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The paper discussesalgorithmic priority inversionin mission-critical machine inference pipelines used in modern neural-network-based perception subsystems and describes a solution to mitigate its effect. In general,priority inversionoccurs in computing systems when computations that are less important are performed together with or ahead of those that are more important. Significant priority inversion occurs in existing machine inference pipelines when they do not differentiate between critical and less critical data. We describe a framework to resolve this problem and demonstrate that it improves a perception system's ability to react to critical inputs, while at the same time reducing platform cost.
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Portals for Interactive Steering of HPC Workflows
High performance computing workloads often benefit from human in the loop interactions. Steps in complex pipelines ranging from quality control to parameter adjustments are critical to the successful and efficient completion of modern problems. We give several example workflows in bioinformatics and deep learning where computing decisions are made throughout the processing pipelines ultimately changing the course of the compute. We also show how users can interact with the pipeline using Open OnDemand plus XDMoD or Plot.ly.
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- Award ID(s):
- 1835725
- PAR ID:
- 10300449
- Date Published:
- Journal Name:
- Communications in computer and information science
- Volume:
- 1190
- Issue:
- 1
- ISSN:
- 1865-0929
- Page Range / eLocation ID:
- 179-189
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-44728-1_11
