micarrsim/apps/acoustic-camera/simulation-worker.js

import { synthesizeMicrophoneSignals } from "/packages/acoustics-core/propagation.js";
import { sampleQuasiStaticWindow } from "/packages/acoustics-core/trajectory.js";
import { createLocalizationAnalysisPlan } from "/packages/acoustics-core/localization.js";
import { computeLocalizationWithBackend } from "/packages/webgpu-kernels/compute.js";

function post(type, jobId, payload = {}, transfer = []) {
  self.postMessage({ type, jobId, ...payload }, transfer);
}

function serializeError(error) {
  return {
    name: error?.name ?? "Error",
    code: error?.code ?? "",
    message: error instanceof Error ? error.message : String(error),
    stack: error?.stack ?? "",
    causes: error instanceof AggregateError
      ? error.errors.map((cause) => ({ name: cause?.name, code: cause?.code, message: cause?.message }))
      : [],
  };
}

function signalStats(synthesis) {
  return {
    preNormalizationPeak: synthesis.preNormalizationPeak,
    appliedScale: synthesis.appliedScale,
    clippedSampleCount: synthesis.clippedSampleCount,
    peak: synthesis.peak,
    acousticModel: synthesis.acousticModel,
    acousticPathCount: synthesis.acousticPathCount,
    acousticPathCounts: synthesis.acousticPathCounts,
    workingSet: synthesis.workingSet,
    windowing: synthesis.windowing,
    sampling: synthesis.sampling,
  };
}

function collectBandTransfers(run, transfer) {
  for (const band of run.bands) {
    transfer.push(band.rawMap.buffer, band.displayMap.buffer);
  }
}

async function runTimeline({ jobId, scene, arrayDefinition, experiment, assetBuffers, framePlan }) {
  if (!Array.isArray(framePlan?.frames) || framePlan.frames.length === 0) {
    throw new TypeError("framePlan.frames must be a non-empty array.");
  }

  const totalStartedAt = performance.now();
  const totalFrames = framePlan.frames.length;
  const frames = [];
  const transfer = [];
  let signalGeneration = 0;
  let localizationTime = 0;
  const localizationScene = { speedOfSound: scene.speedOfSound };
  const algorithm = experiment.beamforming.algorithm ?? "das";
  const requestedBackend = experiment.beamforming.backend ?? "auto";
  const reuseCpuPlan = algorithm === "gcc-phat" || requestedBackend === "cpu";
  let localizationExperiment = experiment;
  let analysisPlan = reuseCpuPlan
    ? createLocalizationAnalysisPlan({
        sampleRate: scene.sampleRate,
        arrayDefinition,
        scene: localizationScene,
        experiment: localizationExperiment,
      })
    : undefined;

  post("progress", jobId, {
    stage: "timeline-start",
    message: `라이브 맵 ${totalFrames}개를 준비하고 있습니다…`,
    completedFrames: 0,
    totalFrames,
  });

  for (let frameIndex = 0; frameIndex < totalFrames; frameIndex += 1) {
    const plannedFrame = framePlan.frames[frameIndex];
    const synthesisWindow = sampleQuasiStaticWindow(
      scene,
      plannedFrame.startTime,
      plannedFrame.endTime,
      {
        arrayDefinition,
        assetBuffers,
        minimumDistance: experiment.beamforming.minimumDistance,
      },
    );
    post("progress", jobId, {
      stage: "timeline-signal",
      message: `라이브 신호 합성 중 · ${frameIndex + 1}/${totalFrames}`,
      frameIndex,
      completedFrames: frameIndex,
      totalFrames,
    });

    const frameStartedAt = performance.now();
    const signalStartedAt = performance.now();
    const synthesis = synthesizeMicrophoneSignals({
      scene: synthesisWindow.scene,
      arrayDefinition,
      assetBuffers,
      minimumDistance: experiment.beamforming.minimumDistance,
      signalOffsets: synthesisWindow.signalOffsets,
    });
    const frameSignalGeneration = performance.now() - signalStartedAt;
    signalGeneration += frameSignalGeneration;

    const localizationStartedAt = performance.now();
    const localization = await computeLocalizationWithBackend({
      channels: synthesis.channels,
      sampleRate: synthesis.sampleRate,
      arrayDefinition,
      scene: localizationScene,
      experiment: localizationExperiment,
      ...(analysisPlan ? { analysisPlan } : {}),
      onFallback: ({ error }) =>
        post("fallback", jobId, {
          code: error.code,
          message: error.message,
          frameIndex,
          totalFrames,
        }),
      onProgress: (progress) =>
        post("progress", jobId, {
          stage: `timeline-${progress.stage}`,
          frameIndex,
          completedFrames: frameIndex,
          totalFrames,
          progress,
        }),
    });
    const frameLocalization = performance.now() - localizationStartedAt;
    localizationTime += frameLocalization;
    collectBandTransfers(localization, transfer);

