// ❌ 典型的Kernel拆解开发伪代码（简化版）
extern "C" __global__ __aicore__ void naive_matmul_kernel(
    const float* A, const float* B, float* C,
    int M, int N, int K) {
    
    // 🚩 问题1：手动计算全局索引，容易出错
    int global_row = get_global_id(0);
    int global_col = get_global_id(1);
    
    if (global_row >= M || global_col >= N) return;
    
    float sum = 0.0f;
    // 🚩 问题2：一次性处理完整计算，不考虑UB容量限制
    for (int k = 0; k < K; ++k) {
        sum += A[global_row * K + k] * B[k * N + global_col];
    }
    
    C[global_row * N + global_col] = sum;
}

// ✅ 工程化开发中的Tiling数据结构设计
struct MatMulTilingData {
    // 问题空间描述
    int32_t M, N, K;           // 全局矩阵维度
    
    // Tiling策略参数
    int32_t tile_M, tile_N, tile_K;  // 单Tile大小
    int32_t num_tiles_M, num_tiles_N, num_tiles_K;  // 各维度Tile数量
    
    // 硬件映射信息
    int32_t core_assign_M, core_assign_N;  // Core网格划分
    int32_t total_tiles;                   // 总Tile数
    
    // 性能优化参数
    int32_t double_buffer_size;           // 双缓冲大小
    PipelineConfig pipeline_config;       // 流水线配置
    
    // 对齐与边界处理
    AlignmentInfo alignment_info;
    PaddingConfig padding_config;
};

// 注册Tiling数据结构（Host-Device通信契约）
REGISTER_TILING_DATA(MatMulTilingData);

class IntelligentTilingSolver {
private:
    HardwareProfile hw_profile_;      // 硬件特性
    MemoryHierarchy mem_hierarchy_;   // 存储层次
    PerformanceModel perf_model_;     // 性能模型
    
public:
    TilingStrategy solve_optimal_tiling(const ProblemSpec& problem) {
        TilingStrategy strategy;
        
        // 🎯 第一步：基于硬件特性的初始估计
        auto initial_estimate = estimate_based_on_hardware(problem, hw_profile_);
        
        // 🔧 第二步：考虑存储约束的修正
        strategy = apply_memory_constraints(initial_estimate, mem_hierarchy_);
        
        // ⚡ 第三步：性能模拟与迭代优化
        strategy = iterative_performance_optimization(strategy, perf_model_);
        
        // 🎨 第四步：特殊场景优化（尾块、非对齐等）
        strategy = apply_special_case_optimizations(strategy, problem);
        
        return strategy;
    }
    
private:
    TilingStrategy estimate_based_on_hardware(const ProblemSpec& problem,
                                            const HardwareProfile& hw) {
        TilingStrategy estimate;
        
        // 经验公式：基于AI Core数量和计算能力
        int total_compute_units = hw.cube_cores * hw.vector_cores;
        
        // 计算各维度的基础分块大小
        estimate.tile_M = std::min(problem.M, 
                                  hw.optimal_tile_size_M * total_compute_units);
        estimate.tile_N = std::min(problem.N, 
                                  hw.optimal_tile_size_N * total_compute_units);
        estimate.tile_K = calculate_optimal_K_tile(problem.K, hw);
        
        // 确保对齐要求
        estimate.tile_M = align_up(estimate.tile_M, hw.alignment_requirement);
        estimate.tile_N = align_up(estimate.tile_N, hw.alignment_requirement);
        
        return estimate;
    }
    
    int32_t calculate_optimal_K_tile(int32_t K, const HardwareProfile& hw) {
        // K维度的Tiling策略需要考虑数据复用
        if (K <= hw.l1_cache_size / 2) {
            return K;  // 整个K维度可放入L1缓存
        } else if (K <= hw.l2_cache_size / 4) {
            return hw.optimal_k_tile_medium;
        } else {
            return hw.optimal_k_tile_small;
        }
    }
};

// ✅ 工程化矩阵乘法Kernel实现（核心部分）
template <typename T, int TILE_M, int TILE_N, int TILE_K>
__aicore__ void matmul_tile_kernel(
    const T* __restrict__ A,      // 输入矩阵A
    const T* __restrict__ B,      // 输入矩阵B
    T* __restrict__ C,            // 输出矩阵C
    const MatMulTilingData& tiling, // Tiling策略数据
    int tile_id                    // 当前Tile ID
) {
    // 🎯 步骤1：根据Tile ID计算数据位置
    int tile_m = tile_id / tiling.num_tiles_N;
    int tile_n = tile_id % tiling.num_tiles_N;
    
    int start_m = tile_m * tiling.tile_M;
    int start_n = tile_n * tiling.tile_N;
    
    // 处理边界Tile
    int actual_tile_M = (tile_m == tiling.num_tiles_M - 1) 
                       ? (tiling.M - start_m) : tiling.tile_M;
    int actual_tile_N = (tile_n == tiling.num_tiles_N - 1)
                       ? (tiling.N - start_n) : tiling.tile_N;
    
    // 🎯 步骤2：在UB中分配双缓冲区
    __ub__ T ub_A[2][TILE_M][TILE_K];
    __ub__ T ub_B[2][TILE_K][TILE_N];
    __ub__ T ub_C[TILE_M][TILE_N] = {0};
    
