HPC & AI INTEGRATION
Achieving Rapid Interactive & AI Workload Starts in Traditional HPC Environments
This analysis explores a proven technique to integrate interactive and AI workloads into existing High-Performance Computing (HPC) infrastructures, ensuring rapid job initiation without compromising traditional batch job efficiency. Based on a successful implementation at Rensselaer's Center for Computational Innovation (CCI), this method addresses the evolving needs of modern research.
Key Performance Indicators
The implemented strategy significantly improved operational efficiency and user satisfaction, demonstrating clear gains across critical metrics.
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Interactive Job Start Time
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Short-Run Job Percentage
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Optimized System Utilization
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Improved User Satisfaction
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The proliferation of AI and interactive workflows demands responsive computing environments. Traditional HPC, optimized for batch processing, often struggles with slow interactive job start times, leading to user frustration and hindering agile research, especially for new users less experienced with HPC schedulers.
Approach Comparison: Chosen vs. Rejected
| Feature | Chosen Approach (High-Priority QOS) | Rejected: Preemption | Rejected: Reserved Nodes |
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| Job Start Consistency |
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| Resource Utilization |
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| User Experience |
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| Implementation Complexity |
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| Alignment with HPC Culture |
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The core solution involves leveraging Slurm's Quality of Service (QOS) mechanism. By introducing a dedicated, high-priority "interactive" QOS with strict, short maximum runtimes (e.g., 60 minutes) and limited resource requests (e.g., 1 GPU), the system can guarantee quick starts for interactive tasks. This is enabled by an overarching policy of short maximum runtimes for all jobs, ensuring high system turnover.
Enterprise Process Flow
Short Max Runtime for ALL Jobs (e.g., 6 hours)
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High-Priority, Resource-Limited QOS for Interactive Jobs (e.g., 1 GPU, 60 min)
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Consistent System Job Turnover
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Rapid Interactive Job Start
This approach was successfully deployed on Rensselaer's CCI AiMOS and AiMOSx systems. Key enablers included the homogeneous nature of GPU resources and the existing culture of short maximum job runtimes. Administrative flexibility, such as allowing critical orchestrator tools like TensorBoard to run outside the scheduler on front-end nodes, further supported AI workflows.
Under 10
Minutes for Interactive Job Starts Achieved
While highly effective on large, homogeneous systems, challenges remain for smaller or more heterogeneous environments where job turnover might be less consistent. Future work includes quantitative analysis of job start times and exploring dynamic reservation strategies, potentially powered by machine learning, to further optimize resource allocation for interactive needs.
Case Study: Rensselaer CCI AiMOS Systems
The implementation on Rensselaer's AiMOS and AiMOSx supercomputers successfully met the objective of providing rapid interactive and AI workload starts. By establishing a high-priority interactive QOS and maintaining a system-wide policy of short maximum job runtimes, users experienced consistent job starts under 10 minutes. This was particularly effective due to the homogeneous GPU architecture and the center's philosophy of maximizing perishable compute resources. This strategic alignment significantly enhanced the experience for a growing user base, particularly those new to traditional HPC.
Calculate Your Potential ROI
Estimate the impact of optimized interactive and AI workload management on your organization's efficiency and cost savings.
Tailored Impact Projection
Implementation Roadmap
Our phased approach ensures a smooth transition and integration of advanced AI workload management within your existing HPC infrastructure.
Phase 1: Current State Assessment
Evaluate existing HPC scheduler configurations, workload patterns, and user requirements. Identify specific bottlenecks for interactive and AI jobs.
Phase 2: QOS Definition & Configuration
Design and implement a dedicated high-priority Quality of Service (QOS) for interactive and AI workloads. Define resource limits (e.g., 1 GPU, 60 min max runtime) and integrate into the Slurm scheduler.
Phase 3: System-Wide Policy Adjustment
Review and adjust default maximum job runtimes across all queues to promote high system turnover. Implement monitoring for resource utilization and job completion rates.
Phase 4: User Training & Rollout
Develop documentation and conduct training sessions for researchers on leveraging the new interactive QOS. Monitor early adoption and gather feedback.
Phase 5: Performance Monitoring & Optimization
Continuously track interactive job start times and overall system efficiency. Explore advanced techniques like ML-driven dynamic reservations for further optimization, especially for heterogeneous environments.
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