Presentation
Performance Optimality or Reproducibility: That Is the Question
Event Type
Paper
TP
Best Practices
Energy
Exascale
Machine Learning
Memory
Modelling
Networks
Performance
Power
Reliability
Reproducibility
TimeThursday, 21 November 20192:30pm - 3pm
Location405-406-407
DescriptionThe era of extremely heterogeneous supercomputing brings with itself the devil of increased performance variation and reduced reproducibility. There is a lack of understanding in the HPC community on how the simultaneous consideration of network traffic, power limits, concurrency tuning, and interference from other jobs impacts application performance.
In this paper, we design a methodology that allows both HPC users and system administrators to understand the trade-off space between optimal and reproducible performance. We present a first-of-its-kind dataset that simultaneously varies multiple system- and user-level parameters on a production cluster, and introduce a new metric, called the desirability score, which enables comparison across different system configurations. We develop a novel, model-agnostic machine learning methodology based on the graph signal theory for comparing the influence of parameters on application predictability, and using a new visualization technique, make practical suggestions for best practices for multi-objective HPC environments.
In this paper, we design a methodology that allows both HPC users and system administrators to understand the trade-off space between optimal and reproducible performance. We present a first-of-its-kind dataset that simultaneously varies multiple system- and user-level parameters on a production cluster, and introduce a new metric, called the desirability score, which enables comparison across different system configurations. We develop a novel, model-agnostic machine learning methodology based on the graph signal theory for comparing the influence of parameters on application predictability, and using a new visualization technique, make practical suggestions for best practices for multi-objective HPC environments.
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