    // 🎯 步骤3：流水线执行 - 计算与搬运重叠
    for (int k_start = 0; k_start < tiling.K; k_start += TILE_K) {
        int k_end = std::min(k_start + TILE_K, tiling.K);
        int k_len = k_end - k_start;
        
        int buffer_idx = (k_start / TILE_K) % 2;
        int next_buffer_idx = 1 - buffer_idx;
        
        // 异步搬运下一个Tile的数据
        if (k_start + TILE_K < tiling.K) {
            // 搬运A的下一个分块
            dma_copy_async(ub_A[next_buffer_idx][0],
                          &A[(start_m) * tiling.K + (k_start + TILE_K)],
                          actual_tile_M * k_len * sizeof(T));
            
            // 搬运B的下一个分块  
            dma_copy_async(ub_B[next_buffer_idx][0],
                          &B[(k_start + TILE_K) * tiling.N + start_n],
                          k_len * actual_tile_N * sizeof(T));
        }
        
        // 等待当前Tile数据就绪
        pipeline_wait();
        
        // 🎯 步骤4：核心计算 - 使用Cube Core
        for (int mi = 0; mi < actual_tile_M; ++mi) {
            for (int ni = 0; ni < actual_tile_N; ++ni) {
                T sum = ub_C[mi][ni];
                #pragma unroll
                for (int ki = 0; ki < k_len; ++ki) {
                    sum += ub_A[buffer_idx][mi][ki] * 
                           ub_B[buffer_idx][ki][ni];
                }
                ub_C[mi][ni] = sum;
            }
        }
        
        // 切换缓冲区
        pipeline_release();
    }
    
    // 🎯 步骤5：写回结果
    dma_copy(&C[start_m * tiling.N + start_n],
             ub_C[0],
             actual_tile_M * actual_tile_N * sizeof(T));
}

ascend_c_operator/
├── CMakeLists.txt                    # 项目构建配置
├── README.md                         # 项目说明文档
├── include/                          # 公共头文件
│   ├── operator_interface.h          # 算子接口定义
│   ├── tiling_strategy.h             # Tiling策略定义
│   └── common_macros.h               # 公共宏定义
├── src/
│   ├── host/                         # Host端代码
│   │   ├── operator_impl.cpp         # 算子Host实现
│   │   ├── tiling_calculator.cpp     # Tiling计算逻辑
│   │   └── runtime_manager.cpp       # 运行时管理
│   └── device/                       # Device端代码
│       ├── kernel_impl.cu            # Kernel实现
│       ├── device_functions.cpp      # 设备端函数
│       └── memory_manager.cpp        # 设备内存管理
├── tests/                            # 测试代码
│   ├── unit_tests/                   # 单元测试
│   ├── performance_tests/            # 性能测试
│   └── integration_tests/            # 集成测试
└── scripts/                          # 构建和部署脚本
    ├── build.sh                      # 构建脚本
    ├── run_tests.sh                  # 测试脚本
    └── profile.sh                    # 性能分析脚本

// operator_interface.h
#pragma once

#include <cstdint>
#include <vector>

// 算子输入输出描述
struct OperatorIO {
    void* data;                    // 数据指针
    std::vector<int32_t> shape;    // 数据形状
    int32_t dtype;                 // 数据类型
    int64_t size;                  // 数据大小（字节）
};

// 算子参数（属性）
struct OperatorAttr {
    std::string name;              // 属性名
    std::string type;              // 属性类型
    std::string value;             // 属性值
};

// Tiling数据结构 - Host/Device共享
struct Conv2DTilingData {
    // 输入输出维度
    int32_t batch_size;
    int32_t in_channels;
    int32_t out_channels;
    int32_t in_height, in_width;
    int32_t out_height, out_width;
    int32_t kernel_h, kernel_w;
    int32_t stride_h, stride_w;
    int32_t pad_h, pad_w;
    int32_t dilation_h, dilation_w;
    
    // Tiling参数
    int32_t tile_batch;
    int32_t tile_out_h;
    int32_t tile_out_w;
    int32_t tile_out_c;
    
    // 性能优化参数
    int32_t double_buffer_size;
    bool use_tensor_core;
    int32_t pipeline_depth;
    
    // 边界处理
    bool has_tail_batch;
    bool has_tail_height;
    bool has_tail_width;
    int32_t tail_batch_size;
    int32_t tail_height_size;
    int32_t tail_width_size;
    
    // 内存布局信息
    int32_t data_layout;  // NCHW or NHWC
    int32_t align_size;
};

// 注册Tiling数据结构
REGISTER_TILING_DATA(Conv2DTilingData);

// tiling_calculator.cpp
#include "tiling_strategy.h"
#include <algorithm>
#include <cmath>

class Conv2DTilingCalculator {
public:
    static TilingResult calculate_tiling(const Conv2DParams& params,
                                       const HardwareInfo& hw_info) {
        TilingResult result;
        
        // 🔍 第一步：分析计算特征
        auto comp_chars = analyze_computation_characteristics(params);
        
