275 lines
10 KiB
Plaintext
Executable File
275 lines
10 KiB
Plaintext
Executable File
#include "test_utils.h"
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#include "performance_utils.h"
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#include "yaml_reporter.h"
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#include <iostream>
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#include <vector>
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#include <iomanip>
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// ============================================================================
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// 实现标记宏 - 参赛者修改实现时请将此宏设为0
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// ============================================================================
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#ifndef USE_DEFAULT_REF_IMPL
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#define USE_DEFAULT_REF_IMPL 1 // 1=默认实现, 0=参赛者自定义实现
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#endif
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#if USE_DEFAULT_REF_IMPL
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#include <thrust/sort.h>
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#include <thrust/device_vector.h>
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#include <thrust/execution_policy.h>
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#include <thrust/iterator/zip_iterator.h>
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#include <thrust/tuple.h>
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#endif
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// ============================================================================
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// SortPair算法实现接口
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// 参赛者需要替换Thrust实现为自己的高性能kernel
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// ============================================================================
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template <typename KeyType, typename ValueType>
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class SortPairAlgorithm {
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public:
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// 主要接口函数 - 参赛者需要实现这个函数
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void sort(const KeyType* d_keys_in, KeyType* d_keys_out,
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const ValueType* d_values_in, ValueType* d_values_out,
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int num_items, bool descending) {
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#if !USE_DEFAULT_REF_IMPL
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// ========================================
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// 参赛者自定义实现区域
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// ========================================
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// TODO: 参赛者在此实现自己的高性能排序算法
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// 示例：参赛者可以调用1个或多个自定义kernel
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// preprocessKernel<<<grid, block>>>(d_keys_in, d_values_in, num_items);
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// mainSortKernel<<<grid, block>>>(d_keys_out, d_values_out, num_items, descending);
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// postprocessKernel<<<grid, block>>>(d_keys_out, d_values_out, num_items);
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#else
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// ========================================
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// 默认基准实现
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// ========================================
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MACA_CHECK(mcMemcpy(d_keys_out, d_keys_in, num_items * sizeof(KeyType), mcMemcpyDeviceToDevice));
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MACA_CHECK(mcMemcpy(d_values_out, d_values_in, num_items * sizeof(ValueType), mcMemcpyDeviceToDevice));
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auto key_ptr = thrust::device_pointer_cast(d_keys_out);
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auto value_ptr = thrust::device_pointer_cast(d_values_out);
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if (descending) {
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thrust::stable_sort_by_key(thrust::device, key_ptr, key_ptr + num_items, value_ptr, thrust::greater<KeyType>());
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} else {
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thrust::stable_sort_by_key(thrust::device, key_ptr, key_ptr + num_items, value_ptr, thrust::less<KeyType>());
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}
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#endif
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}
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// 获取当前实现状态
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static const char* getImplementationStatus() {
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#if USE_DEFAULT_REF_IMPL
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return "DEFAULT_REF_IMPL";
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#else
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return "CUSTOM_IMPL";
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#endif
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}
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private:
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// 参赛者可以在这里添加辅助函数和成员变量
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// 例如：临时缓冲区、多个kernel函数、流等
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};
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// ============================================================================
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// 测试和性能评估
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// ============================================================================
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bool testCorrectness() {
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std::cout << "SortPair 正确性测试..." << std::endl;
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TestDataGenerator generator;
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SortPairAlgorithm<float, uint32_t> algorithm;
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// 测试小规模数据
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int size = 10000;
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auto keys = generator.generateRandomFloats(size);
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auto values = generator.generateRandomUint32(size);
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// 分配GPU内存
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float *d_keys_in, *d_keys_out;
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uint32_t *d_values_in, *d_values_out;
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MACA_CHECK(mcMalloc(&d_keys_in, size * sizeof(float)));
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MACA_CHECK(mcMalloc(&d_keys_out, size * sizeof(float)));
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MACA_CHECK(mcMalloc(&d_values_in, size * sizeof(uint32_t)));
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MACA_CHECK(mcMalloc(&d_values_out, size * sizeof(uint32_t)));
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MACA_CHECK(mcMemcpy(d_keys_in, keys.data(), size * sizeof(float), mcMemcpyHostToDevice));
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MACA_CHECK(mcMemcpy(d_values_in, values.data(), size * sizeof(uint32_t), mcMemcpyHostToDevice));
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// 测试升序和降序
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bool allPassed = true;
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for (bool descending : {false, true}) {
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std::cout << " " << (descending ? "降序" : "升序") << " 测试..." << std::endl;
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// CPU参考结果
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auto cpu_keys = keys;
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auto cpu_values = values;
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cpuSortPair(cpu_keys, cpu_values, descending);
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// GPU算法结果
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algorithm.sort(d_keys_in, d_keys_out, d_values_in, d_values_out, size, descending);
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// 获取结果
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std::vector<float> gpu_keys(size);
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std::vector<uint32_t> gpu_values(size);
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MACA_CHECK(mcMemcpy(gpu_keys.data(), d_keys_out, size * sizeof(float), mcMemcpyDeviceToHost));
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MACA_CHECK(mcMemcpy(gpu_values.data(), d_values_out, size * sizeof(uint32_t), mcMemcpyDeviceToHost));
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// 验证结果
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bool keysMatch = compareArrays(cpu_keys, gpu_keys, 1e-5);
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bool valuesMatch = compareArrays(cpu_values, gpu_values);
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if (!keysMatch || !valuesMatch) {
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std::cout << " 失败: 结果不匹配" << std::endl;
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allPassed = false;
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} else {
