prob, cfg);
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b3_sum += result.best_solution.objectives[0];
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printf(" Run %d: obj=%.2f\n", run+1, result.best_solution.objectives[0]);
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}
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float b3_avg = b3_sum / num_runs;
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printf("\n B3 Average: %.2f\n", b3_avg);
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delete[] h_dist;
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}
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// ============================================================
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// Main
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// ============================================================
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int main() {
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printf("Multi-GPU B3 (Passive Injection) Test Suite\n");
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printf("============================================\n");
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test_inject_buffer();
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test_inject_check_kernel();
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test_2gpu_tsp_small();
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test_2gpu_vrp_medium();
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test_performance_comparison();
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printf("\n=== All Tests Completed ===\n");
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return 0;
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}
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@ -528,42 +528,52 @@ __global__ void inject_to_islands_kernel(Sol* pop, int pop_size, int island_size
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// ============================================================
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// v5.0 plan B3: inject_check_kernel — passive injection check
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// ============================================================
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// During migrate, GPU checks InjectBuffer; if new solution exists, inject at worst of first island
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// atomicExch reads and clears flag atomically for thread safety
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// During migrate, GPU checks InjectBuffer; if new solution exists, inject into
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// target islands based on MultiGpuInjectMode.
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//
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// Design notes:
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// 1. Single thread (thread 0 of block 0) to avoid races
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// 1. Single thread (thread 0 of block 0) — serial over target islands (count is small)
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// 2. atomicExch reads flag and clears it so each solution is handled once
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// 3. Inject only into first island (OneIsland strategy) to preserve diversity
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// 3. Inject mode: OneIsland (island 0), HalfIslands (random half), AllIslands (all)
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// 4. Optional: if inject_buf is nullptr, skip (single-GPU unaffected)
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template<typename Sol>
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__global__ void inject_check_kernel(Sol* pop, int pop_size, int island_size,
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InjectBuffer<Sol>* inject_buf, ObjConfig oc) {
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// Single-thread execution
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InjectBuffer<Sol>* inject_buf, ObjConfig oc,
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MultiGpuInjectMode mode) {
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if (threadIdx.x != 0 || blockIdx.x != 0) return;
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// No injection buffer — return (single-GPU case)
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if (inject_buf == nullptr) return;
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// Atomically read and clear flag (each solution processed once)
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int flag = atomicExch(inject_buf->d_flag, 0);
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// No new solution — return
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if (flag != 1) return;
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// Read injected solution
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Sol inject_sol = *(inject_buf->d_solution);
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// Find worst slot on first island
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int num_islands = pop_size / island_size;
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if (num_islands == 0) return;
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int worst = find_worst_in_island(pop, 0, island_size, oc);
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// Replace if injection is better
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if (is_better(inject_sol, pop[worst], oc)) {
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pop[worst] = inject_sol;
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if (mode == MultiGpuInjectMode::OneIsland) {
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int worst = find_worst_in_island(pop, 0, island_size, oc);
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if (is_better(inject_sol, pop[worst], oc))
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pop[worst] = inject_sol;
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} else if (mode == MultiGpuInjectMode::AllIslands) {
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for (int i = 0; i < num_islands; i++) {
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int worst = find_worst_in_island(pop, i * island_size, island_size, oc);
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if (is_better(inject_sol, pop[worst], oc))
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pop[worst] = inject_sol;
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}
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} else { // HalfIslands — randomly select num_islands/2 islands
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int half = (num_islands + 1) / 2;
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unsigned seed = (unsigned)clock();
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for (int count = 0; count < half; count++) {
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seed = seed * 1664525u + 1013904223u; // LCG
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int isle = (int)(seed % (unsigned)num_islands);
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int worst = find_worst_in_island(pop, isle * island_size, island_size, oc);
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if (is_better(inject_sol, pop[worst], oc))
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pop[worst] = inject_sol;
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}
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}
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}
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@ -1247,7 +1257,8 @@ SolveResult<typename Problem::Sol> solve(Problem& prob, const SolverConfig& cfg,
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// Must be outside Graph: inject_buf content changes dynamically
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if (inject_buf != nullptr && use_islands) {
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inject_check_kernel<<<1, 1>>>(pop.d_solutions, pop_size,
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island_size, inject_buf, oc);
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island_size, inject_buf, oc,
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cfg.multi_gpu_inject_mode);
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CUDA_CHECK(cudaDeviceSynchronize());
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}
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