GPU Throttling

GPU Throttling Architecture Analysis

This diagram illustrates the GPU’s power and thermal management system.

Key Components

1. Two Throttling Triggers

  • Power Throttling: Throttling triggered by power limits
  • Thermal Throttling: Throttling triggered by temperature limits

2. Different Control Approaches

  • Power Limit (Budget) Controller: Slow, Linear Step Down
  • Thermal Safety Controller: Fast, Hard Step Down
    • This aggressive response is necessary because overheating can cause immediate hardware damage

3. Priority Gate

Receives signals from both controllers and determines which limitation to apply.

4. PMU/SMU/DVFS Controller

The Common Control Unit that manages:

  • PMU: Power Management Unit
  • SMU: System Management Unit
  • DVFS: Dynamic Voltage and Frequency Scaling

5. Actual Adjustment Mechanisms

  • Clock Domain Controller: Reduces GPU Frequency
  • Voltage Regulator: Reduces GPU Voltage

6. Final Result

Lower Power/Temp (Throttled): Reduced power consumption and temperature in throttled state

Core Principle

When the GPU reaches power budget or temperature limits, it automatically reduces performance to protect the system. By lowering both frequency and voltage simultaneously, it effectively reduces power consumption (P ∝ V²f).


Summary

GPU throttling uses two controllers—power (slow, linear) and thermal (fast, aggressive)—that feed into a shared PMU/SMU/DVFS system to dynamically reduce clock frequency and voltage. Thermal throttling responds more aggressively than power throttling because overheating poses immediate hardware damage risks. The end result is lower power consumption and temperature, sacrificing performance to maintain system safety and longevity.


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Dynamic Voltage and Frequency Scaling (in GPU)

This image illustrates the DVFS (Dynamic Voltage and Frequency Scaling) system workflow, which is a power management technique that dynamically adjusts CPU/GPU voltage and frequency to optimize power consumption.

Key Components and Operation Flow

1. Main Process Flow (Top Row)

  • Workload InitWorkload AnalysisDVFS Policy DecisionClock Frequency AdjustmentVoltage AdjustmentWorkload ExecutionWorkload Finish

2. Core System Components

Power State Management:

  • Basic power states: P0~P12 (P0 = highest performance, P12 = lowest power)
  • Real-time monitoring through PMU (Power Management Unit)

Analysis & Decision Phase:

  • Applies dynamic power consumption formula using algorithms
  • Considers thermal limits in analysis
  • Selects new power state (High: P0-P2, Low: P8-P10)
  • P-State changes occur within 10μs~1ms

Frequency Adjustment (PLL – Phase-Locked Loop):

  • Adjusts GPU core and memory clock frequencies
  • Typical range: 1,410MHz~1,200MHz (memory), 1,000MHz~600MHz (core)
  • Adjustment time: 10-100 microseconds

Voltage Adjustment (VRM – Voltage Regulator Module):

  • Adjusts voltage supplied to GPU core and memory
  • Typical range: 1.1V (P0) to 0.8V (P8)
  • VRM stabilizes voltage within tens of microseconds

3. Real-time Feedback Loop

The system operates a continuous feedback loop that readjusts P-states in real-time based on workload changes, maintaining optimal balance between performance and power efficiency.

4. Execution Phase

The GPU executes workloads at new frequency and voltage settings, with asynchronous adjustments based on frequency and voltage changes. After completion, the system transitions to low-power states (e.g., P10, P12) to conserve energy.


Summary: Key Benefits of DVFS

DVFS technology is for AI data centers as it optimizes GPU efficiency management to achieve maximum overall power efficiency. By intelligently scaling thousands of GPUs based on AI workload demands, DVFS can reduce total data center power consumption by 30-50% while maintaining peak AI performance during training and inference operations, making it essential for sustainable and cost-effective AI infrastructure at scale.

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