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std::cout << " 通过" << std::endl;
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}
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}
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// 清理内存
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mcFree(d_keys_in);
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mcFree(d_keys_out);
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mcFree(d_values_in);
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mcFree(d_values_out);
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return allPassed;
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}
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void benchmarkPerformance() {
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PerformanceDisplay::printSortPairHeader();
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TestDataGenerator generator;
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PerformanceMeter meter;
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SortPairAlgorithm<float, uint32_t> algorithm;
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const int WARMUP_ITERATIONS = 5;
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const int BENCHMARK_ITERATIONS = 10;
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// 用于YAML报告的数据收集
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std::vector<std::map<std::string, std::string>> perf_data;
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for (int i = 0; i < NUM_TEST_SIZES; i++) {
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int size = TEST_SIZES[i];
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// 生成测试数据
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auto keys = generator.generateRandomFloats(size);
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auto values = generator.generateRandomUint32(size);
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// 分配GPU内存
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float *d_keys_in, *d_keys_out;
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uint32_t *d_values_in, *d_values_out;
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MACA_CHECK(mcMalloc(&d_keys_in, size * sizeof(float)));
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MACA_CHECK(mcMalloc(&d_keys_out, size * sizeof(float)));
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MACA_CHECK(mcMalloc(&d_values_in, size * sizeof(uint32_t)));
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MACA_CHECK(mcMalloc(&d_values_out, size * sizeof(uint32_t)));
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MACA_CHECK(mcMemcpy(d_keys_in, keys.data(), size * sizeof(float), mcMemcpyHostToDevice));
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MACA_CHECK(mcMemcpy(d_values_in, values.data(), size * sizeof(uint32_t), mcMemcpyHostToDevice));
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float asc_time = 0, desc_time = 0;
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// 测试升序和降序
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for (bool descending : {false, true}) {
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// Warmup阶段
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for (int iter = 0; iter < WARMUP_ITERATIONS; iter++) {
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algorithm.sort(d_keys_in, d_keys_out, d_values_in, d_values_out, size, descending);
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}
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// 正式测试阶段
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float total_time = 0;
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for (int iter = 0; iter < BENCHMARK_ITERATIONS; iter++) {
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meter.startTiming();
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algorithm.sort(d_keys_in, d_keys_out, d_values_in, d_values_out, size, descending);
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total_time += meter.stopTiming();
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}
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float avg_time = total_time / BENCHMARK_ITERATIONS;
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if (descending) {
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desc_time = avg_time;
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} else {
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asc_time = avg_time;
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}
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}
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// 计算性能指标
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auto asc_metrics = PerformanceCalculator::calculateSortPair(size, asc_time);
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auto desc_metrics = PerformanceCalculator::calculateSortPair(size, desc_time);
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// 显示性能数据
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PerformanceDisplay::printSortPairData(size, asc_time, desc_time, asc_metrics, desc_metrics);
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// 收集YAML报告数据
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auto entry = YAMLPerformanceReporter::createEntry();
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entry["data_size"] = std::to_string(size);
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entry["asc_time_ms"] = std::to_string(asc_time);
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entry["desc_time_ms"] = std::to_string(desc_time);
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entry["asc_throughput_gps"] = std::to_string(asc_metrics.throughput_gps);
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entry["desc_throughput_gps"] = std::to_string(desc_metrics.throughput_gps);
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entry["key_type"] = "float";
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entry["value_type"] = "uint32_t";
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perf_data.push_back(entry);
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// 清理内存
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mcFree(d_keys_in);
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mcFree(d_keys_out);
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mcFree(d_values_in);
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mcFree(d_values_out);
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}
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// 生成YAML性能报告
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YAMLPerformanceReporter::generateSortPairYAML(perf_data, "sort_pair_performance.yaml");
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PerformanceDisplay::printSavedMessage("sort_pair_performance.yaml");
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}
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// ============================================================================
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// 主函数
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// ============================================================================
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int main(int argc, char* argv[]) {
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std::cout << "=== SortPair 算法测试 ===" << std::endl;
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// 检查参数
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std::string mode = "all";
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if (argc > 1) {
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mode = argv[1];
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}
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bool correctness_passed = true;
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bool performance_completed = true;
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try {
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if (mode == "correctness" || mode == "all") {
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correctness_passed = testCorrectness();
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}
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if (mode == "performance" || mode == "all") {
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if (correctness_passed || mode == "performance") {
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benchmarkPerformance();
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} else {
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std::cout << "跳过性能测试，因为正确性测试未通过" << std::endl;
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performance_completed = false;
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}
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}
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std::cout << "\n=== 测试完成 ===" << std::endl;
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std::cout << "实现状态: " << SortPairAlgorithm<float, uint32_t>::getImplementationStatus() << std::endl;
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if (mode == "all") {
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std::cout << "正确性: " << (correctness_passed ? "通过" : "失败") << std::endl;
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std::cout << "性能测试: " << (performance_completed ? "完成" : "跳过") << std::endl;
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}
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return correctness_passed ? 0 : 1;
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} catch (const std::exception& e) {
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std::cerr << "测试出错: " << e.what() << std::endl;
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return 1;
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}
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} |