GPUKernelContest/S1/ICTN0N/sort_pair_algorithm.maca

#include "test_utils.h"
#include "performance_utils.h"
#include "yaml_reporter.h"
#include <iostream>
#include <vector>
#include <iomanip>

// ============================================================================
// 实现标记宏 - 参赛者修改实现时请将此宏设为0
// ============================================================================
#ifndef USE_DEFAULT_REF_IMPL
#define USE_DEFAULT_REF_IMPL 1  // 1=默认实现, 0=参赛者自定义实现
#endif

#if USE_DEFAULT_REF_IMPL
#include <thrust/sort.h>
#include <thrust/device_vector.h>
#include <thrust/execution_policy.h>
#include <thrust/iterator/zip_iterator.h>
#include <thrust/tuple.h>
#endif

// ============================================================================
// SortPair算法实现接口
// 参赛者需要替换Thrust实现为自己的高性能kernel
// ============================================================================

template <typename KeyType, typename ValueType>
class SortPairAlgorithm {
public:
    // 主要接口函数 - 参赛者需要实现这个函数
    void sort(const KeyType* d_keys_in, KeyType* d_keys_out,
              const ValueType* d_values_in, ValueType* d_values_out,
              int num_items, bool descending) {
        
#if !USE_DEFAULT_REF_IMPL
        // ========================================
        // 参赛者自定义实现区域
        // ========================================
        
        // TODO: 参赛者在此实现自己的高性能排序算法
        
        // 示例：参赛者可以调用1个或多个自定义kernel
        // preprocessKernel<<<grid, block>>>(d_keys_in, d_values_in, num_items);
        // mainSortKernel<<<grid, block>>>(d_keys_out, d_values_out, num_items, descending);
        // postprocessKernel<<<grid, block>>>(d_keys_out, d_values_out, num_items);
#else
        // ========================================
        // 默认基准实现
        // ========================================
        
        MACA_CHECK(mcMemcpy(d_keys_out, d_keys_in, num_items * sizeof(KeyType), mcMemcpyDeviceToDevice));
        MACA_CHECK(mcMemcpy(d_values_out, d_values_in, num_items * sizeof(ValueType), mcMemcpyDeviceToDevice));
        
        auto key_ptr = thrust::device_pointer_cast(d_keys_out);
        auto value_ptr = thrust::device_pointer_cast(d_values_out);
        
        if (descending) {
            thrust::stable_sort_by_key(thrust::device, key_ptr, key_ptr + num_items, value_ptr, thrust::greater<KeyType>());
        } else {
            thrust::stable_sort_by_key(thrust::device, key_ptr, key_ptr + num_items, value_ptr, thrust::less<KeyType>());
        }
#endif
    }
    
    // 获取当前实现状态
    static const char* getImplementationStatus() {
#if USE_DEFAULT_REF_IMPL
        return "DEFAULT_REF_IMPL";
#else
        return "CUSTOM_IMPL";
#endif
    }
    
private:
    // 参赛者可以在这里添加辅助函数和成员变量
    // 例如：临时缓冲区、多个kernel函数、流等
};

// ============================================================================
// 测试和性能评估
// ============================================================================

bool testCorrectness() {
    std::cout << "SortPair 正确性测试..." << std::endl;
    TestDataGenerator generator;
    SortPairAlgorithm<float, uint32_t> algorithm;
    
    // 测试小规模数据
    int size = 10000;
    auto keys = generator.generateRandomFloats(size);
    auto values = generator.generateRandomUint32(size);
    
    // 分配GPU内存
    float *d_keys_in, *d_keys_out;
    uint32_t *d_values_in, *d_values_out;
    
    MACA_CHECK(mcMalloc(&d_keys_in, size * sizeof(float)));
    MACA_CHECK(mcMalloc(&d_keys_out, size * sizeof(float)));
    MACA_CHECK(mcMalloc(&d_values_in, size * sizeof(uint32_t)));
    MACA_CHECK(mcMalloc(&d_values_out, size * sizeof(uint32_t)));
    
    MACA_CHECK(mcMemcpy(d_keys_in, keys.data(), size * sizeof(float), mcMemcpyHostToDevice));
    MACA_CHECK(mcMemcpy(d_values_in, values.data(), size * sizeof(uint32_t), mcMemcpyHostToDevice));
    
    // 测试升序和降序
    bool allPassed = true;
    for (bool descending : {false, true}) {
        std::cout << "  " << (descending ? "降序" : "升序") << " 测试..." << std::endl;
        
        // CPU参考结果
        auto cpu_keys = keys;
        auto cpu_values = values;
        cpuSortPair(cpu_keys, cpu_values, descending);
        
        // GPU算法结果
        algorithm.sort(d_keys_in, d_keys_out, d_values_in, d_values_out, size, descending);
        
        // 获取结果
        std::vector<float> gpu_keys(size);
        std::vector<uint32_t> gpu_values(size);
        MACA_CHECK(mcMemcpy(gpu_keys.data(), d_keys_out, size * sizeof(float), mcMemcpyDeviceToHost));
        MACA_CHECK(mcMemcpy(gpu_values.data(), d_values_out, size * sizeof(uint32_t), mcMemcpyDeviceToHost));
        