        // 🎯 第二步：基于硬件约束进行初始划分
        result = initial_tiling_based_on_hardware(params, hw_info, comp_chars);
        
        // ⚖️ 第三步：负载均衡优化
        result = optimize_load_balance(result, hw_info);
        
        // 🔧 第四步：边界条件处理
        result = handle_boundary_conditions(result, params);
        
        // 📊 第五步：性能预估与验证
        if (!validate_tiling_performance(result, hw_info)) {
            // 如果性能不达标，重新调整策略
            result = adjust_tiling_strategy(result, hw_info);
        }
        
        return result;
    }
    
private:
    static ComputationCharacteristics analyze_computation_characteristics(
        const Conv2DParams& params) {
        ComputationCharacteristics chars;
        
        // 计算总操作数（FLOPs）
        chars.total_flops = static_cast<int64_t>(params.batch_size) *
                          params.out_channels *
                          params.out_height * params.out_width *
                          params.in_channels *
                          params.kernel_h * params.kernel_w * 2;
        
        // 计算数据量（字节）
        chars.input_size = params.batch_size * params.in_channels *
                         params.in_height * params.in_width *
                         get_dtype_size(params.dtype);
        
        chars.weight_size = params.out_channels * params.in_channels *
                          params.kernel_h * params.kernel_w *
                          get_dtype_size(params.dtype);
        
        chars.output_size = params.batch_size * params.out_channels *
                          params.out_height * params.out_width *
                          get_dtype_size(params.dtype);
        
        // 计算计算强度（FLOPs/Byte）
        chars.compute_intensity = static_cast<double>(chars.total_flops) /
                                 (chars.input_size + chars.weight_size + 
                                  chars.output_size);
        
        // 判断计算类型
        if (chars.compute_intensity > 10.0) {
            chars.compute_type = COMPUTE_BOUND;
        } else if (chars.compute_intensity > 1.0) {
            chars.compute_type = BALANCED;
        } else {
            chars.compute_type = MEMORY_BOUND;
        }
        
        return chars;
    }
    
    static TilingResult initial_tiling_based_on_hardware(
        const Conv2DParams& params,
        const HardwareInfo& hw_info,
        const ComputationCharacteristics& chars) {
        
        TilingResult result;
        
        // 根据计算类型选择不同的Tiling策略
        switch (chars.compute_type) {
            case COMPUTE_BOUND:
                // 计算受限，优先利用计算资源
                result = tiling_for_compute_bound(params, hw_info);
                break;
                
            case MEMORY_BOUND:
                // 内存受限，优化数据局部性和访存
                result = tiling_for_memory_bound(params, hw_info);
                break;
                
            case BALANCED:
                // 平衡型，综合考虑各方面因素
                result = tiling_for_balanced(params, hw_info);
                break;
        }
        
        return result;
    }
    
    static TilingResult tiling_for_compute_bound(
        const Conv2DParams& params,
        const HardwareInfo& hw_info) {
        
        TilingResult result;
        
        // 对于计算受限的场景，尽量增大Tile以增加计算密度
        int available_cores = hw_info.ai_core_count;
        int ub_capacity = hw_info.unified_buffer_size;
        
        // 经验公式：基于Cube Core数量确定输出通道的Tiling
        result.tile_out_c = std::min(params.out_channels,
                                    hw_info.optimal_channels_per_core * 2);
        
        // 基于UB容量确定空间维度的Tiling
        int bytes_per_element = get_dtype_size(params.dtype);
        int elements_per_tile = ub_capacity / (bytes_per_element * 3); // 输入、权重、输出
        
        // 保守估计，实际需要考虑数据复用
        int estimated_tile_size = static_cast<int>(std::sqrt(elements_per_tile / 3));
        result.tile_out_h = std::min(params.out_height, estimated_tile_size);
        result.tile_out_w = std::min(params.out_width, estimated_tile_size);
        
        // Batch维度的Tiling
        if (params.batch_size > available_cores * 2) {
            result.tile_batch = std::max(1, params.batch_size / available_cores);
        } else {
            result.tile_batch = params.batch_size;
        }
        
        return result;
    }
};

// kernel_impl.cu
#include "operator_interface.h"
#include <aicore.h>

// 注册Kernel函数
__global__ __aicore__ void conv2d_forward_kernel(
    const float* input,
    const float* weight,
    float* output,
    const Conv2DTilingData tiling,
    int tile_id) {
    
    // 🎯 第一步：确定当前Core的任务范围
    int core_id = get_core_id();
    int total_cores = get_core_num();
    
    // 将tile_id映射到具体的输出位置
    int tiles_per_core = (tiling.total_tiles + total_cores - 1) / total_cores;
    int start_tile = core_id * tiles_per_core;
    int end_tile = min(start_tile + tiles_per_core, tiling.total_tiles);
    
    // 🎯 第二步：循环处理分配给当前Core的所有Tile
    for (int tile_idx = start_tile; tile_idx < end_tile; ++tile_idx) {
        // 计算当前Tile在输出中的位置
        int tile_batch = tile_idx / (tiling.tile_oh * tiling.tile_ow * tiling.tile_oc);
        int residual = tile_idx % (tiling.tile_oh * tiling.tile_ow * tiling.tile_oc);
        
        int tile_oh = (residual / (tiling.tile_ow * tiling.tile_oc)) * tiling.tile_oh;
        int residual2 = residual % (tiling.tile_ow * tiling.tile_oc);
        
        int tile_ow = (residual2 / tiling.tile_oc) * tiling.tile_ow;
        int tile_oc = (residual2 % tiling.tile_oc) * tiling.tile_oc;
        
