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Queue Wait Time Prediction in Supercomputers
DescriptionHigh Performance Computing systems play critical role in advancing scientific research. They use schedulers for allocating resources to queued jobs. Waiting time can vary, even among jobs of similar characteristics, making it difficult to accurately estimate the exact time a job will wait in the queue. Knowing how long a job will wait is beneficial for adequate planning and to avoid frustrations that may arise when a user's expectation of waiting time is not met. Efficient job wait time estimation is also crucial for optimizing resource allocation.
This work investigates the impact of job characteristics and user behaviors on job wait time on leadership-class HPC systems. The paper evaluates the performance of different supervised learning algorithms for job wait time estimation. While this study focuses on the workload and hardware characteristics of Theta CrayXC40, the processes and tools developed in this study can be applied to any other leadership-class machine.
This work investigates the impact of job characteristics and user behaviors on job wait time on leadership-class HPC systems. The paper evaluates the performance of different supervised learning algorithms for job wait time estimation. While this study focuses on the workload and hardware characteristics of Theta CrayXC40, the processes and tools developed in this study can be applied to any other leadership-class machine.
Event Type
Workshop
TimeMonday, 13 November 20232:30pm - 2:33pm MST
Location505
State of the Practice
W