​​​​HarmonyOS 5 异构计算调度测试：自动分配CNN层到NPU/GPU/CPU的负载均衡

以下为 ​​HarmonyOS 5 异构计算调度测试方案​​，实现CNN模型分层自动分配与负载均衡的完整代码实现：

---

1. 系统架构

---

2. 核心调度模块

2.1 智能层分配器

// layer-scheduler.ets
class HeterogeneousScheduler {
  static async partitionModel(model: CNNModel): Promise<LayerAssignment[]> {
    const layerMetrics = await Profiler.profileLayers(model);
    const deviceCapabilities = await DeviceMonitor.getCapabilities();
    
    return layerMetrics.map(layer => {
      // NPU优先策略
      if (layer.ops.includes('Conv2D') && 
          deviceCapabilities.npu.ops.includes(layer.type)) {
        return { layer, device: 'NPU' };
      }
      
      // GPU适合大矩阵运算
      if (layer.flops > 1e6 && deviceCapabilities.gpu.memory > layer.memReq) {
        return { layer, device: 'GPU' };
      }
      
      // 默认CPU处理
      return { layer, device: 'CPU' };
    });
  }
}

2.2 实时负载均衡器

// load-balancer.ets
class DynamicLoadBalancer {
  private static deviceLoads = { NPU: 0, GPU: 0, CPU: 0 };
  
  static async adjustAllocation(task: LayerTask): Promise<string> {
    const currentLoad = await DeviceMonitor.getCurrentLoad();
    const capabilities = DeviceMonitor.getCapabilities();
    
    // 负载均衡算法
    const scores = {
      NPU: this.calculateScore('NPU', task, currentLoad, capabilities),
      GPU: this.calculateScore('GPU', task, currentLoad, capabilities),
      CPU: this.calculateScore('CPU', task, currentLoad, capabilities)
    };
    
    return Object.entries(scores).reduce((a, b) => 
      a[1] > b[1] ? a : b
    )[0];
  }
  
  private static calculateScore(
    device: DeviceType,
    task: LayerTask,
    load: DeviceLoad,
    caps: DeviceCapabilities
  ): number {
    const perfScore = caps[device].peakPerf / task.requiredFlops;
    const loadPenalty = load[device] / 100;
    const memScore = caps[device].memory / task.memReq;
    
    return perfScore * (1 - loadPenalty) * Math.min(memScore, 1);
  }
}

---

3. 设备适配层

3.1 NPU加速器

// npu-executor.ets
class NPUAccelerator {
  static async execute(layer: Layer, input: Tensor): Promise<Tensor> {
    const kernel = await NPUKernelCompiler.compile(layer);
    return NPURuntime.execute(kernel, input, {
      priority: 'HIGH',
      memoryPolicy: 'REUSE'
    });
  }
}

3.2 GPU执行器

// gpu-executor.ets
class GPUOffloader {
  static async process(layer: Layer, input: Tensor): Promise<Tensor> {
    const stream = await GPUContext.createStream();
    const buffer = await GPUMemory.alloc(layer.outputShape);
    
    return GPUProgram.run(
      'cnn_kernel', 
      input, 
      buffer, 
      { 
        threadPerBlock: [16, 16],
        blocks: this.calcBlocks(layer.outputShape) 
      }
    );
  }
}

---

4. 性能监控系统

4.1 实时指标采集

// performance-monitor.ets
class DeviceMonitor {
  static async getCurrentLoad(): Promise<DeviceLoad> {
    return {
      NPU: await this.queryNPULoad(),
      GPU: await this.queryGPULoad(),
      CPU: await this.queryCPULoad()
    };
  }
  
  private static async queryNPULoad(): Promise<number> {
    const stats = await NPUDriver.getStats();
    return stats.activeCores / stats.totalCores;
  }
}