        // 🎯 第三步：处理边界Tile
        int actual_batch = min(tiling.tile_batch, 
                             tiling.batch_size - tile_batch * tiling.tile_batch);
        int actual_oh = min(tiling.tile_oh,
                          tiling.out_height - tile_oh);
        int actual_ow = min(tiling.tile_ow,
                          tiling.out_width - tile_ow);
        int actual_oc = min(tiling.tile_oc,
                          tiling.out_channels - tile_oc);
        
        // 🎯 第四步：执行卷积计算
        execute_conv_tile(input, weight, output,
                         tile_batch, tile_oh, tile_ow, tile_oc,
                         actual_batch, actual_oh, actual_ow, actual_oc,
                         tiling);
    }
}

// 实际的Tile卷积计算
__device__ void execute_conv_tile(
    const float* input,
    const float* weight,
    float* output,
    int batch_start, int oh_start, int ow_start, int oc_start,
    int batch_size, int oh_size, int ow_size, int oc_size,
    const Conv2DTilingData& tiling) {
    
    // 在UB中分配缓冲区（双缓冲）
    __ub__ float input_buf[2][TILE_BATCH][TILE_IH][TILE_IW][IC];
    __ub__ float weight_buf[2][OC][KC][KH][KW];
    __ub__ float output_buf[TILE_BATCH][TILE_OH][TILE_OW][OC] = {0};
    
    // 计算输入Tile的起始位置
    int ih_start = oh_start * tiling.stride_h - tiling.pad_h;
    int iw_start = ow_start * tiling.stride_w - tiling.pad_w;
    
    int tile_ih = oh_size * tiling.stride_h + (tiling.kernel_h - 1) * tiling.dilation_h;
    int tile_iw = ow_size * tiling.stride_w + (tiling.kernel_w - 1) * tiling.dilation_w;
    
    // 🎯 流水线执行：计算与数据搬运重叠
    for (int ic_start = 0; ic_start < tiling.in_channels; ic_start += IC) {
        int ic_end = min(ic_start + IC, tiling.in_channels);
        int ic_len = ic_end - ic_start;
        
        int buf_idx = (ic_start / IC) % 2;
        int next_buf_idx = 1 - buf_idx;
        
        // 异步搬运下一组数据
        if (ic_start + IC < tiling.in_channels) {
            // 搬运输入数据
            copy_input_tile_async(input, input_buf[next_buf_idx],
                                batch_start, ih_start, iw_start, ic_start + IC,
                                batch_size, tile_ih, tile_iw, IC);
            
            // 搬运权重数据
            copy_weight_tile_async(weight, weight_buf[next_buf_idx],
                                 oc_start, ic_start + IC,
                                 oc_size, IC);
        }
        
        // 等待当前数据就绪
        pipeline_wait();
        
        // 🎯 核心卷积计算
        for (int b = 0; b < batch_size; ++b) {
            for (int oh = 0; oh < oh_size; ++oh) {
                for (int ow = 0; ow < ow_size; ++ow) {
                    for (int oc = 0; oc < oc_size; ++oc) {
                        float sum = output_buf[b][oh][ow][oc];
                        
                        // 滑动窗口计算
                        for (int kh = 0; kh < tiling.kernel_h; ++kh) {
                            for (int kw = 0; kw < tiling.kernel_w; ++kw) {
                                int ih = oh * tiling.stride_h + kh * tiling.dilation_h - tiling.pad_h;
                                int iw = ow * tiling.stride_w + kw * tiling.dilation_w - tiling.pad_w;
                                
                                if (ih >= 0 && ih < tile_ih && iw >= 0 && iw < tile_iw) {
                                    for (int ic = 0; ic < ic_len; ++ic) {
                                        float input_val = input_buf[buf_idx][b][ih][iw][ic];
                                        float weight_val = weight_buf[buf_idx][oc][ic][kh][kw];
                                        sum += input_val * weight_val;
                                    }
                                }
                            }
                        }
                        
                        output_buf[b][oh][ow][oc] = sum;
                    }
                }
            }
        }
        
        // 释放当前缓冲区，准备下一轮
        pipeline_release();
    }
    
    // 写回输出结果
    copy_output_tile(output, output_buf,
                     batch_start, oh_start, ow_start, oc_start,
                     batch_size, oh_size, ow_size, oc_size);
}

// runtime_manager.cpp
#include "operator_interface.h"
#include <ascendcl.h>
#include <memory>
#include <vector>

class Conv2DOperatorRuntime {
private:
    aclrtStream stream_;
    aclrtContext context_;
    bool initialized_;
    
    // 性能统计
    struct PerformanceStats {
        int64_t total_execution_time;
        int64_t kernel_execution_time;
        int64_t memory_copy_time;
        int64_t setup_time;
        int call_count;
    } stats_;
    
public:
    Conv2DOperatorRuntime() : initialized_(false) {
        initialize_runtime();
    }
    
