GDND - GPU Dead Node Detector

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GDND is a proactive GPU health monitoring and fault isolation system for Kubernetes clusters. It runs as a DaemonSet on all GPU nodes, detects unhealthy GPUs through multi-level detection, and automatically isolates faulty nodes via Taint/Cordon mechanisms.

Features

• Three-tier Detection Pipeline

  • L1 Passive Detection (30s): NVML queries, XID error scanning, zombie process detection
  • L2 Active Detection (5min): CUDA 128x128 matrix multiplication micro-benchmark
  • L3 PCIe Detection (24h, optional): PCIe bandwidth testing

• Health State Machine: HEALTHY → SUSPECTED → UNHEALTHY → ISOLATED

• Automatic Isolation: Cordon nodes, apply taints, evict pods (configurable)

• Prometheus Metrics: Full observability with gdnd_gpu_status, temperature, utilization metrics

• Lightweight: Target image size < 50MB, minimal resource footprint (10m CPU, 32Mi memory)

• Extensible: Device abstraction layer supports NVIDIA GPUs and Huawei Ascend NPUs

Architecture

┌─────────────────────────────────────────────────────────────────┐
│                         GDND DaemonSet                          │
├─────────────────────────────────────────────────────────────────┤
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐             │
│  │ L1 Passive  │  │ L2 Active   │  │ L3 PCIe     │  Detectors  │
│  │ (30s)       │  │ (5min)      │  │ (24h)       │             │
│  └──────┬──────┘  └──────┬──────┘  └──────┬──────┘             │
│         │                │                │                     │
│         └────────────────┼────────────────┘                     │
│                          ▼                                      │
│              ┌───────────────────────┐                          │
│              │   Health State Machine │                         │
│              │  HEALTHY → SUSPECTED  │                          │
│              │  → UNHEALTHY → ISOLATED│                         │
│              └───────────┬───────────┘                          │
│                          │                                      │
│         ┌────────────────┼────────────────┐                     │
│         ▼                ▼                ▼                     │
│  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐             │
│  │   Cordon    │  │    Taint    │  │    Alert    │  Actions    │
│  └─────────────┘  └─────────────┘  └─────────────┘             │
└─────────────────────────────────────────────────────────────────┘

Quick Start

Prerequisites

• Kubernetes cluster 1.25+
• NVIDIA GPU nodes with drivers installed
• kubectl configured to access your cluster

Install with Helm (Recommended)

# Install from local chart
helm install gdnd ./release/rust/gdnd/chart \
  --namespace kube-system \
  --set config.dryRun=true  # Start in dry-run mode for safety

# After verifying logs, disable dry-run
helm upgrade gdnd ./release/rust/gdnd/chart \
  --namespace kube-system \
  --set config.dryRun=false

Install with kubectl

cd release/rust/gdnd/deploy

# Apply RBAC
kubectl apply -f rbac.yaml

# Apply ConfigMap
kubectl apply -f configmap.yaml

# Deploy DaemonSet
kubectl apply -f daemonset.yaml

Verify Installation

# Check DaemonSet status
kubectl get daemonset gdnd -n kube-system

# View logs
kubectl logs -l app.kubernetes.io/name=gdnd -n kube-system -f

# Check metrics
kubectl port-forward -n kube-system daemonset/gdnd 9100:9100
curl http://localhost:9100/metrics | grep gdnd_gpu

Configuration

Key Configuration Options

Parameter	Description	Default
device_type	Device type: auto, nvidia, ascend	auto
l1_interval	L1 passive detection interval	30s
l2_interval	L2 active detection interval	5m
health.failure_threshold	Consecutive failures before UNHEALTHY	3
health.fatal_xids	Fatal XID codes for immediate isolation	[31, 43, 48, 79]
health.temperature_threshold	Temperature threshold (Celsius)	85
isolation.cordon	Whether to cordon unhealthy nodes	true
isolation.evict_pods	Whether to evict pods	false
isolation.taint_key	Taint key	nvidia.com/gpu-health
isolation.taint_effect	Taint effect	NoSchedule
dry_run	Log actions without executing	false

Example config.yaml

device_type: auto
l1_interval: 30s
l2_interval: 5m

health:
  failure_threshold: 3
  fatal_xids: [31, 43, 48, 79]
  temperature_threshold: 85
  active_check_timeout: 5s

isolation:
  cordon: true
  evict_pods: false
  taint_key: nvidia.com/gpu-health
  taint_value: failed
  taint_effect: NoSchedule

metrics:
  enabled: true
  port: 9100

dry_run: false

Fatal XID Error Codes

These XID errors trigger immediate GPU isolation:

XID	Description
31	GPU memory page fault / MMU fault
43	GPU stopped processing
48	Double Bit ECC Error
79	GPU has fallen off the bus

Prometheus Metrics

Metric	Type	Labels	Description
gdnd_gpu_status	Gauge	gpu, uuid, name	Health status (0=healthy, 1=suspected, 2=unhealthy, 3=isolated)
gdnd_gpu_temperature_celsius	Gauge	gpu	GPU temperature
gdnd_gpu_utilization_percent	Gauge	gpu	GPU utilization
gdnd_gpu_memory_used_bytes	Gauge	gpu	GPU memory used
gdnd_check_duration_seconds	Histogram	level, gpu	Detection check duration
gdnd_check_failures_total	Counter	level, gpu, reason	Total detection failures
gdnd_isolation_actions_total	Counter	action	Total isolation actions
gdnd_gpu_count	Gauge	-	Number of GPUs detected

Development

• Rust 1.75+
• CUDA Toolkit 12.2+ (for gpu-check binary)

Build from Source

cd src/rust/gdnd

# Check compilation
cargo check

# Run tests
cargo test

# Build release binary
cargo build --release

# Run locally (dry-run mode)
cargo run -- --config configs/config.yaml --node-name test-node --dry-run

Build Docker Image

cd release/rust/gdnd

# Build release binaries
./build.sh

# Build Docker image
./build.sh --docker

Project Structure

src/rust/gdnd/
├── gdnd/                    # Main binary
│   └── src/
│       ├── main.rs          # Entry point
│       ├── config.rs        # Configuration
│       └── cli.rs           # CLI arguments
├── gdnd-core/               # Core detection logic
│   └── src/
│       ├── device/          # Device abstraction
│       │   ├── interface.rs # DeviceInterface trait
│       │   ├── nvidia.rs    # NVIDIA implementation
│       │   └── mock.rs      # Mock for testing
│       ├── detection/       # Detectors
│       │   ├── l1_passive.rs
│       │   └── l2_active.rs
│       ├── state_machine.rs # Health state machine
│       ├── scheduler.rs     # Detection scheduler
│       └── metrics.rs       # Prometheus metrics
├── gdnd-k8s/                # Kubernetes integration
│   └── src/
│       ├── client.rs        # K8s client
│       └── node_ops.rs      # Node operations
└── gpu-check/               # CUDA micro-benchmark
    └── gpu_check.cu         # 128x128 matrix multiply

release/rust/gdnd/
├── build.sh                 # Build script
├── chart/                   # Helm chart
├── configs/                 # Production configs
└── deploy/                  # K8s manifests

Comparison with Alternatives

Feature	GDND	Node Problem Detector	DIY Scripts
GPU-specific detection	✅ XID, ECC, driver deadlock	❌ Generic	Varies
Active health check	✅ CUDA matrix mul	❌	Varies
Automatic isolation	✅ Cordon + Taint	⚠️ Manual rules	⚠️
Image size	< 50MB	~100MB	Varies
Configuration	Simple YAML	Complex	Custom
Prometheus metrics	✅ Built-in	✅	Manual

Roadmap

Completed (v1.0)

• Core Rust implementation with NVIDIA GPU support
• L1 Passive Detection (NVML/npu-smi, XID scanning, zombie process detection)
• L2 Active Detection (CUDA/AscendCL micro-benchmark)
• Health State Machine (HEALTHY → SUSPECTED → UNHEALTHY → ISOLATED)
• Kubernetes integration (Cordon/Taint/Evict)
• Prometheus metrics
• Helm Chart deployment
• Huawei Ascend NPU full support (2026-01-21)

In Progress

• L3 PCIe bandwidth test (framework ready, 80% complete)
• Real hardware integration testing

Planned

• ECC error detection enhancement
• Grafana dashboard templates
• AlertManager integration
• Node auto-recovery (GPU reset, ISOLATED → HEALTHY)
• Multi-GPU per-device isolation

Contributing

Contributions are welcome! Please feel free to submit issues and pull requests.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Run tests (cargo test)
4. Commit your changes (git commit -m 'Add amazing feature')
5. Push to the branch (git push origin feature/amazing-feature)
6. Open a Pull Request

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Acknowledgments

• nvml-wrapper - Rust bindings for NVIDIA NVML
• kube-rs - Kubernetes client for Rust
• prometheus-rs - Prometheus client for Rust