4.2 历史数据分析

// history-analyzer.ets
class PerformanceAnalyzer {
  static async findBottleneck(layerLogs: LayerLog[]): Promise<Bottleneck[]> {
    const avgTimes = layerLogs.reduce((acc, log) => {
      if (!acc[log.layerId]) acc[log.layerId] = [];
      acc[log.layerId].push(log.duration);
      return acc;
    }, {});
    
    return Object.entries(avgTimes).map(([layerId, times]) => ({
      layerId,
      avgTime: average(times),
      stdDev: standardDeviation(times),
      device: layerLogs.find(l => l.layerId === layerId)!.device
    })).filter(x => x.avgTime > 10); // 超过10ms视为瓶颈
  }
}

---

5. 测试验证框架

5.1 分配策略验证

// allocation-test.ets
describe('异构分配策略', () => {
  let model: CNNModel;
  
  beforeAll(async () => {
    model = await ModelLoader.load('mobilenet_v3');
  });
  
  it('Conv层应优先分配NPU', async () => {
    const assignments = await HeterogeneousScheduler.partitionModel(model);
    const convLayers = assignments.filter(a => 
      a.layer.type === 'Conv2D'
    );
    
    expect(convLayers.every(a => a.device === 'NPU')).toBeTruthy();
  });
  
  it('负载过高时应动态迁移', async () => {
    // 模拟NPU过载
    DeviceMonitor.mockLoad({ NPU: 95, GPU: 40, CPU: 60 });
    const task = { layer: model.layers[0], requiredFlops: 1e9, memReq: 500 };
    
    const targetDevice = await DynamicLoadBalancer.adjustAllocation(task);
    expect(targetDevice).toBe('GPU');
  });
});

5.2 端到端性能测试

// e2e-test.ets
class EndToEndTester {
  static async testModel(model: CNNModel) {
    // 1. 初始分配
    const assignments = await HeterogeneousScheduler.partitionModel(model);
    
    // 2. 执行推理
    const input = Tensor.random(model.inputShape);
    const results = await Promise.all(
      assignments.map(async ({ layer, device }) => {
        const start = performance.now();
        const output = await this.executeOnDevice(layer, input, device);
        return { layerId: layer.id, device, duration: performance.now() - start };
      })
    );
    
    // 3. 分析性能
    return PerformanceAnalyzer.analyze(results);
  }
}

---

6. 可视化监控面板

6.1 实时负载仪表盘

// load-dashboard.ets
@Component
struct LoadDashboard {
  @State loadData: DeviceLoad[] = [];
  
  build() {
    Grid() {
      GridItem() {
        Gauge({
          value: this.loadData.NPU,
          title: 'NPU负载',
          color: '#ff6384'
        })
      }
      GridItem() {
        LineChart({
          data: this.loadData.map((_, i) => ({
            x: i,
            y: [this.loadData[i].NPU, this.loadData[i].GPU, this.loadData[i].CPU]
          })),
          series: ['NPU', 'GPU', 'CPU']
        })
      }
    }
    .onAppear(() => {
      setInterval(async () => {
        this.loadData.push(await DeviceMonitor.getCurrentLoad());
      }, 1000);
    })
  }
}

6.2 层执行热力图

// layer-heatmap.ets
@Component
struct LayerHeatmap {
  @Prop layerMetrics: LayerMetric[];
  
  build() {
    Heatmap({
      data: this.layerMetrics.map(metric => ({
        x: metric.layerId,
        y: metric.device,
        value: metric.duration
      })),
      xLabel: '网络层',
      yLabel: '执行设备',
      colorScale: ['#00ff00', '#ff0000']
    })
  }
}

---

7. 关键性能指标

指标	测量方法	目标值
设备利用率	活跃计算单元占比	NPU>80%
层分配合理性	关键层加速比	Conv2D≥3x
负载均衡度	设备间负载标准差	<15%
端到端延迟	全流程执行时间	<100ms

---

8. 高级测试场景

8.1 突发负载测试

// burst-test.ets
class BurstLoadTest {
  static async simulatePeakLoad() {
    // 并行执行多个模型
    const models = await Promise.all([
      ModelLoader.load('resnet50'),
      ModelLoader.load('yolov5'),
      ModelLoader.load('bert')
    ]);
    
    const results = await Promise.all(
      models.map(model => 
        HeterogeneousExecutor.run(model, randomInput())
      )
    );
    