        // 验证结果
        bool keysMatch = compareArrays(cpu_keys, gpu_keys, 1e-5);
        bool valuesMatch = compareArrays(cpu_values, gpu_values);
        
        if (!keysMatch || !valuesMatch) {
            std::cout << "    失败: 结果不匹配" << std::endl;
            allPassed = false;
        } else {
            std::cout << "    通过" << std::endl;
        }
    }
    
    // 清理内存
    mcFree(d_keys_in);
    mcFree(d_keys_out);
    mcFree(d_values_in);
    mcFree(d_values_out);
    
    return allPassed;
}

void benchmarkPerformance() {
    PerformanceDisplay::printSortPairHeader();
    
    TestDataGenerator generator;
    PerformanceMeter meter;
    SortPairAlgorithm<float, uint32_t> algorithm;
    
    const int WARMUP_ITERATIONS = 5;
    const int BENCHMARK_ITERATIONS = 10;
    
    // 用于YAML报告的数据收集
    std::vector<std::map<std::string, std::string>> perf_data;
    
    for (int i = 0; i < NUM_TEST_SIZES; i++) {
        int size = TEST_SIZES[i];
        
        // 生成测试数据
        auto keys = generator.generateRandomFloats(size);
        auto values = generator.generateRandomUint32(size);
        
        // 分配GPU内存
        float *d_keys_in, *d_keys_out;
        uint32_t *d_values_in, *d_values_out;
        
        MACA_CHECK(mcMalloc(&d_keys_in, size * sizeof(float)));
        MACA_CHECK(mcMalloc(&d_keys_out, size * sizeof(float)));
        MACA_CHECK(mcMalloc(&d_values_in, size * sizeof(uint32_t)));
        MACA_CHECK(mcMalloc(&d_values_out, size * sizeof(uint32_t)));
        
        MACA_CHECK(mcMemcpy(d_keys_in, keys.data(), size * sizeof(float), mcMemcpyHostToDevice));
        MACA_CHECK(mcMemcpy(d_values_in, values.data(), size * sizeof(uint32_t), mcMemcpyHostToDevice));
        
        float asc_time = 0, desc_time = 0;
        
        // 测试升序和降序
        for (bool descending : {false, true}) {
            // Warmup阶段
            for (int iter = 0; iter < WARMUP_ITERATIONS; iter++) {
                algorithm.sort(d_keys_in, d_keys_out, d_values_in, d_values_out, size, descending);
            }
            
            // 正式测试阶段
            float total_time = 0;
            for (int iter = 0; iter < BENCHMARK_ITERATIONS; iter++) {
                meter.startTiming();
                algorithm.sort(d_keys_in, d_keys_out, d_values_in, d_values_out, size, descending);
                total_time += meter.stopTiming();
            }
            
            float avg_time = total_time / BENCHMARK_ITERATIONS;
            if (descending) {
                desc_time = avg_time;
            } else {
                asc_time = avg_time;
            }
        }
        
        // 计算性能指标
        auto asc_metrics = PerformanceCalculator::calculateSortPair(size, asc_time);
        auto desc_metrics = PerformanceCalculator::calculateSortPair(size, desc_time);
        
        // 显示性能数据
        PerformanceDisplay::printSortPairData(size, asc_time, desc_time, asc_metrics, desc_metrics);
        
        // 收集YAML报告数据
        auto entry = YAMLPerformanceReporter::createEntry();
        entry["data_size"] = std::to_string(size);
        entry["asc_time_ms"] = std::to_string(asc_time);
        entry["desc_time_ms"] = std::to_string(desc_time);
        entry["asc_throughput_gps"] = std::to_string(asc_metrics.throughput_gps);
        entry["desc_throughput_gps"] = std::to_string(desc_metrics.throughput_gps);
        entry["key_type"] = "float";
        entry["value_type"] = "uint32_t";
        perf_data.push_back(entry);
        
        // 清理内存
        mcFree(d_keys_in);
        mcFree(d_keys_out);
        mcFree(d_values_in);
        mcFree(d_values_out);
    }
    
    // 生成YAML性能报告
    YAMLPerformanceReporter::generateSortPairYAML(perf_data, "sort_pair_performance.yaml");
    PerformanceDisplay::printSavedMessage("sort_pair_performance.yaml");
}

// ============================================================================
// 主函数
// ============================================================================
int main(int argc, char* argv[]) {
    std::cout << "=== SortPair 算法测试 ===" << std::endl;
    
    // 检查参数
    std::string mode = "all";
    if (argc > 1) {
        mode = argv[1];
    }
    
    bool correctness_passed = true;
    bool performance_completed = true;
    
    try {
        if (mode == "correctness" || mode == "all") {
            correctness_passed = testCorrectness();
        }
        
        if (mode == "performance" || mode == "all") {
            if (correctness_passed || mode == "performance") {
                benchmarkPerformance();
            } else {
                std::cout << "跳过性能测试，因为正确性测试未通过" << std::endl;
                performance_completed = false;
            }
        }
        
        std::cout << "\n=== 测试完成 ===" << std::endl;
        std::cout << "实现状态: " << SortPairAlgorithm<float, uint32_t>::getImplementationStatus() << std::endl;
        if (mode == "all") {
            std::cout << "正确性: " << (correctness_passed ? "通过" : "失败") << std::endl;
            std::cout << "性能测试: " << (performance_completed ? "完成" : "跳过") << std::endl;
        }
        
        return correctness_passed ? 0 : 1;
        
    } catch (const std::exception& e) {
        std::cerr << "测试出错: " << e.what() << std::endl;
        return 1;
    }
}