    if (
      !analysisPlan &&
      localization.backend === "cpu" &&
      localization.fallback?.from === "webgpu"
    ) {
      // Auto has established that WebGPU is unavailable for this worker. Keep
      // the first frame's honest fallback metadata, then avoid retrying the GPU
      // and rebuilding CPU geometry/workspaces on every remaining live frame.
      localizationExperiment = {
        ...experiment,
        beamforming: {
          ...experiment.beamforming,
          backend: "cpu",
        },
      };
      analysisPlan = createLocalizationAnalysisPlan({
        sampleRate: scene.sampleRate,
        arrayDefinition,
        scene: localizationScene,
        experiment: localizationExperiment,
      });
    }

    frames.push({
      index: plannedFrame.index ?? frameIndex,
      startTime: synthesisWindow.startTime,
      endTime: synthesisWindow.endTime,
      sampleTime: synthesisWindow.sampleTime,
      duration: synthesisWindow.duration,
      run: {
        ...localization,
        signalStats: signalStats(synthesis),
        timingMs: {
          total: performance.now() - frameStartedAt,
          signalGeneration: frameSignalGeneration,
          localization: frameLocalization,
          beamforming: frameLocalization,
        },
      },
    });

    post("progress", jobId, {
      stage: "timeline-frame-complete",
      message: `라이브 맵 준비 · ${frameIndex + 1}/${totalFrames}`,
      frameIndex,
      completedFrames: frameIndex + 1,
      totalFrames,
    });
  }

  const backends = new Set(frames.map((frame) => frame.run.backend));
  const fallbacks = frames
    .filter((frame) => frame.run.fallback)
    .map((frame) => ({ frameIndex: frame.index, ...frame.run.fallback }));
  post(
    "timeline-result",
    jobId,
    {
      timeline: {
        backend: backends.size === 1 ? frames[0].run.backend : "mixed",
        algorithm: experiment.beamforming.algorithm ?? "das",
        requestedBackend: experiment.beamforming.backend ?? "auto",
        duration: scene.duration,
        frameCount: frames.length,
        frames,
        ...(fallbacks.length > 0 ? { fallbacks } : {}),
        timingMs: {
          total: performance.now() - totalStartedAt,
          signalGeneration,
          localization: localizationTime,
          beamforming: localizationTime,
        },
      },
    },
    transfer,
  );
}

self.addEventListener("message", async (event) => {
  if (event.data?.type !== "run" && event.data?.type !== "run-timeline") return;
  const { jobId, scene, arrayDefinition, experiment, assetBuffers } = event.data;
  const totalStartedAt = performance.now();
  try {
    if (event.data.type === "run-timeline") {
      await runTimeline(event.data);
      return;
    }
    post("progress", jobId, { stage: "signal", message: "마이크 수신 신호를 합성하고 있습니다…" });
    const signalStartedAt = performance.now();
    const synthesis = synthesizeMicrophoneSignals({
      scene,
      arrayDefinition,
      assetBuffers,
      minimumDistance: experiment.beamforming.minimumDistance,
    });
    const signalGeneration = performance.now() - signalStartedAt;
    post("progress", jobId, {
      stage: "signal-complete",
      message: `마이크 신호 합성 완료 · ${signalGeneration.toFixed(0)} ms`,
    });

    const localizationStartedAt = performance.now();
    const localization = await computeLocalizationWithBackend({
      channels: synthesis.channels,
      sampleRate: synthesis.sampleRate,
      arrayDefinition,
      scene,
      experiment,
      onFallback: ({ error }) =>
        post("fallback", jobId, {
          code: error.code,
          message: error.message,
        }),
      onProgress: (progress) => post("progress", jobId, { stage: progress.stage, progress }),
    });
    const localizationTime = performance.now() - localizationStartedAt;
    const transfer = [];
    collectBandTransfers(localization, transfer);
    post(
      "result",
      jobId,
      {
        run: {
          ...localization,
          signalStats: signalStats(synthesis),
          timingMs: {
            total: performance.now() - totalStartedAt,
            signalGeneration,
            localization: localizationTime,
            beamforming: localizationTime,
          },
        },
      },
      transfer,
    );
  } catch (error) {
    post("error", jobId, { error: serializeError(error) });
  }
});