EVALUATING HPC SCHEDULING STRATEGIES
Optimizing HPC for Urgent Workloads: A Hybrid Scheduling Approach
Scientific computing centers are increasingly challenged by dynamic, event-triggered workflows demanding rapid execution. This research systematically analyzes HPC scheduler configurations under urgent computing scenarios, evaluating multiple simulators and proposing practical strategies to optimize performance and user satisfaction.
Key Performance Indicators for HPC Scheduling
Our comprehensive analysis reveals critical trade-offs and significant gains achieved through optimized scheduling strategies. These metrics highlight the potential for improved responsiveness and efficiency.
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Avg. Urgent Job Wait Time (E3)
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Avg. Urgent Job Wait Time (E5 - No Preemption)
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Node Minutes Wasted by Preemption (E3)
Deep Analysis & Enterprise Applications
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Methodology Overview
Experimental Findings
Hybrid Scheduling Strategy
We performed a comparative evaluation of four widely used job scheduling simulators (BatSim, AccaSim, Alea, and Slurm Simulator), identifying critical limitations for modeling urgent and mixed-urgency HPC workloads, such as lack of native QoS or preemption. This led us to develop an emulation-based methodology, operating on a real Slurm cluster (OLCF's ACE testbed). This approach enabled reproducible experiments with realistic user submission patterns and configurable urgency levels, capturing authentic scheduler behavior without impacting production workloads. Urgent jobs were defined as those requiring immediate or short-window execution.
Our study involved a suite of nine experimental scenarios (E1-E9), systematically exploring combinations of QoS configurations, preemption strategies (cancellation vs. requeueing), and workload characteristics (short vs. long jobs, with varying runtimes). We used stochastic job submission to mimic realistic user activity, with 5-10% of jobs designated as urgent. Visual analytics, including aggregated end states, per-user timelines (swimlanes), and cluster utilization trends, provided actionable insights into how scheduler policies affect both urgent and normal workloads. Preemption dramatically reduced urgent job wait times, but often at the cost of wasted compute time, especially for long jobs.
The findings confirmed that no single static configuration is optimal for all workloads. We developed a practical mapping between workload profiles and recommended scheduler configurations. This hybrid strategy allows HPC centers to dynamically adapt their policies to balance urgent computing needs with sustained system utilization. For instance, urgent short jobs benefit from high QoS with preemption, while urgent long jobs benefit from high QoS priority boosts without preemption to balance faster starts with resource preservation.
| Feature | BatSim | AccaSim |
|---|---|---|
| Static Job Submission | Yes | Yes |
| Dynamic Job Submission | Yes | No |
| Static Failure Simulation | Yes | Yes |
| Dynamic Failure Simulation | No | No |
| Plots | No | Yes |
| Results Output Format | csv | swf |
| Urgency-aware QoS | No (requires modification) | No (requires modification) |
| Job Preemption | No (requires modification) | No (requires modification) |
| Workload Generation | Custom Script | Built-in |
Emulation-Based Methodology Workflow
Our approach involved a systematic workflow to ensure reproducible experiments and capture authentic scheduler behavior.
Define Workload Profiles
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Configure Slurm QoS & Preemption
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Emulate on Real Cluster
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Collect Performance Metrics
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Analyze Trade-offs
→
Refine Scheduling Policies
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Node Minutes Wasted by Preemption (E3)
While preemption significantly reduces wait times for urgent jobs, it comes at a cost of wasted compute resources for interrupted normal jobs, as observed in scenario E3.
| Job Profile | Recommended Configuration | Rationale |
|---|---|---|
| Urgent, short runtime (< 15 min) | High QoS with preemption | Maximizes responsiveness with minimal wasted compute time. |
| Urgent, long runtime (> 1 hr) | High QoS priority boost without preemption | Reduces wait time while avoiding costly interruptions. |
| Normal priority, exploratory | Normal QoS with possible preemption and requeue | Balances availability for urgent jobs with continued progress for non-critical work. |
| Normal priority, production-scale | Normal QoS without preemption | Protects long-running, resource-intensive jobs from disruption. |
| Feedback-driven workloads | High QoS priority boost without preemption | Enables timely updates while preserving stability. |
| Testing and validation runs | Normal QoS with preemption allowed | Facilitates rapid turnover to make room for critical work. |
Estimate Your HPC Optimization ROI
See how optimizing your HPC scheduling strategies can translate into significant operational efficiencies and cost savings for your enterprise.
Potential Annual Savings
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Hours Reclaimed Annually
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Your HPC Scheduling Optimization Roadmap
Implementing a hybrid scheduling strategy involves a structured approach to assess, configure, and monitor your HPC environment.
Phase 1: Current State Assessment
Analyze existing HPC workloads, identify urgent job patterns, and evaluate current scheduler configurations and their limitations. Collect baseline metrics for wait times, utilization, and job completion rates.
Phase 2: Strategy Design & Configuration
Design a tailored hybrid scheduling strategy based on job profiles and urgency levels. Configure Slurm QoS, preemption, and backfilling policies on a testbed environment, mapping policies to specific application types.
Phase 3: Emulation & Validation
Utilize emulation on a real Slurm cluster with synthetic, mixed-urgency workloads to validate the designed configurations. Measure the impact on key metrics like job turnaround time, wasted resources, and system utilization under controlled conditions.
Phase 4: Monitoring & Continuous Improvement
Implement continuous monitoring of HPC scheduler performance in production. Analyze real-world job traces and user feedback to refine scheduling policies, adapting to evolving workload characteristics and system demands.
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Unsure how to implement a hybrid scheduling strategy for your specific HPC environment? Our experts can help you balance urgent computing needs with overall system efficiency. Schedule a free consultation to discuss a tailored plan.
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