​

// 矢量加法算子核心实现
// 语言：Ascend C | 版本：CANN 7.0+

#include "kernel_operator.h"
using namespace AscendC;

// 常量定义，硬件特性相关
constexpr int32_t BUFFER_NUM = 2;  // 双缓冲设计
constexpr int32_t BLOCK_LENGTH = 256;  // 块大小，适配AI Core特性

class KernelAdd {
public:
    __aicore__ inline KernelAdd() {}
    
    // 初始化函数：内存分配和参数设置
    __aicore__ inline void Init(GM_ADDR x, GM_ADDR y, GM_ADDR z, 
                              uint32_t totalLength, uint32_t tileNum) {
        // 参数验证
        if (GetBlockNum() == 0) {
            return;  // 错误处理
        }
        
        // 计算每个核的任务量
        this->blockLength = totalLength / GetBlockNum();
        this->tileNum = tileNum;
        
        // 计算分块大小
        if (tileNum == 0) return;
        this->tileLength = this->blockLength / tileNum / BUFFER_NUM;
        
        // 设置全局内存地址
        xGm.SetGlobalBuffer((__gm__ half*)x + this->blockLength * GetBlockIdx(), 
                           this->blockLength);
        yGm.SetGlobalBuffer((__gm__ half*)y + this->blockLength * GetBlockIdx(), 
                           this->blockLength);
        zGm.SetGlobalBuffer((__gm__ half*)z + this->blockLength * GetBlockIdx(), 
                           this->blockLength);
        
        // 管道内存初始化
        pipe.InitBuffer(inQueueX, BUFFER_NUM, this->tileLength * sizeof(half));
        pipe.InitBuffer(inQueueY, BUFFER_NUM, this->tileLength * sizeof(half));
        pipe.InitBuffer(outQueueZ, BUFFER_NUM, this->tileLength * sizeof(half));
    }
    
    // 核心处理函数
    __aicore__ inline void Process() {
        // 流水线并行处理
        int32_t loopCount = this->tileNum * BUFFER_NUM;
        
        for (int32_t i = 0; i < loopCount; i++) {
            // 三级流水：数据搬运、计算、结果写回
            CopyIn(i);
            Compute(i);
            CopyOut(i);
        }
    }

private:
    // 数据搬入函数
    __aicore__ inline void CopyIn(int32_t progress) {
        // 从全局内存加载数据到局部缓存
        LocalTensor<half> xLocal = inQueueX.AllocTensor<half>();
        LocalTensor<half> yLocal = inQueueY.AllocTensor<half>();
        
        // 异步数据拷贝
        DataCopy(xLocal, xGm[progress * this->tileLength], this->tileLength);
        DataCopy(yLocal, yGm[progress * this->tileLength], this->tileLength);
        
        // 数据入队，准备计算
        inQueueX.EnQue(xLocal);
        inQueueY.EnQue(yLocal);
    }
    
    // 计算函数
    __aicore__ inline void Compute(int32_t progress) {
        // 从队列获取数据
        LocalTensor<half> xLocal = inQueueX.DeQue<half>();
        LocalTensor<half> yLocal = inQueueY.DeQue<half>();
        LocalTensor<half> zLocal = outQueueZ.AllocTensor<half>();
        
        // 矢量加法核心计算
        Add(zLocal, xLocal, yLocal, this->tileLength);
        
        // 结果入队
        outQueueZ.EnQue<half>(zLocal);
        
        // 释放输入张量
        inQueueX.FreeTensor(xLocal);
        inQueueY.FreeTensor(yLocal);
    }
    
    // 结果写回函数
    __aicore__ inline void CopyOut(int32_t progress) {
        LocalTensor<half> zLocal = outQueueZ.DeQue<half>();
        DataCopy(zGm[progress * this->tileLength], zLocal, this->tileLength);
        outQueueZ.FreeTensor(zLocal);
    }

private:
    // 管道内存管理
    TPipe pipe;
    TQue<QuePosition::VECIN, BUFFER_NUM> inQueueX, inQueueY;
    TQue<QuePosition::VECOUT, BUFFER_NUM> outQueueZ;
    GlobalTensor<half> xGm, yGm, zGm;
    
    uint32_t blockLength, tileNum, tileLength;
};