    // 验证无死锁和超时
    expect(results.every(r => !r.error)).toBeTruthy();
    expect(DeviceMonitor.getMaxLoad().CPU).toBeLessThan(95);
  }
}

8.2 故障转移测试

// failover-test.ets
describe('设备故障转移', () => {
  it('NPU故障时应自动降级', async () => {
    // 模拟NPU故障
    NPUDevice.simulateFailure();
    const model = await ModelLoader.load('efficientnet');
    
    const assignments = await HeterogeneousScheduler.partitionModel(model);
    expect(assignments.some(a => 
      a.layer.type === 'Conv2D' && a.device === 'GPU'
    )).toBeTruthy();
  });
});

---

9. 调度优化建议

9.1 智能重分配建议

// rebalancer.ets
class AutoRebalancer {
  static async optimize(model: CNNModel): Promise<ReallocPlan> {
    const history = await PerformanceDB.queryHistory(model.name);
    const bottlenecks = PerformanceAnalyzer.findBottleneck(history);
    
    return bottlenecks.map(bottleneck => {
      const alternatives = ['NPU', 'GPU', 'CPU'].filter(d => d !== bottleneck.device);
      const scores = alternatives.map(device => ({
        device,
        score: this.calculateGain(bottleneck, device)
      }));
      
      return {
        layerId: bottleneck.layerId,
        currentDevice: bottleneck.device,
        recommended: scores.reduce((a, b) => a.score > b.score ? a : b).device
      };
    });
  }
}

9.2 内存优化策略

// memory-optimizer.ets
class MemoryOptimizer {
  static async optimizeAllocation(plan: LayerAssignment[]): Promise<LayerAssignment[]> {
    const deviceMemory = await DeviceMonitor.getAvailableMemory();
    return plan.map(assignment => {
      if (assignment.layer.memReq > deviceMemory[assignment.device] * 0.8) {
        return this.findAlternative(assignment, deviceMemory);
      }
      return assignment;
    });
  }
  
  private static findAlternative(
    original: LayerAssignment,
    memory: DeviceMemory
  ): LayerAssignment {
    const altDevices = ['NPU', 'GPU', 'CPU'].filter(d => 
      d !== original.device && 
      memory[d] >= original.layer.memReq
    );
    
    return altDevices.length > 0 ? 
      { ...original, device: altDevices[0] } : 
      original;
  }
}

---

10. 完整测试示例

10.1 性能基准测试

// benchmark.ets
async function runBenchmark() {
  const model = await ModelLoader.load('mobilenet_v3');
  const input = Tensor.random(model.inputShape);
  
  // 1. 单设备基准
  const cpuTime = await measure(() => CPUExecutor.run(model, input));
  const gpuTime = await measure(() => GPUExecutor.run(model, input));
  
  // 2. 异构调度测试
  const heteroTime = await measure(() => 
    HeterogeneousScheduler.execute(model, input)
  );
  
  return {
    cpuTime,
    gpuTime,
    heteroTime,
    speedup: {
      vsCPU: cpuTime / heteroTime,
      vsGPU: gpuTime / heteroTime
    }
  };
}

10.2 CI集成配置

# .github/workflows/hetero-test.yml
jobs:
  hetero-schedule:
    runs-on: harmonyos-multi-device
    steps:
      - uses: harmonyos/hetero-test-action@v1
        with:
          models: 'mobilenet_v3,yolov5'
          test-cases: 'normal,overload,failure'
      - name: Upload report
        uses: actions/upload-artifact@v3
        with:
          name: hetero-report
          path: report.html

---

通过本方案可实现：

1. ​​智能分层​​ 自动分配CNN计算图
2. ​​动态负载​​ 实时均衡设备利用率
3. ​​故障自愈​​ 设备异常时自动降级
4. ​​性能可视​​ 全流程执行热力图