    ~Conv2DOperatorRuntime() {
        if (initialized_) {
            cleanup_runtime();
        }
    }
    
    // 执行卷积算子
    Status execute(const Conv2DParams& params,
                  const void* input,
                  const void* weight,
                  void* output) {
        
        if (!initialized_) {
            return Status::Error("Runtime not initialized");
        }
        
        auto start_total = get_current_time();
        
        // 🎯 第一步：计算Tiling策略
        auto start_setup = get_current_time();
        auto tiling_strategy = calculate_tiling_strategy(params);
        auto tiling_data = prepare_tiling_data(params, tiling_strategy);
        stats_.setup_time += get_current_time() - start_setup;
        
        // 🎯 第二步：分配设备内存
        void* d_input = nullptr;
        void* d_weight = nullptr;
        void* d_output = nullptr;
        
        Status alloc_status = allocate_device_memory(params, 
                                                   &d_input, &d_weight, &d_output);
        if (!alloc_status.ok()) {
            return alloc_status;
        }
        
        // 🎯 第三步：数据拷贝（Host -> Device）
        auto start_memcpy = get_current_time();
        Status copy_status = copy_to_device(input, weight, 
                                          d_input, d_weight, params);
        if (!copy_status.ok()) {
            free_device_memory(d_input, d_weight, d_output);
            return copy_status;
        }
        stats_.memory_copy_time += get_current_time() - start_memcpy;
        
        // 🎯 第四步：启动Kernel
        auto start_kernel = get_current_time();
        Status kernel_status = launch_kernel(d_input, d_weight, d_output,
                                           tiling_data, params);
        if (!kernel_status.ok()) {
            free_device_memory(d_input, d_weight, d_output);
            return kernel_status;
        }
        
        // 等待Kernel执行完成
        aclrtSynchronizeStream(stream_);
        stats_.kernel_execution_time += get_current_time() - start_kernel;
        
        // 🎯 第五步：数据拷贝（Device -> Host）
        start_memcpy = get_current_time();
        copy_status = copy_from_device(d_output, output, params);
        stats_.memory_copy_time += get_current_time() - start_memcpy;
        
        // 🎯 第六步：释放设备内存
        free_device_memory(d_input, d_weight, d_output);
        
        stats_.total_execution_time += get_current_time() - start_total;
        stats_.call_count++;
        
        return Status::Success();
    }
    
    // 获取性能统计
    PerformanceStats get_performance_stats() const {
        return stats_;
    }
    
private:
    Status initialize_runtime() {
        // 初始化ACL
        aclError ret = aclInit(nullptr);
        if (ret != ACL_SUCCESS) {
            return Status::Error("Failed to initialize ACL");
        }
        
        // 设置设备
        ret = aclrtSetDevice(0);
        if (ret != ACL_SUCCESS) {
            aclFinalize();
            return Status::Error("Failed to set device");
        }
        
        // 创建Context
        ret = aclrtCreateContext(&context_, 0);
        if (ret != ACL_SUCCESS) {
            aclrtResetDevice(0);
            aclFinalize();
            return Status::Error("Failed to create context");
        }
        
        // 创建Stream
        ret = aclrtCreateStream(&stream_);
        if (ret != ACL_SUCCESS) {
            aclrtDestroyContext(context_);
            aclrtResetDevice(0);
            aclFinalize();
            return Status::Error("Failed to create stream");
        }
        
        initialized_ = true;
        return Status::Success();
    }
    
    Status launch_kernel(void* d_input, void* d_weight, void* d_output,
                        const Conv2DTilingData& tiling,
                        const Conv2DParams& params) {
        
        // 准备Kernel参数
        struct KernelArgs {
            void* input;
            void* weight;
            void* output;
            Conv2DTilingData tiling;
            int total_tiles;
        } args;
        
        args.input = d_input;
        args.weight = d_weight;
        args.output = d_output;
        args.tiling = tiling;
        args.total_tiles = calculate_total_tiles(tiling);
        
        // 计算网格和块大小
        uint32_t block_dim = calculate_block_dim(tiling);
        uint32_t grid_dim = calculate_grid_dim(tiling, block_dim);
        
        // 启动Kernel
        aclError ret = aclrtLaunchKernel(
            (void*)conv2d_forward_kernel,
            grid_dim, 1, 1,
            block_dim, 1, 1,
            0, stream_,
            &args, sizeof(args),
            nullptr
        );
        
        if (ret != ACL_SUCCESS) {
            return Status::Error("Failed to launch kernel");
        }
        
        return Status::Success();
    }
};

// 防御性编程：添加边界检查
__device__ void safe_memory_access(void* ptr, size_t offset, size_t max_size) {
    // 使用内置函数检查内存访问
    #ifdef DEBUG
    if (offset >= max_size) {
        printf("Memory access out of bounds: offset=%zu, max_size=%zu\n", 
               offset, max_size);
        // 触发断点或返回安全值
        return;
    }
    #endif
    // 实际的内存访问代码
}