// 核函数入口
extern "C" __global__ __aicore__ void add_custom(GM_ADDR x, GM_ADDR y, GM_ADDR z, 
                                               GM_ADDR workspace, GM_ADDR tiling) {
    // 获取Tiling参数
    GET_TILING_DATA(tilingData, tiling);
    
    // 初始化算子实例
    KernelAdd op;
    op.Init(x, y, z, tilingData.totalLength, tilingData.tileNum);
    
    // 执行计算
    if (TILING_KEY_IS(1)) {
        op.Process();
    }
}

// Tiling策略动态调整算法
class DynamicTilingManager {
public:
    struct TilingConfig {
        uint32_t tileSize;
        uint32_t numTiles;
        uint32_t bufferFactor;
        bool useDoubleBuffering;
    };
    
    TilingConfig calculateOptimalTiling(const TensorShape& inputShape, 
                                      const HardwareInfo& hwInfo) {
        TilingConfig config;
        
        // 基于输入形状的启发式分块
        uint32_t totalElements = inputShape.getNumElements();
        uint32_t recommendedTileSize = calculateBaseTileSize(totalElements);
        
        // 考虑硬件约束调整
        config.tileSize = adjustForHardwareLimits(recommendedTileSize, hwInfo);
        
        // 计算分块数量
        config.numTiles = (totalElements + config.tileSize - 1) / config.tileSize;
        
        // 考虑多核负载均衡
        config.numTiles = adjustForLoadBalancing(config.numTiles, hwInfo.getCoreCount());
        
        // 双缓冲优化
        config.useDoubleBuffering = shouldUseDoubleBuffering(totalElements, hwInfo);
        config.bufferFactor = config.useDoubleBuffering ? 2 : 1;
        
        return config;
    }
    
private:
    uint32_t calculateBaseTileSize(uint32_t totalElements) {
        // 经验公式：基于数据量的分块策略
        if (totalElements <= 1024) {
            return 64;   // 小数据量，小分块
        } else if (totalElements <= 65536) {
            return 256;  // 中等数据量
        } else {
            return 1024; // 大数据量，大分块减少开销
        }
    }
    
    uint32_t adjustForHardwareLimits(uint32_t tileSize, const HardwareInfo& hwInfo) {
        // 考虑UB容量限制（256KB）
        uint32_t maxTileSize = hwInfo.getUBCapacity() / (sizeof(half) * 3); // 输入输出各一份
        
        // 考虑内存对齐要求
        uint32_t alignedTileSize = (tileSize + 31) / 32 * 32; // 32元素对齐
        
        return std::min(alignedTileSize, maxTileSize);
    }
};

​

// add_custom.cpp - 完整的AddCustom算子实现
#include "kernel_operator.h"
using namespace AscendC;

// 核函数实现
extern "C" __global__ __aicore__ void add_custom(GM_ADDR x, GM_ADDR y, GM_ADDR z, 
                                               GM_ADDR workspace, GM_ADDR tiling) {
    // 获取Tiling参数
    GET_TILING_DATA(tilingData, tiling);
    
    // 初始化算子实例
    KernelAdd op;
    op.Init(x, y, z, tilingData.totalLength, tilingData.tileNum);
    
    if (TILING_KEY_IS(1)) {
        op.Process();
    }
}

// Host侧实现
class AddCustomOp {
public:
    // 算子初始化
    bool Initialize() {
        // 初始化ACL环境
        aclError ret = aclInit(nullptr);
        if (ret != ACL_SUCCESS) {
            printf("Failed to initialize ACL: %d\n", ret);
            return false;
        }
        
        // 设置设备
        ret = aclrtSetDevice(0);
        if (ret != ACL_SUCCESS) {
            printf("Failed to set device: %d\n", ret);
            aclFinalize();
            return false;
        }
        
        // 创建上下文和流
        ret = aclrtCreateContext(&context_, 0);
        if (ret != ACL_SUCCESS) {
            printf("Failed to create context: %d\n", ret);
            aclrtResetDevice(0);
            aclFinalize();
            return false;
        }
        
        ret = aclrtCreateStream(&stream_);
        if (ret != ACL_SUCCESS) {
            printf("Failed to create stream: %d\n", ret);
            aclrtDestroyContext(context_);
            aclrtResetDevice(0);
            aclFinalize();
            return false;
        }
        
        initialized_ = true;
        return true;
    }
    
    // 执行算子
    bool Compute(const std::vector<half>& input1, 
                 const std::vector<half>& input2,
                 std::vector<half>& output) {
        if (!initialized_) return false;
        
        size_t data_size = input1.size() * sizeof(half);
        
