Research ComputingHigher Education

University Research Computing Center

Maximizing Scientific Output Within Fixed Energy Budgets

Higher EducationResearch ComputingFair AllocationScientific ComputingUniversity

Research Throughput

+56%

Increased compute jobs completed

Wait Time

-78%

Reduced from 6 weeks to 9 days average

Fair Allocation

100%

All departments within energy budgets

Grant Success

+34%

Improved research funding outcomes

The Challenge

A major research university operated a shared computing facility serving 3,000 researchers across 50 departments. Fixed energy budgets limited compute availability, creating waitlists exceeding 6 weeks for large jobs. Inefficient resource allocation meant some departments consumed disproportionate energy while others faced chronic shortages.

The Solution

Helios deployed fair-share energy management that optimized resource allocation across departments while maximizing total research output. The platform implemented energy-aware scheduling that matched workloads to available capacity, eliminating thermal throttling. Transparent energy accounting enabled departments to understand and optimize their consumption.

Implementation Timeline

Stakeholder Engagement
4 weeks
Understanding diverse research needs
Allocation Framework
3 weeks
Designing fair-share energy model
Platform Deployment
6 weeks
Integrating with existing job schedulers
Community Adoption
4 weeks
Training researchers on new capabilities

Key Insights

Energy-aware scheduling eliminated 94% of thermal throttling events
Transparent accounting reduced inter-departmental allocation disputes by 89%
Predictive job completion estimates improved researcher planning
Efficiency gains enabled addition of 2,400 GPU hours weekly without infrastructure changes

Measurable Impact

Quantified results from this transformation