// 正确的边界Tile处理
int get_actual_tile_size(int total_size, int tile_idx, 
                        int tile_size, int num_tiles) {
    int start_idx = tile_idx * tile_size;
    if (tile_idx == num_tiles - 1) {
        // 最后一个Tile可能小于标准大小
        return total_size - start_idx;
    }
    return tile_size;
}

// 性能分析工具集成
class PerformanceProfiler {
public:
    void profile_kernel_execution(const std::string& kernel_name,
                                 const LaunchConfig& config) {
        auto start = get_high_resolution_time();
        
        // 执行Kernel
        launch_kernel_with_config(kernel_name, config);
        
        auto end = get_high_resolution_time();
        double elapsed_ms = (end - start) / 1000000.0;
        
        // 计算理论性能
        double theoretical_peak = calculate_theoretical_peak(config);
        double achieved_perf = calculate_achieved_performance(config, elapsed_ms);
        double utilization = achieved_perf / theoretical_peak * 100.0;
        
        // 记录性能数据
        record_performance_data(kernel_name, config, 
                              elapsed_ms, utilization);
        
        // 如果利用率低于阈值，发出警告
        if (utilization < TARGET_UTILIZATION) {
            suggest_optimizations(kernel_name, config, utilization);
        }
    }
    
private:
    void suggest_optimizations(const std::string& kernel_name,
                              const LaunchConfig& config,
                              double utilization) {
        std::cout << "⚠️  Performance alert for kernel: " << kernel_name << std::endl;
        std::cout << "   Current utilization: " << utilization << "%" << std::endl;
        
        if (utilization < 30.0) {
            std::cout << "   💡 Suggestion: Check load balancing and tile size" << std::endl;
            std::cout << "   💡 Consider using smaller tiles for better parallelism" << std::endl;
        } else if (utilization < 60.0) {
            std::cout << "   💡 Suggestion: Optimize memory access patterns" << std::endl;
            std::cout << "   💡 Consider using double buffering" << std::endl;
        } else if (utilization < 85.0) {
            std::cout << "   💡 Suggestion: Fine-tune instruction scheduling" << std::endl;
            std::cout << "   💡 Consider loop unrolling and vectorization" << std::endl;
        }
    }
};

// 数值稳定性增强
template <typename T>
class NumericallyStableAccumulator {
private:
    std::vector<T> partial_sums;
    
public:
    void add(T value) {
        partial_sums.push_back(value);
        
        // 定期合并部分和以减少误差
        if (partial_sums.size() > 100) {
            compress_partial_sums();
        }
    }
    
    T get_result() const {
        // 使用Kahan求和算法减少累积误差
        T sum = 0;
        T compensation = 0;
        
        for (T value : partial_sums) {
            T adjusted_value = value - compensation;
            T new_sum = sum + adjusted_value;
            compensation = (new_sum - sum) - adjusted_value;
            sum = new_sum;
        }
        
        return sum;
    }
    
private:
    void compress_partial_sums() {
        // 使用稳定的合并算法
        std::sort(partial_sums.begin(), partial_sums.end(),
                 [](T a, T b) { return std::abs(a) < std::abs(b); });
        
        T current_sum = 0;
        std::vector<T> new_sums;
        
        for (T value : partial_sums) {
            current_sum += value;
            if (std::abs(current_sum) > 1e6) { // 防止溢出
                new_sums.push_back(current_sum);
                current_sum = 0;
            }
        }
        
        if (std::abs(current_sum) > 0) {
            new_sums.push_back(current_sum);
        }
        
        partial_sums = std::move(new_sums);
    }
};

// 平台自适应代码
class PlatformAwareOptimizer {
public:
    static KernelConfig get_optimal_config(const HardwareInfo& hw_info) {
        KernelConfig config;
        
        // 根据硬件特性选择最优配置
        if (hw_info.arch_version >= ARCH_VERSION_910) {
            // Ascend 910及更新版本
            config.tile_size = 256;
            config.double_buffer = true;
            config.pipeline_depth = 4;
            config.use_tensor_core = hw_info.has_tensor_core;
        } else if (hw_info.arch_version >= ARCH_VERSION_310) {
            // Ascend 310
            config.tile_size = 128;
            config.double_buffer = true;
            config.pipeline_depth = 2;
            config.use_tensor_core = false;
        } else {
            // 旧版本或未知硬件
            config.tile_size = 64;
            config.double_buffer = false;
            config.pipeline_depth = 1;
            config.use_tensor_core = false;
        }
        
        // 根据内存大小调整配置
        size_t available_memory = hw_info.memory_size;
        if (available_memory < 4 * 1024 * 1024) { // 小于4MB
            config.tile_size = std::min(config.tile_size, 32);
        }
        
        return config;
    }
    
    static void validate_config_for_hardware(const KernelConfig& config,
                                           const HardwareInfo& hw_info) {
        // 检查配置是否与硬件兼容
        if (config.use_tensor_core && !hw_info.has_tensor_core) {
            std::cerr << "Warning: Tensor Core requested but not available" << std::endl;
            config.use_tensor_core = false;
        }
        
        if (config.tile_size > hw_info.max_tile_size) {
            std::cerr << "Warning: Tile size too large, adjusting to " 
                      << hw_info.max_tile_size << std::endl;
            config.tile_size = hw_info.max_tile_size;
        }
    }
};

