Standardized data coordination between computational workloads and energy infrastructure
AI workloads create 200+ MW power swings within 40ms, destabilizing electrical grids. Current data centers lack consistent data movement mechanisms for coordinated workload-infrastructure optimization.
WDPC provides standardized temporal data formats and interfaces enabling intelligent coordination without prescriptive control implementation.
Key Capabilities:
- 🕐 Temporal Data Standards - 100ms resolution with metadata tagging
- 🔌 Infrastructure Coordination - Power, thermal, and grid data integration
- 🌱 Renewable Optimization - Carbon-aware workload scheduling data
- ♨️ Heat Recovery - Municipal heating network coordination
Power Metrics by Component
| Component | Category | Key Power Metrics |
|---|---|---|
| GPU | System | Power Usage, Throttle Status/Reason |
| Memory | System | Memory Metrics: Power Consumption |
| Power Supply | Chassis | Power Metrics: Average, Peak, Limit |
| Power Domain | System | Input Power, Output Power, Efficiency |
| Voltage | Chassis | Current Voltage, Min/Max/Avg, Thresholds |
| Power Control | Chassis | Power Limit, Allocated Power, Requested Power |
| Environment Metrics | System/Chassis | Total Power, Power Consumed, Power Limit |
| Component | Requirement |
|---|---|
| Temporal Resolution | 100ms minimum |
| Power Accuracy | ±0.5% |
| Temperature Accuracy | ±0.1°C |
| Time Synchronization | ±1ms (NTP/PTP) |
| Data Format | JSON with metadata |
- AI Training Coordination - Schedule compute during renewable energy peaks
- Grid Stability - Provide load forecasting and demand response data
- Municipal Heat - Coordinate waste heat delivery to district heating
- Carbon Optimization - Enable workload scheduling based on grid carbon intensity
MIT License - see LICENSE file for details.
Creating the data foundation for sustainable, grid-friendly infrastructure 🌱⚡