        // 分配设备内存
        half* device_input1 = nullptr;
        half* device_input2 = nullptr;  
        half* device_output = nullptr;
        
        aclError ret = aclrtMalloc((void**)&device_input1, data_size, ACL_MEM_MALLOC_HUGE_FIRST);
        if (ret != ACL_SUCCESS) {
            printf("Failed to malloc device input1: %d\n", ret);
            return false;
        }
        
        ret = aclrtMalloc((void**)&device_input2, data_size, ACL_MEM_MALLOC_HUGE_FIRST);
        if (ret != ACL_SUCCESS) {
            printf("Failed to malloc device input2: %d\n", ret);
            aclrtFree(device_input1);
            return false;
        }
        
        ret = aclrtMalloc((void**)&device_output, data_size, ACL_MEM_MALLOC_HUGE_FIRST);
        if (ret != ACL_SUCCESS) {
            printf("Failed to malloc device output: %d\n", ret);
            aclrtFree(device_input1);
            aclrtFree(device_input2);
            return false;
        }
        
        // 拷贝数据到设备
        ret = aclrtMemcpy(device_input1, data_size, input1.data(), data_size, 
                         ACL_MEMCPY_HOST_TO_DEVICE);
        if (ret != ACL_SUCCESS) {
            printf("Failed to copy input1 to device: %d\n", ret);
            goto cleanup;
        }
        
        ret = aclrtMemcpy(device_input2, data_size, input2.data(), data_size, 
                         ACL_MEMCPY_HOST_TO_DEVICE);
        if (ret != ACL_SUCCESS) {
            printf("Failed to copy input2 to device: %d\n", ret);
            goto cleanup;
        }
        
        // 准备Tiling参数
        TilingData tiling_data;
        tiling_data.totalLength = input1.size();
        tiling_data.tileNum = 8;  // 经验值
        
        // 执行核函数
        add_custom<<<8, stream>>>(device_input1, device_input2, device_output, 
                                nullptr, &tiling_data);
        
        // 同步流
        ret = aclrtSynchronizeStream(stream_);
        if (ret != ACL_SUCCESS) {
            printf("Failed to synchronize stream: %d\n", ret);
            goto cleanup;
        }
        
        // 拷贝结果回主机
        output.resize(input1.size());
        ret = aclrtMemcpy(output.data(), data_size, device_output, data_size,
                         ACL_MEMCPY_DEVICE_TO_HOST);
        if (ret != ACL_SUCCESS) {
            printf("Failed to copy output to host: %d\n", ret);
            goto cleanup;
        }
        
    cleanup:
        aclrtFree(device_input1);
        aclrtFree(device_input2);
        aclrtFree(device_output);
        return ret == ACL_SUCCESS;
    }
    
private:
    bool initialized_ = false;
    aclrtContext context_ = nullptr;
    aclrtStream stream_ = nullptr;
};

// 测试函数
int main() {
    AddCustomOp op;
    if (!op.Initialize()) {
        printf("Failed to initialize operator\n");
        return -1;
    }
    
    // 准备测试数据
    const int data_size = 1024;
    std::vector<half> input1(data_size, half(1.0f));
    std::vector<half> input2(data_size, half(2.0f));
    std::vector<half> output(data_size);
    
    // 执行计算
    if (!op.Compute(input1, input2, output)) {
        printf("Computation failed\n");
        return -1;
    }
    
    // 验证结果
    bool correct = true;
    for (int i = 0; i < data_size; ++i) {
        float expected = 3.0f;  // 1.0 + 2.0
        float actual = static_cast<float>(output[i]);
        if (std::abs(actual - expected) > 1e-3) {
            printf("Result verification failed at index %d: expected %f, got %f\n", 
                   i, expected, actual);
            correct = false;
            break;
        }
    }
    
    if (correct) {
        printf("Test passed!\n");
    }
    
    return 0;
}

#!/bin/bash
# setup_environment.sh - Ascend C开发环境配置脚本

echo "开始配置Ascend C开发环境..."

