Good Works for AI workloads

The infographic outlines a comprehensive strategy for optimizing AI workloads by balancing computational performance with power efficiency and thermal management.

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1. GPU Parallelism

This section focuses on distributing the computational load to prevent “hot spots” (heat concentration) within the hardware.

• Core Strategy: Adjusting model partitioning and tensor parallelism levels to balance the thermal load across multiple GPUs.
• Key Techniques: * Tensor Parallelism: Splitting individual tensors across devices.
  • Pipeline Parallelism: Distributing different layers of a model across various GPUs.
  • FSDP (Fully Sharded Data Parallelism): Sharding model states to minimize memory overhead while maintaining high throughput.

2. DVFS (Dynamic Voltage and Frequency Scaling)

This represents the hardware-level power management used to reduce energy waste.

• Core Strategy: Dynamically adjusting GPU clock speeds and voltages based on the real-time workload to minimize unnecessary heat generation.
• Key Techniques:
  • P-State and C-State Control: Managing active performance and idle power states.
  • Hardware Power Capping (TDP Limit): Setting strict thermal design power limits to prevent overheating.
  • Clock/Power Gating: Shutting down power to inactive portions of the chip.

3. Cooling Control

This shifts the focus from reactive cooling to proactive and autonomous thermal infrastructure management.

• Core Strategy: Pre-emptively adjusting cooling parameters (fan speeds, coolant temperatures) based on predicted heat generation from incoming workloads.
• Key Techniques:
  • CDU and DLC Optimization: Maximizing the efficiency of Coolant Distribution Units and Direct Liquid Cooling systems.
  • Telemetry-based Proactive Control: Using real-time data to adjust infrastructure before temperatures spike.
  • AI-driven Autonomous Cooling: Utilizing AI for anomaly detection and self-regulating thermal environments.

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