Tantangan Transceiver ZLUDA pada GPU Non-Nvidia

Menjalankan workload inferensi CUDA pada GPU non-Nvidia (seperti AMD ROCm) menggunakan translation layer ZLUDA sering memicu masalah kestabilan runtime. Masalah utama mencakup kegagalan inisialisasi pointer driver runtime (misal hipInit() atau wrapper CUDA driver API), memory allocation fault (HIP error hipErrorInitializationError atau hipErrorLaunchFailure), hingga silent kernel panic pada host OS saat konteks instruksi GPU menggantung.

Strategi canary deployment membatasi blast radius rilis baru inference worker. Kegagalan transceiver ZLUDA dideteksi pada subset kecil instance worker sebelum rilis dipromosikan ke seluruh klaster.

Arsitektur Health Check Probe Kubernetes

Pengecekan HTTP endpoint standar tidak mencerminkan ketersediaan hardware GPU. Kita memerlukan script probe internal yang memvalidasi binding transceiver ZLUDA secara berkala tanpa memicu beban inferensi berlebih.

Konfigurasi Probe Pod Kubernetes

Konfigurasi berikut menjalankan eksekusi minimal ke layer ROCm/ZLUDA via liveness dan readiness probe.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: zluda-inference-worker-canary
  labels:
    app: zluda-worker
    track: canary
spec:
  replicas: 1
  selector:
    matchLabels:
      app: zluda-worker
      track: canary
  template:
    metadata:
      labels:
        app: zluda-worker
        track: canary
    spec:
      containers:
      - name: worker
        image: registry.internal/ai/inference-worker:v2-zluda
        resources:
          limits:
            amd.com/gpu: 1
        readinessProbe:
          exec:
            command:
            - /bin/sh
            - -c
            - "rocm-smi --showid && python3 -c 'import torch; assert torch.cuda.is_available() and torch.zeros(1).cuda() is not None'"
          initialDelaySeconds: 15
          periodSeconds: 10
          failureThreshold: 2
        livenessProbe:
          exec:
            command:
            - /bin/sh
            - -c
            - "rocm-smi --showuse | grep -v 'GPU\[.*error'"
          initialDelaySeconds: 30
          periodSeconds: 20
          failureThreshold: 3
        securityContext:
          capabilities:
            drop: ["ALL"]
          devices:
          - hostPath: /dev/kfd
          - hostPath: /dev/dri

Metrik Observability: Prometheus & Grafana Alerting

Untuk mendeteksi silent failure (misalnya transceiver mati tetapi container tetap running), ekspos metrik custom melalui Prometheus client di dalam worker, dipadukan dengan amd-gpu-exporter.

PrometheusRule untuk Deteksi Failure Rate

apiVersion: monitoring.coreos.com/v1
kind: PrometheusRule
metadata:
  name: zluda-worker-canary-alerts
  labels:
    role: alert-rules
spec:
  groups:
  - name: zluda.canary.rules
    rules:
    - alert: ZludaDriverInitializationFailure
      expr: rate(inference_zluda_init_errors_total[2m]) > 0
      for: 1m
      labels:
        severity: critical
      annotations:
        summary: "ZLUDA driver initialization failed on Canary Pod"
        description: "Canary worker {{ $labels.instance }} mendeteksi kegagalan komunikasi ROCm/CUDA transceiver."

    - alert: GPUPageFaultDetected
      expr: increase(node_amd_gpu_memory_errors_total[5m]) > 0
      for: 30s
      labels:
        severity: critical
      annotations:
        summary: "Hardware/Translation Layer Memory Fault"
        description: "Memory fault terdeteksi pada GPU node {{ $labels.node }}. Potensi invalid ZLUDA pointer translation."

Automated Rollback via Threshold Failure Rate

Implementasi automated rollback memanfaatkan deployment controller seperti Argo Rollouts atau skrip orchestrator CI/CD berbasis output metrik Prometheus.

Analisis Metrik Argo Rollout

apiVersion: argoproj.io/v1alpha1
kind: AnalysisTemplate
metadata:
  name: zluda-health-analysis
spec:
  metrics:
  - name: error-rate
    interval: 30s
    successCondition: result[0] < 0.01
    failureLimit: 2
    provider:
      prometheus:
        address: http://prometheus-k8s.monitoring:9090
        query: |
          sum(rate(inference_request_errors_total{job="zluda-worker", track="canary", reason=~"zluda.*|cuda.*"}[2m]))
          /
          sum(rate(inference_request_total{job="zluda-worker", track="canary"}[2m]))

Jika metrik error rate akibat failure ZLUDA melebihi 1%, status analisis canary berubah menjadi Failed, menghentikan rollout, dan memutar balik trafik ke deployment stable secara instan.

Mitigasi di Level Container Runtime & Kernel Host

Saat transceiver ZLUDA crash pada tingkat native C++, proses worker sering meninggalkan alokasi VRAM tergantung (zombie memory allocations) pada ROCm KFD (Kernel Fusion Driver). Terapkan mitigasi berikut pada node level:

  • IPC Namespace Isolation: Hindari hostIPC: true kecuali mutlak diperlukan. Shared memory corruption pada satu pod dapat merusak pod lain.
  • KFD Memory Eviction: Pastikan driver host AMDGPU menggunakan parameter reset module. Tambahkan konfigurasi sysfs atau modprobe: options amdgpu gpu_recovery=1.
  • Cgroup Limits: Terapkan securityContext.privileged: false dan petakan node device /dev/kfd serta /dev/dri/renderD* secara spesifik via device plugin Kubernetes, bukan via manual mount global.

Template Postmortem Masalah Driver

# INCIDENT POSTMORTEM: Kegagalan Transceiver ZLUDA Canary

Tanggal: YYYY-MM-DD
Penyebab: Inisialisasi hipMemAlloc() gagal akibat missing symbol translation pada ZLUDA vX.Y.

1. Ringkasan Kejadian:
   Canary pod menerima 5% traffic inferensi. Muncul segfault pada libzluda.so saat memproses
   layer transformer tertentu. Traffic berhasil di-rollback otomatis dalam 90 detik.

2. Timeline:
   - 10:00: Rollout canary dimulai (1 replica).
   - 10:01: Log worker merekam 'HIP error: invalid device pointer' (0x19).
   - 10:01:30: Alert ZludaDriverInitializationFailure aktif di Alertmanager.
   - 10:02:30: Argo Rollouts menandai health analysis FAIL, traffic dikembalikan ke 100% stable.

3. Akar Masalah:
   Kernel ROCm 6.x tidak kompatibel dengan syscall translation mapping ZLUDA untuk instruksi GEMM FP16.

4. Langkah Pencegahan:
   - Tambahkan test coverage integration testing ZLUDA pada hardware target di pipeline CI.
   - Kunci versi kernel host dan library ROCm via image base immutable.