# 1. 检查基础环境
if [ ! -d "/usr/local/Ascend" ]; then
    echo "错误: CANN未正确安装"
    exit 1
fi

# 2. 加载CANN环境变量
source /usr/local/Ascend/ascend-toolkit/set_env.sh

# 3. 检查CANN版本
CANN_VERSION=$(cat /usr/local/Ascend/ascend-toolkit/latest/version.info)
echo "CANN版本: $CANN_VERSION"

# 4. 设置项目环境变量
export ASCEND_CUSTOM_PATH=$HOME/Ascend/ascend-toolkit/latest
export ASCEND_HOME_DIR=$HOME/Ascend/ascend-toolkit/latest

# 5. 将Ascend C编译器添加到PATH
export PATH=$ASCEND_CUSTOM_PATH/compiler/ccec_compiler/bin:$PATH

# 6. 验证环境
python3 -c "
import torch
import torch_npu
print('✅ PyTorch环境验证成功')

if torch.npu.is_available():
    print('✅ NPU设备可用')
    print(f'设备数量: {torch.npu.device_count()}')
    for i in range(torch.npu.device_count()):
        print(f'设备{i}: {torch.npu.get_device_name(i)}')
else:
    print('❌ NPU设备不可用')
"

echo "开发环境配置完成"

#!/bin/bash
# create_operator_project.sh

# 创建算子原型定义文件
cat > add_custom.json << EOF
[
{
"op": "AddCustom",
"input_desc": [
{
"name": "x",
"param_type": "required",
"format": ["ND"],
"type": ["fp16"]
},
{
"name": "y", 
"param_type": "required",
"format": ["ND"],
"type": ["fp16"]
}
],
"output_desc": [
{
"name": "z",
"param_type": "required", 
"format": ["ND"],
"type": ["fp16"]
}
]
}
]
EOF

# 使用msopgen生成工程
$ASCEND_CUSTOM_PATH/python/site-packages/bin/msopgen gen -i ./add_custom.json \
    -c ai_core-ascend910b \
    -lan cpp \
    -out ./AddCustom
    
echo "算子工程创建完成"

graph TD
    A[AddCustom工程根目录] --> B[build.sh 编译脚本]
    A --> C[CMakeLists.txt 构建配置]
    A --> D[op_kernel/ 核函数实现]
    A --> E[op_host/ Host侧代码]
    A --> F[scripts/ 工具脚本]
    
    D --> D1[add_custom.cpp 核函数]
    E --> E1[add_custom.cpp Host函数]
    E --> E2[add_custom_tiling.h Tiling定义]
    
    F --> F1[部署脚本]
    F --> F2[测试脚本]

# performance_analysis.py - 性能分析脚本
import subprocess
import pandas as pd
import matplotlib.pyplot as plt

class AscendCAnalyzer:
    def __init__(self, operator_path):
        self.operator_path = operator_path
        self.performance_data = []
    
    def run_performance_test(self, input_sizes=[256, 1024, 4096, 16384]):
        """运行性能测试"""
        results = []
        
        for size in input_sizes:
            print(f"测试数据规模: {size}")
            
            # 编译算子
            compile_result = self.compile_operator()
            if not compile_result:
                continue
                
            # 运行测试
            runtime = self.execute_operator(size)
            
            # 计算性能指标
            gflops = self.calculate_gflops(size, runtime)
            bandwidth = self.calculate_bandwidth(size, runtime)
            
            results.append({
                'size': size,
                'runtime_ms': runtime,
                'gflops': gflops,
                'bandwidth_gbs': bandwidth
            })
        
        return results
    
    def generate_report(self, results):
        """生成性能报告"""
        df = pd.DataFrame(results)
        
        # 绘制性能图表
        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
        
        # 计算吞吐量图表
        ax1.plot(df['size'], df['gflops'], 'b-o')
        ax1.set_xlabel('数据规模')
        ax1.set_ylabel('计算吞吐量 (GFLOPS)')
        ax1.set_title('计算性能随数据规模变化')
        ax1.grid(True)
        
        # 内存带宽图表
        ax2.plot(df['size'], df['bandwidth_gbs'], 'r-o')
        ax2.set_xlabel('数据规模')
        ax2.set_ylabel('内存带宽 (GB/s)')
        ax2.set_title('内存带宽使用情况')
        ax2.grid(True)
        
        plt.tight_layout()
        plt.savefig('performance_analysis.png', dpi=300)
        
        return df

// memory_debugger.cpp - 内存调试工具
class MemoryDebugger {
public:
    struct MemoryAccessPattern {
        size_t total_accesses;
        size_t out_of_bound_accesses;
        size_t misaligned_accesses;
        float error_rate;
    };
    