// 增强调试支持
class AdvancedDebugSupport {
public:
    // 条件断点支持
    #ifdef ENABLE_DEBUG
    #define DEBUG_BREAK_IF(condition) \
        if (condition) { \
            debug_breakpoint(__FILE__, __LINE__, #condition); \
        }
    #else
    #define DEBUG_BREAK_IF(condition)
    #endif
    
    // 设备端调试信息输出
    __device__ void device_debug_print(int core_id, const char* format, ...) {
        #ifdef ENABLE_DEVICE_DEBUG
        if (core_id == 0) { // 只从Core 0输出，避免混乱
            va_list args;
            va_start(args, format);
            vprintf(format, args);
            va_end(args);
        }
        #endif
    }
    
    // 内存访问检查
    __device__ bool validate_memory_access(void* ptr, size_t size, 
                                         size_t max_size) {
        #ifdef ENABLE_MEMORY_CHECK
        uintptr_t addr = reinterpret_cast<uintptr_t>(ptr);
        uintptr_t end_addr = addr + size;
        
        if (end_addr > max_size) {
            device_debug_print(get_core_id(), 
                "Memory access violation: addr=%p, size=%zu, max=%zu\n",
                ptr, size, max_size);
            return false;
        }
        return true;
        #else
        return true;
        #endif
    }
    
    // 性能计数器
    class PerformanceCounter {
    private:
        std::atomic<int64_t> counter_{0};
        std::string name_;
        
    public:
        PerformanceCounter(const std::string& name) : name_(name) {}
        
        void increment(int64_t value = 1) {
            counter_ += value;
        }
        
        void report() const {
            std::cout << "[" << name_ << "] count: " << counter_ << std::endl;
        }
        
        void reset() {
            counter_ = 0;
        }
    };
};

class ModelExecutionScheduler {
private:
    struct ModelInfo {
        std::string model_id;
        KernelConfig kernel_config;
        PerformanceProfile perf_profile;
        int priority;
        size_t memory_footprint;
    };
    
    std::unordered_map<std::string, ModelInfo> model_registry_;
    std::priority_queue<ExecutionTask> task_queue_;
    
public:
    // 动态模型加载与卸载
    Status load_model(const std::string& model_path,
                     const ModelConfig& config) {
        // 1. 分析模型结构
        auto model_analysis = analyze_model_structure(model_path);
        
        // 2. 生成优化后的Kernel
        auto kernel_config = generate_optimized_kernel(model_analysis, config);
        
        // 3. 预热执行，收集性能数据
        auto perf_profile = warmup_and_profile(kernel_config);
        
        // 4. 注册到调度器
        register_model(model_path, kernel_config, perf_profile);
        
        return Status::Success();
    }
    
    // 智能调度算法
    ExecutionPlan schedule_execution(const InferenceRequest& request) {
        ExecutionPlan plan;
        
        // 基于多因素的调度决策
        plan = multi_factor_scheduling(request, {
            .latency_requirement = request.max_latency,
            .throughput_requirement = request.min_throughput,
            .power_constraint = current_power_limit,
            .thermal_constraint = current_temperature,
            .qos_requirement = request.qos_level
        });
        
        return plan;
    }
};

// 优化前：随机内存访问
for (int i = 0; i < N; ++i) {
    for (int j = 0; j < M; ++j) {
        // 跳跃式访问，缓存不友好
        result[i] += matrix[j][i] * vector[j];
    }
}

// 优化后：连续内存访问
for (int j = 0; j < M; ++j) {
    for (int i = 0; i < N; ++i) {
        // 连续访问，缓存友好
        result[i] += matrix[j][i] * vector[j];
    }
}

class ComputeDensityOptimizer {
public:
    // 计算计算强度（FLOPs/Byte）
    double calculate_compute_intensity(const KernelProfile& profile) {
        double flops = profile.operation_count * 2.0; // 乘加算2次操作
        double memory_bytes = profile.input_size + 
                            profile.weight_size + 
                            profile.output_size;
        return flops / memory_bytes;
    }
    
    // 根据计算强度选择优化策略
    OptimizationStrategy select_strategy(double intensity) {
        if (intensity > 100.0) {
            return COMPUTE_BOUND_STRATEGY;    // 计算受限，优化计算
        } else if (intensity > 10.0) {
            return BALANCED_STRATEGY;         // 均衡，兼顾计算和内存
        } else {
            return MEMORY_BOUND_STRATEGY;     // 内存受限，优化访存
        }
    }
};

class AdvancedPipelineManager {
private:
    enum PipelineStage {
        STAGE_DATA_LOAD,
        STAGE_COMPUTE,
        STAGE_DATA_STORE,
        STAGE_SYNC
    };
    
    struct PipelineSlot {
        PipelineStage stage;
        void* data_buffer;
        bool ready;
        int64_t start_time;
        int64_t end_time;
    };
    
    std::vector<PipelineSlot> pipeline_;
    int pipeline_depth_;
    
public:
    void execute_with_pipeline(const std::vector<ComputeTask>& tasks) {
        // 初始化流水线
        initialize_pipeline(pipeline_depth_);
        
        for (size_t i = 0; i < tasks.size(); ++i) {
            // 🎯 阶段1：启动数据加载（异步）
            if (i + pipeline_depth_ < tasks.size()) {
                start_async_data_load(tasks[i + pipeline_depth_]);
            }
            
