Energy Consumption Reduction (Green HPC) is the most critical challenge in modern supercomputing.

The limiting factor for the next generation of supercomputers is not silicon technology; it is the electricity bill. An Exascale system can consume 20-30 Megawatts—enough to power a small city. Reducing consumption is not just about "saving the planet"; it is about preventing the operational costs (OpEx) from exceeding the cost of the hardware itself.

We tackle this via "Watts for Science," ensuring that electricity creates calculations, not waste heat.

Here is the detailed breakdown of the reduction strategies (Cooling, DVFS, and Hardware), followed by the downloadable Word file.

1. The Metric: PUE and FLOPS/Watt

You cannot manage what you do not measure.

• PUE (Power Usage Effectiveness): The ratio of Total Facility Energy to IT Equipment Energy.
• Bad: 1.5 (For every 1 watt of science, 0.5 watts is wasted on cooling).
• Excellent: 1.05 (Almost all energy goes to the servers).
• FLOPS/Watt: The true measure of efficiency. How much math can you do for 1 Joule of energy?

2. Facility Strategy: Cooling Optimization

Cooling typically accounts for 30-40% of the energy bill in air-cooled data centers.

• Warm Water Cooling (DLC):
• Concept: Water conducts heat 24x better than air. Instead of blasting cold air at 18°C (64°F), we pump warm water at 30°C (86°F) directly into the CPU blocks.
• The Save: Because the water is warm, we don't need energy-hungry chillers (compressors). We can just use a simple radiator (Dry Cooler) outside, even in summer. This drops PUE to ~1.03.
• Waste Heat Recovery:
• Concept: The water coming out of the supercomputer is hot (50°C+). Instead of dumping this heat, it is pumped into the building's heating system or nearby district heating.

3. Software Strategy: Energy-Aware Scheduling (DVFS)

This is the "Low Hanging Fruit."

• The Physics: Power consumption scales with the cube of the frequency. Running a CPU 10% slower saves ~30% power.
• Memory Bound Jobs: If a job is waiting for RAM (Memory Bound), running the CPU at 3.0 GHz is a waste. It will run just as fast at 2.2 GHz because the bottleneck is RAM, not the clock speed.
• Implementation: We install EAR (Energy Aware Runtime). It watches the job. If it sees the job is Memory Bound, it automatically lowers the CPU frequency. The user gets the same result at the same speed, but the node uses 50 watts less.

4. Hardware Strategy: Accelerators

• GPU vs. CPU: A CPU might consume 200W to do X math. A GPU might consume 400W but do 50X the math.
• The Result: Even though the GPU is "power hungry," its Energy to Solution is drastically lower. Migrating code to GPUs is the single biggest energy reduction step a scientist can take.

5. Key Applications & Tools

Category	Tool	Usage
Control	Slurm Power Plugin	Allows the scheduler to cap the power usage of a job (e.g., "Run this job, but do not exceed 200 Watts").
Optimization	EAR (Energy Aware Runtime)	An automated framework that tunes CPU frequency dynamically based on the application's real-time behavior.
Monitoring	IPMI / Redfish	The protocol used to read the power sensors inside the Power Supply Units (PSU).
Hardware	CoolIT / Asetek	Leaders in Direct Liquid Cooling (DLC) hardware solutions.