    MemoryAccessPattern analyzeMemoryAccess(void* ptr, size_t allocated_size, 
                                          const std::vector<size_t>& accesses) {
        MemoryAccessPattern pattern = {0, 0, 0, 0.0};
        
        for (size_t offset : accesses) {
            pattern.total_accesses++;
            
            // 检查越界访问
            if (offset >= allocated_size) {
                pattern.out_of_bound_accesses++;
                continue;
            }
            
            // 检查内存对齐（昇腾要求128位对齐）
            if ((offset % 16) != 0) {
                pattern.misaligned_accesses++;
            }
        }
        
        pattern.error_rate = static_cast<float>(pattern.out_of_bound_accesses) / 
                           pattern.total_accesses * 100.0;
        
        return pattern;
    }
    
    // 安全内存分配函数
    void* safeMalloc(size_t size, size_t alignment = 16) {
        void* ptr = nullptr;
        aclError ret = aclrtMalloc(&ptr, size, ACL_MEM_MALLOC_HUGE_FIRST);
        
        if (ret != ACL_SUCCESS) {
            printf("内存分配失败: %d\n", ret);
            return nullptr;
        }
        
        // 检查对齐
        if (reinterpret_cast<uintptr_t>(ptr) % alignment != 0) {
            printf("警告: 内存未正确对齐\n");
        }
        
        return ptr;
    }
};

// 使用示例
void debugMemoryIssues() {
    MemoryDebugger debugger;
    size_t buffer_size = 1024;
    
    // 安全分配内存
    half* device_ptr = static_cast<half*>(debugger.safeMalloc(buffer_size * sizeof(half)));
    if (!device_ptr) {
        return;
    }
    
    // 模拟访问模式
    std::vector<size_t> test_accesses = {0, 256, 512, 1024, 2048};  // 包含越界访问
    
    auto result = debugger.analyzeMemoryAccess(device_ptr, buffer_size, test_accesses);
    printf("内存访问分析: 总访问=%zu, 越界=%zu, 未对齐=%zu, 错误率=%.2f%%\n",
           result.total_accesses, result.out_of_bound_accesses, 
           result.misaligned_accesses, result.error_rate);
    
    aclrtFree(device_ptr);
}

// synchronization_manager.cpp - 同步管理
class SynchronizationManager {
public:
    struct SyncConfig {
        bool use_barrier;
        uint32_t barrier_threshold;
        SyncMode sync_mode;
    };
    
    enum SyncMode {
        SYNC_NONE,      // 无同步
        SYNC_LIGHT,     // 轻量同步
        SYNC_FULL       // 完全同步
    };
    
    void manageSynchronization(aclrtStream stream, const SyncConfig& config) {
        aclError ret = ACL_SUCCESS;
        
        switch (config.sync_mode) {
            case SYNC_NONE:
                // 无同步，最高风险也最高性能
                break;
                
            case SYNC_LIGHT:
                // 轻量同步，在关键点同步
                if (config.use_barrier) {
                    ret = aclrtBarrier(stream);
                    if (ret != ACL_SUCCESS) {
                        printf("Barrier同步失败: %d\n", ret);
                    }
                }
                break;
                
            case SYNC_FULL:
                // 完全同步，保证数据一致性
                ret = aclrtSynchronizeStream(stream);
                if (ret != ACL_SUCCESS) {
                    printf("流同步失败: %d\n", ret);
                }
                break;
        }
    }
    
    // 检测死锁
    bool detectDeadlock(const std::vector<aclrtStream>& streams, uint32_t timeout_ms = 5000) {
        auto start_time = std::chrono::steady_clock::now();
        
        for (auto stream : streams) {
            aclError ret = aclrtSynchronizeStreamWithTimeout(stream, timeout_ms);
            if (ret == ACL_ERROR_RT_WAIT_TIMEOUT) {
                printf("检测到死锁: 流 %p 超时\n", stream);
                return true;
            }
        }
        
        auto end_time = std::chrono::steady_clock::now();
        auto duration = std::chrono::duration_cast<std::chrono::milliseconds>(
            end_time - start_time);
            
        printf("同步完成，耗时: %ld ms\n", duration.count());
        return false;
    }
};

// embedding_retrieval_optimized.cpp
class OptimizedEmbeddingRetrieval {
public:
    struct PerformanceMetrics {
        double latency_ms;
        double throughput_qps;
        double accuracy;
    };
    
