277 lines
9.7 KiB
Plaintext
Executable File
277 lines
9.7 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/reduce.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/functional.h>
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#endif
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// 误差容忍度
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constexpr double REDUCE_ERROR_TOLERANCE = 0.005; // 0.5%
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// ============================================================================
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// ReduceSum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 InputT = float, typename OutputT = float>
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class ReduceSumAlgorithm {
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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 reduce(const InputT* d_in, OutputT* d_out, int num_items, OutputT init_value) {
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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归约算法
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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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// blockReduceKernel<<<grid, block>>>(d_in, temp_results, num_items, init_value);
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// finalReduceKernel<<<1, block>>>(temp_results, d_out, grid.x);
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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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auto input_ptr = thrust::device_pointer_cast(d_in);
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auto output_ptr = thrust::device_pointer_cast(d_out);
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// 直接使用thrust::reduce进行归约
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*output_ptr = thrust::reduce(
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thrust::device,
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input_ptr,
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input_ptr + num_items,
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static_cast<OutputT>(init_value)
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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：中间结果缓冲区、多阶段归约等
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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 << "ReduceSum 正确性测试..." << std::endl;
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TestDataGenerator generator;
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ReduceSumAlgorithm<float, float> algorithm;
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bool allPassed = true;
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// 测试不同数据规模
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for (int i = 0; i < NUM_TEST_SIZES && i < 2; i++) { // 限制测试规模
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int size = std::min(TEST_SIZES[i], 10000);
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std::cout << " 测试规模: " << size << std::endl;
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// 测试普通数据
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{
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auto data = generator.generateRandomFloats(size, -10.0f, 10.0f);
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float init_value = 1.0f;
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// CPU参考计算
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double cpu_result = cpuReduceSum(data, static_cast<double>(init_value));
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// GPU计算
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float *d_in;
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float *d_out;
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MACA_CHECK(mcMalloc(&d_in, size * sizeof(float)));
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MACA_CHECK(mcMalloc(&d_out, sizeof(float)));
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MACA_CHECK(mcMemcpy(d_in, data.data(), size * sizeof(float), mcMemcpyHostToDevice));
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algorithm.reduce(d_in, d_out, size, init_value);
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float gpu_result;
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MACA_CHECK(mcMemcpy(&gpu_result, d_out, sizeof(float), mcMemcpyDeviceToHost));
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// 验证误差
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double relative_error = std::abs(gpu_result - cpu_result) / std::abs(cpu_result);
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if (relative_error > REDUCE_ERROR_TOLERANCE) {
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std::cout << " 失败: 误差过大 " << relative_error << std::endl;
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allPassed = false;
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} else {
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std::cout << " 通过 (误差: " << relative_error << ")" << std::endl;
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}
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mcFree(d_in);
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mcFree(d_out);
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}
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// 测试特殊值 (NaN, Inf)
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if (size > 100) {
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std::cout << " 测试特殊值..." << std::endl;
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auto data = generator.generateSpecialFloats(size);
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float init_value = 0.0f;
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double cpu_result = cpuReduceSum(data, static_cast<double>(init_value));
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float *d_in;
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float *d_out;
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MACA_CHECK(mcMalloc(&d_in, size * sizeof(float)));
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MACA_CHECK(mcMalloc(&d_out, sizeof(float)));
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MACA_CHECK(mcMemcpy(d_in, data.data(), size * sizeof(float), mcMemcpyHostToDevice));
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algorithm.reduce(d_in, d_out, size, init_value);
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float gpu_result;
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MACA_CHECK(mcMemcpy(&gpu_result, d_out, sizeof(float), mcMemcpyDeviceToHost));
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// 对于包含特殊值的情况，检查是否正确处理
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if (std::isfinite(cpu_result) && std::isfinite(gpu_result)) {
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double relative_error = std::abs(gpu_result - cpu_result) / std::abs(cpu_result);
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if (relative_error > REDUCE_ERROR_TOLERANCE) {
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std::cout << " 失败: 特殊值处理错误" << std::endl;
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allPassed = false;
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} else {
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std::cout << " 通过 (特殊值处理)" << std::endl;
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}
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} else {
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std::cout << " 通过 (特殊值结果)" << std::endl;
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}
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mcFree(d_in);
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mcFree(d_out);
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}
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}
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return allPassed;
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}
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void benchmarkPerformance() {
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PerformanceDisplay::printReduceSumHeader();
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TestDataGenerator generator;
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PerformanceMeter meter;
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ReduceSumAlgorithm<float, float> 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 data = generator.generateRandomFloats(size);
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float init_value = 0.0f;
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// 分配GPU内存
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float *d_in;
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float *d_out;
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MACA_CHECK(mcMalloc(&d_in, size * sizeof(float)));
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MACA_CHECK(mcMalloc(&d_out, sizeof(float)));
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MACA_CHECK(mcMemcpy(d_in, data.data(), size * sizeof(float), mcMemcpyHostToDevice));
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// Warmup阶段
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for (int iter = 0; iter < WARMUP_ITERATIONS; iter++) {
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algorithm.reduce(d_in, d_out, size, init_value);
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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.reduce(d_in, d_out, size, init_value);
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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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// 计算性能指标
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auto metrics = PerformanceCalculator::calculateReduceSum(size, avg_time);
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// 显示性能数据
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PerformanceDisplay::printReduceSumData(size, avg_time, 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["time_ms"] = std::to_string(avg_time);
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entry["throughput_gps"] = std::to_string(metrics.throughput_gps);
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entry["data_type"] = "float";
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perf_data.push_back(entry);
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mcFree(d_in);
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mcFree(d_out);
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}
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// 生成YAML性能报告
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YAMLPerformanceReporter::generateReduceSumYAML(perf_data, "reduce_sum_performance.yaml");
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PerformanceDisplay::printSavedMessage("reduce_sum_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 << "=== ReduceSum 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: " << ReduceSumAlgorithm<float, float>::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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} |