RESOURCE MANAGEMENT OPTIMIZATION
HPC Digital Twins for Evaluating Scheduling Policies, Incentive Structures and their Impact on Power and Cooling
This paper introduces Scheduled-RAPS (S-RAPS), an innovative extension of the ExaDigiT framework that integrates advanced scheduling capabilities with digital twins for High-Performance Computing (HPC) systems. S-RAPS enables comprehensive 'what-if' studies to assess the impact of parameter configurations, scheduling decisions, and incentive structures on physical assets like cooling and power consumption, even before deployment. By leveraging open datasets and supporting external schedulers, S-RAPS provides a unique platform for evaluating sustainability, optimizing resource utilization, and prototyping machine learning-guided scheduling policies in a holistic manner, addressing limitations of traditional simulation methods.
Key Metrics & Impact
Our analysis reveals critical performance indicators for optimizing HPC resource management, scheduling, and energy efficiency:
688x
Simulation Speedup (FastSim Integration)
FastSim integration achieved a 688x speedup compared to real-time for simulating large job traces, enabling rapid what-if scenario analysis.
2%
Average Power Savings (Backfilled Policies)
Backfilled scheduling policies (e.g., FCFS Easy, Priority First-Fit) demonstrated a 2% reduction in average power consumption per job compared to non-backfilled approaches, indicating improved energy efficiency.
100%
Overall System Utilization (Optimized Scheduling)
Optimized scheduling with backfill achieved 100% continued system utilization during peak periods, maximizing resource throughput.
Deep Analysis & Enterprise Applications
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Integrating Scheduling into Digital Twins for HPC
The core innovation of S-RAPS lies in its ability to merge traditional HPC scheduling simulators with a detailed digital twin framework. This integration moves beyond simple job trace replay, allowing for dynamic rescheduling and 'what-if' analyses that incorporate physical infrastructure models (power, cooling). This enables a holistic view of system behavior under various scheduling policies and configurations, a capability previously unavailable in isolated simulation environments.
688x
Simulation Speedup with FastSim
Our integration with the FastSim scheduler allowed for a simulation speedup of 688 times compared to real-time, enabling rapid evaluation of complex scheduling policies and their impact on system performance and energy efficiency for large-scale HPC systems.
S-RAPS Simulation Loop with Scheduler Integration
System Initialization
→
Add Eligible Jobs to Queue
→
Call Scheduler (Policy & Resource Mgmt)
→
Tick (Update Resources, Power, Cooling)
→
Simulation Statistics & Output
The enhanced S-RAPS simulation loop dynamically integrates built-in or external schedulers to perform 'what-if' analyses on HPC systems, providing insights into resource allocation, power, and cooling implications based on various scheduling policies and real-world telemetry.
| Metric | FCFS No Backfill | FCFS Easy Backfill | Priority First-Fit Backfill | ML-Guided Policy |
|---|---|---|---|---|
| Average Wait Time | Higher | Moderate | Lower | Lowest |
| Job Turnaround Time | Higher | Moderate | Lower | Lowest |
| Energy Consumption per Job | Highest | Moderate | Lower | Lowest |
| System Utilization | Variable | High | High | Highest Consistent |
| Power Spikes | Frequent | Reduced | Minimal | Smoothed |
Comparing different scheduling policies on the Fugaku dataset revealed that ML-guided policies significantly reduce average wait times and energy consumption while maintaining high, consistent system utilization, leading to improved overall system efficiency and sustainability.
Incentive-Based Scheduling for Energy Efficiency
Scenario: A study using the Fugaku dataset explored incentive structures where user job priorities were adjusted based on their historical power consumption behavior. Jobs with lower average energy consumption were rewarded with higher priority in the redeeming phase.
Outcome: The results demonstrated that such incentive-based scheduling successfully influenced job placement, leading to smoother power profiles and reduced power spikes. Specifically, jobs with a generally low power profile were prioritized as intended, validating the potential of S-RAPS to prototype and evaluate complex incentive mechanisms without real-world deployment risks.
Key Takeaway: S-RAPS enables the evaluation of incentive structures for promoting energy-efficient job submission behavior, leading to optimized system power consumption and improved sustainability.
Advanced ROI Calculator
Understand the tangible benefits of optimizing your HPC scheduling and resource management. Our calculator estimates potential annual savings and reclaimed operational hours by implementing advanced digital twin and AI-driven scheduling solutions.
Implementation Roadmap
Our proven methodology guides your AI transformation from initial data integration to continuous optimization, ensuring measurable impact and sustainable growth.
Phase 1: System Data Integration & Digital Twin Setup
Duration: 4-6 Weeks
Integrate HPC system telemetry, job traces, and infrastructure models into the S-RAPS framework. Configure initial digital twin for accurate baseline simulation.
Phase 2: Custom Scheduler Integration & Policy Prototyping
Duration: 6-8 Weeks
Integrate existing or develop new scheduling policies and external simulators (e.g., Slurm Simulator, FastSim) within S-RAPS. Conduct initial 'what-if' studies to evaluate policy impacts.
Phase 3: AI/ML Model Training & Incentive Structure Design
Duration: 8-12 Weeks
Train ML models for predictive scheduling based on historical data. Design and evaluate custom incentive structures to optimize energy efficiency and resource utilization.
Phase 4: Comprehensive Performance & Sustainability Analysis
Duration: 4-6 Weeks
Perform extensive simulations to assess the holistic impact on power, cooling, job throughput, and user experience. Generate detailed reports on sustainability metrics and ROI.
Phase 5: Operational Deployment & Continuous Optimization
Duration: Ongoing
Deploy validated scheduling policies to production systems. Establish continuous monitoring and iterative optimization processes leveraging digital twin insights.
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