    PerformanceMetrics optimizeRetrieval(const std::vector<float>& query_embeddings,
                                       const std::vector<float>& item_embeddings,
                                       int top_k) {
        PerformanceMetrics metrics = {0, 0, 0};
        
        // 1. 数据重排优化缓存局部性
        auto reordered_embeddings = optimizeDataLayout(item_embeddings);
        
        // 2. 基于数据分布的动态Tiling
        auto tiling_strategy = calculateAdaptiveTiling(query_embeddings.size(), 
                                                     item_embeddings.size());
        
        // 3. 多核并行相似度计算
        auto results = parallelSimilarityCalculation(query_embeddings, 
                                                   reordered_embeddings,
                                                   tiling_strategy);
        
        // 4. 高效Top-K选择
        auto top_k_results = optimizedTopKSelection(results, top_k);
        
        metrics.latency_ms = measureLatency();
        metrics.throughput_qps = calculateThroughput();
        metrics.accuracy = validateAccuracy(top_k_results);
        
        return metrics;
    }
    
private:
    std::vector<float> optimizeDataLayout(const std::vector<float>& embeddings) {
        // 数据块重排，提高缓存命中率
        std::vector<float> reordered(embeddings.size());
        
        const int block_size = 64;  // 缓存行友好
        int num_blocks = embeddings.size() / block_size;
        
        for (int i = 0; i < num_blocks; ++i) {
            for (int j = 0; j < block_size; ++j) {
                reordered[i * block_size + j] = embeddings[j * num_blocks + i];
            }
        }
        
        return reordered;
    }
};

// attention_mechanism_optimized.cpp
class OptimizedAttention {
public:
    void optimizedSelfAttention(GM_ADDR query, GM_ADDR key, GM_ADDR value,
                              GM_ADDR output, int batch_size, int seq_len, 
                              int hidden_size, int head_size) {
        // 1. 分块矩阵乘法优化
        auto qk_matmul = tiledMatrixMultiplication(query, key, batch_size, 
                                                  seq_len, hidden_size, head_size);
        
        // 2. 软核化Softmax，避免精度损失
        auto attention_weights = softmaxOptimized(qk_matmul, seq_len);
        
        // 3. 融合注意力权重与Value的乘法
        auto attention_output = fusedAttentionMatmul(attention_weights, value);
        
        // 4. 输出投影与残差连接融合
        fusedOutputProjection(attention_output, output);
    }
    
private:
    GM_ADDR tiledMatrixMultiplication(GM_ADDR a, GM_ADDR b, int m, int n, int k) {
        // 基于硬件特性的分块矩阵乘法
        const int tile_m = 64;  // 适配Cube单元
        const int tile_n = 64;
        const int tile_k = 128; // 数据重用优化
        
        for (int i = 0; i < m; i += tile_m) {
            for (int j = 0; j < n; j += tile_n) {
                for (int k = 0; k < k; k += tile_k) {
                    // 分块矩阵乘法核心逻辑
                    processMatrixTile(a, b, i, j, k, tile_m, tile_n, tile_k);
                }
            }
        }
    }
};

// memory_access_optimizer.cpp
class MemoryAccessOptimizer {
public:
    struct AccessPattern {
        float cache_hit_rate;
        float bank_conflict_rate;
        float memory_efficiency;
    };
    
    AccessPattern analyzeAccessPattern(const std::vector<size_t>& accesses, 
                                     size_t working_set_size) {
        AccessPattern pattern = {0, 0, 0};
        
        // 模拟缓存行为分析
        size_t cache_hits = simulateCacheBehavior(accesses, working_set_size);
        pattern.cache_hit_rate = static_cast<float>(cache_hits) / accesses.size();
        
        // 存储体冲突分析
        pattern.bank_conflict_rate = analyzeBankConflicts(accesses);
        
        // 内存效率计算
        pattern.memory_efficiency = calculateMemoryEfficiency(pattern);
        
        return pattern;
    }
    
    void optimizeAccessPattern(std::vector<size_t>& accesses, 
                             const AccessPattern& pattern) {
        if (pattern.cache_hit_rate < 0.6) {
            // 低缓存命中率，优化数据局部性
            improveDataLocality(accesses);
        }
        
        if (pattern.bank_conflict_rate > 0.3) {
            // 高存储体冲突，优化访问模式
            reduceBankConflicts(accesses);
        }
    }
    
private:
    void improveDataLocality(std::vector<size_t>& accesses) {
        // 数据访问重排序，提高空间局部性
        std::sort(accesses.begin(), accesses.end());
        