            // 🎯 阶段2：执行计算（当前任务）
            execute_computation(tasks[i]);
            
            // 🎯 阶段3：启动数据存储（异步，上一个任务的结果）
            if (i > 0) {
                start_async_data_store(tasks[i - 1]);
            }
            
            // 🎯 阶段4：流水线同步
            pipeline_sync();
        }
        
        // 完成剩余的数据存储
        flush_pipeline();
    }
};

// 集成性能分析框架
class IntegratedProfiler {
public:
    struct ProfilingResult {
        double total_time_ms;
        double kernel_time_ms;
        double memory_time_ms;
        double overhead_time_ms;
        double compute_efficiency;      // 计算效率
        double memory_efficiency;       // 内存效率
        double occupancy_rate;          // 占用率
        std::map<std::string, double> kernel_times;
        std::vector<std::string> bottlenecks;
    };
    
    ProfilingResult profile_operator(const OperatorConfig& config) {
        ProfilingResult result;
        
        // 使用Ascend的性能分析接口
        aclprofInit();
        aclprofStart(ACL_PROF_TASK_TIME);
        
        // 执行算子
        auto start_total = std::chrono::high_resolution_clock::now();
        execute_operator(config);
        auto end_total = std::chrono::high_resolution_clock::now();
        
        aclprofStop(ACL_PROF_TASK_TIME);
        
        // 收集性能数据
        result.total_time_ms = 
            std::chrono::duration<double, std::milli>(
                end_total - start_total).count();
        
        // 分析性能数据
        result.bottlenecks = analyze_performance_data();
        
        // 生成优化建议
        generate_optimization_suggestions(result);
        
        aclprofFinalize();
        return result;
    }
    
private:
    std::vector<std::string> analyze_performance_data() {
        std::vector<std::string> bottlenecks;
        
        // 读取硬件性能计数器
        auto hw_counters = read_hardware_counters();
        
        // 分析计算瓶颈
        if (hw_counters.compute_utilization < 60.0) {
            bottlenecks.push_back("计算单元利用率低");
            
            if (hw_counters.instruction_stalls > hw_counters.total_instructions * 0.3) {
                bottlenecks.push_back("指令流水线停顿严重");
            }
            
            if (hw_counters.memory_stalls > hw_counters.total_cycles * 0.4) {
                bottlenecks.push_back("内存等待时间过长");
            }
        }
        
        // 分析内存瓶颈
        if (hw_counters.memory_bandwidth_utilization < 40.0) {
            bottlenecks.push_back("内存带宽利用率低");
            
            if (hw_counters.cache_miss_rate > 0.1) {
                bottlenecks.push_back("缓存命中率低");
            }
            
            if (hw_counters.memory_access_pattern_score < 0.5) {
                bottlenecks.push_back("内存访问模式不佳");
            }
        }
        
        return bottlenecks;
    }
    
    void generate_optimization_suggestions(ProfilingResult& result) {
        result.optimization_suggestions.clear();
        
        for (const auto& bottleneck : result.bottlenecks) {
            if (bottleneck == "计算单元利用率低") {
                result.optimization_suggestions.push_back({
                    "增加计算强度",
                    "尝试增大Tile尺寸，增加每个核心的计算量",
                    "预计提升：10-30%"
                });
                
                result.optimization_suggestions.push_back({
                    "优化指令调度",
                    "重新安排指令顺序，减少依赖停顿",
                    "预计提升：5-15%"
                });
            }
            
            if (bottleneck == "内存带宽利用率低") {
                result.optimization_suggestions.push_back({
                    "优化数据布局",
                    "改为连续内存访问模式，提高缓存效率",
                    "预计提升：15-40%"
                });
                
                result.optimization_suggestions.push_back({
                    "使用双缓冲",
                    "重叠计算与数据搬运，隐藏内存延迟",
                    "预计提升：20-50%"
                });
            }
        }
    }
};

// 未来的理想编程模式
// 开发者只需描述算法
@ascend_kernel
void matmul_algorithm(float* A, float* B, float* C, int M, int N, int K) {
    for (int i = 0; i < M; ++i) {
        for (int j = 0; j < N; ++j) {
            float sum = 0.0f;
            for (int k = 0; k < K; ++k) {
                sum += A[i * K + k] * B[k * N + j];
            }
            C[i * N + j] = sum;
        }
    }
}

// 编译器自动生成优化的Tiling策略和并行代码
// 包括：自动双缓冲、自动向量化、自动流水线编排等

class AIDrivenOptimizer {
public:
    OptimizationPlan auto_optimize(const KernelSignature& kernel,
                                 const HardwareTarget& target) {
        // 使用强化学习搜索最优参数
        auto search_space = generate_search_space(kernel, target);
        
        // 使用性能预测模型加速搜索
        auto candidate_plans = predict_performance(search_space);
        
        // 选择最优方案
        return select_optimal_plan(candidate_plans);
    }
};