TLDR: A new research paper introduces S-RAPS, a pioneering framework that integrates scheduling capabilities into High-Performance Computing (HPC) digital twins. This allows for comprehensive “what-if” studies to evaluate the impact of scheduling policies and incentive structures on system power, cooling, and overall performance, even before deployment. The framework uses open datasets, supports external schedulers, and enables the evaluation of machine learning-guided scheduling, offering a holistic approach to optimizing HPC resource utilization and sustainability.
High-performance computing (HPC) systems are the backbone of scientific discovery and technological advancement, but managing their vast resources efficiently is a complex challenge. Traditional methods for evaluating how these systems schedule tasks often fall short, either by only looking at performance after a system is deployed or by using simulators that don’t account for crucial physical infrastructure like power and cooling. This limitation means that understanding the full impact of scheduling decisions, especially on sustainability, has been difficult to achieve.
A groundbreaking new research paper, titled “HPC Digital Twins for Evaluating Scheduling Policies, Incentive Structures and their Impact on Power and Cooling,” introduces a novel solution to this problem. Authored by Matthias Maiterth, Wesley H. Brewer, Jaya S. Kuruvella, Arunavo Dey, Tanzima Z. Islam, Kevin Menear, Dmitry Duplyakin, Rashadul Kabir, Tapasya Patki, Terry Jones, and Feiyi Wang, the paper presents the first-of-its-kind integration of scheduling capabilities within HPC digital twins.
What are Digital Twins?
Digital twins are virtual replicas of physical systems, integrating real-time data, modeling, simulation, and artificial intelligence to create a comprehensive virtual representation. For data centers, these digital twins can model everything from cooling and power consumption to workloads, offering a powerful tool for design, virtual prototyping, and operational optimization without consuming actual system resources. The key innovation here is extending these digital twins to include scheduling, transforming them from reactive monitoring tools into predictive platforms capable of answering “what-if” questions.
Introducing S-RAPS
The core of this work is Scheduled-RAPS (S-RAPS), an extension of the existing ExaDigiT framework’s Resource Allocator and Power Simulator (RAPS). S-RAPS significantly enhances the ability to study how different scheduling policies and parameter configurations affect the physical assets of an HPC system, even before deployment. This allows for a holistic understanding of a system’s power, cooling, and workload responses, which was previously unobservable when using scheduling simulators in isolation.
The researchers highlight several key contributions of S-RAPS. Firstly, it provides the first digital twin framework specifically designed with integrated scheduling capabilities. Secondly, it incorporates various top-tier HPC systems by utilizing publicly available datasets, making the research accessible and reproducible. Thirdly, S-RAPS is designed to integrate with external scheduling simulators, demonstrating its flexibility and wide applicability. Finally, the framework enables the implementation and evaluation of incentive structures for scheduling, as well as the assessment of machine learning-based scheduling approaches, offering insights that are difficult to obtain in live production environments.
How S-RAPS Works
S-RAPS features an improved simulation loop, enhanced dataloaders for diverse datasets, a robust simulation engine, and a flexible scheduler abstraction. This design allows users to model their specific systems and experiment with various scheduling policies, including traditional ones like First-Come, First-Served (FCFS) and priority-based scheduling, along with different backfill options. The system also includes comprehensive accounting and statistics tracking, providing deeper insights into job, user, and system behavior, such as energy consumption per job, wait times, and turnaround times.
Also Read:
- Murakkab: Optimizing AI Agent Workflows for Efficiency in the Cloud
- Equinox: A New Approach to Fair Resource Allocation in Large Language Model Serving
Real-World Applications and Future Outlook
The paper demonstrates S-RAPS’s utility through several use cases. It evaluates how different scheduling policies impact system power and utilization using datasets from systems like Marconi100 and Adastra. It also showcases successful integrations with external schedulers like ScheduleFlow and FastSim, proving its extensibility. A particularly interesting application is the study of incentive structures, where S-RAPS can mimic how different reward metrics (e.g., based on power usage or Fugaku points) influence job prioritization and system behavior. Furthermore, the framework was used to prototype and evaluate a new machine learning-guided scheduling policy, which showed improved trade-offs across objectives like wait time, turnaround time, and energy consumption under high system load.
This work represents a significant step forward in understanding and predicting HPC system behavior. By enabling “what-if” studies on power, cooling, and job scheduling, S-RAPS offers a powerful tool for evaluating sustainability and optimizing system operations. For more details, you can read the full research paper here.
