Energy Consumption Reduction (Green HPC)
"Watts for Science": Ensuring Electricity Creates Calculations, Not Waste Heat.
The Exascale Energy Challenge
The limiting factor for modern supercomputing is no longer silicon density; 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 environmental responsibility; it is a financial necessity to prevent OpEx from dwarfing the initial hardware investment.
Efficiency Metrics: PUE and FLOPS/Watt
PUE (Power Usage Effectiveness)
The ratio of Total Facility Energy to IT Equipment Energy. An industry-leading PUE of 1.05 means almost 100% of your energy reaches the processors.
FLOPS/Watt
The true measure of efficiency. We optimize your system to perform the maximum amount of "math" per Joule of energy consumed.
Cooling Optimization: Warm Water DLC
Traditional air cooling is inefficient. Water conducts heat 24x better. We implement Direct Liquid Cooling (DLC) using warm water (30°C/86°F).
- No Chillers: Eliminate energy-hungry compressors; use simple dry coolers instead.
- Heat Recovery: Redirect 50°C+ exhaust water into building heating or local district heating systems.
Energy-Aware Scheduling (DVFS)
Power consumption scales with the cube of the frequency. For "Memory Bound" jobs waiting for RAM, high CPU clock speeds are wasted energy.
We deploy EAR (Energy Aware Runtime) to automatically lower CPU frequency when a job is bottlenecked by RAM, saving ~30% power with zero performance loss.
The Paradox of Power: CPU vs. GPU
A GPU consumes more watts per chip, but completes work 50x faster. The Energy to Solution is drastically lower.
| Architecture | Nominal Power Draw | Science Throughput | Total Energy to Solution |
|---|---|---|---|
| Standard CPU Cluster | Medium | Low | High Waste |
| GPU Accelerated (Blackwell) | High | Extreme | Highly Efficient |
Green HPC Toolkit
| Category | Tool | Usage |
|---|---|---|
| Control | Slurm Power Plugin | Capping power usage per job or per node (Power Budgeting). |
| Optimization | EAR (Energy Aware Runtime) | Dynamic CPU frequency scaling based on real-time application behavior. |
| Monitoring | Redfish / IPMI | Reading internal PSU sensors to calculate real-time PUE and efficiency. |
| Hardware | CoolIT / Asetek | Direct Liquid Cooling solutions for high-density GPU racks. |
Build a Sustainable Supercomputer
Download our "Green HPC Strategy Whitepaper" to learn how to transition to Liquid Cooling and EAR-based scheduling.
Download Green HPC Guide (.docx)