        // 添加预取提示
        for (size_t i = 0; i < accesses.size() - 1; ++i) {
            if (accesses[i + 1] - accesses[i] < 256) {
                // 添加预取指令
                prefetchHint(accesses[i + 1]);
            }
        }
    }
};

// compute_balancer.cpp
class ComputeResourceBalancer {
public:
    struct WorkloadDistribution {
        float cube_utilization;
        float vector_utilization;
        float load_imbalance;
    };
    
    WorkloadDistribution analyzeWorkload(const ComputeTask& task) {
        WorkloadDistribution dist = {0, 0, 0};
        
        // 分析计算任务特性
        auto task_profile = profileComputeTask(task);
        
        // Cube单元利用率
        dist.cube_utilization = task_profile.matrix_ops / task_profile.total_ops;
        
        // Vector单元利用率  
        dist.vector_utilization = task_profile.vector_ops / task_profile.total_ops;
        
        // 负载不均衡度
        dist.load_imbalance = calculateLoadImbalance(task_profile);
        
        return dist;
    }
    
    void balanceWorkload(ComputeTask& task, const WorkloadDistribution& dist) {
        if (dist.load_imbalance > 0.2) {
            // 负载不均衡，重新分配任务
            redistributeWorkload(task);
        }
        
        if (dist.cube_utilization < 0.3 && dist.vector_utilization > 0.7) {
            // Vector单元过载，将部分任务迁移到Cube单元
            migrateVectorToCube(task);
        }
    }
};

// systematic_debugger.cpp
class AscendCDebugger {
public:
    struct DebugScenario {
        std::string issue;
        std::function<bool()> detector;
        std::function<void()> resolver;
        int priority; // 1-10，10最高
    };
    
    void initializeDebugScenarios() {
        scenarios_ = {
            {"内存分配失败", 
             []() { return detectMemoryAllocationFailure(); },
             []() { resolveMemoryAllocation(); }, 9},
             
            {"核函数执行超时",
             []() { return detectKernelTimeout(); },
             []() { resolveKernelTimeout(); }, 10},
             
            {"数据精度异常",
             []() { return detectNumericalError(); },
             []() { fixNumericalPrecision(); }, 8},
             
            {"多核同步失败",
             []() { return detectSyncFailure(); },
             []() { fixSynchronization(); }, 7},
             
            {"性能不达标",
             []() { return detectPerformanceIssue(); },
             []() { optimizePerformance(); }, 6}
        };
    }
    
    void runComprehensiveDiagnosis() {
        std::vector<std::string> issues_found;
        
        // 按优先级排序
        std::sort(scenarios_.begin(), scenarios_.end(),
                 [](const DebugScenario& a, const DebugScenario& b) {
                     return a.priority > b.priority;
                 });
        
        for (const auto& scenario : scenarios_) {
            if (scenario.detector()) {
                issues_found.push_back(scenario.issue);
                scenario.resolver();
            }
        }
        
        generateDiagnosticReport(issues_found);
    }
    
private:
    bool detectMemoryAllocationFailure() {
        // 检查内存分配错误
        aclError ret = aclrtGetLastError();
        return ret == ACL_ERROR_RT_MEMORY_ALLOCATION;
    }
    
    void resolveMemoryAllocation() {
        // 内存分配问题解决方案
        printf("检测到内存分配失败，尝试以下解决方案:\n");
        printf("1. 检查设备内存是否充足\n");
        printf("2. 尝试减少单次分配大小\n");
        printf("3. 使用内存池优化分配策略\n");
    }
    
    void generateDiagnosticReport(const std::vector<std::string>& issues) {
        printf("=== Ascend C诊断报告 ===\n");
        printf("发现的问题数量: %zu\n", issues.size());
        
        for (size_t i = 0; i < issues.size(); ++i) {
            printf("%zu. %s\n", i + 1, issues[i].c_str());
        }
        
        if (issues.empty()) {
            printf("✅ 未发现明显问题\n");
        } else {
            printf("⚠️ 请根据上述问题逐一排查\n");
        }
    }
    
    std::vector<DebugScenario> scenarios_;
};