Center for Computing Research (CCR)
Center for Computing Research (CCR)

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PAO 1.0: A Python Library for Adversarial Optimization

Hart, William E.; Castillo, Anya; Johnson, Emma S.; Punla-Green, She'Ifa'

PAO is a Python-based package for Adversarial Optimization. The goal of this package is to provide a general modeling and analysis capability for bilevel, trilevel and other multilevel optimization forms that express adversarial dynamics. PAO integrates two different modeling abstractions: 1. Algebraic models extend the modeling concepts in the Pyomo algebraic modeling language to express problems with an intuitive algebraic syntax. Thus, we expect that this modeling abstraction will commonly be used by PAO end-users. 2. Compact models express objective and constraints in a manner that is typically used to express the mathematical form of these problems (e.g. using vector and matrix data types). PAO denes custom Multilevel Problem Representations (MPRs) that simplify the implementation of solvers for bilevel, trilevel and other multilevel optimization problems.

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TYPE SAND Report YEAR 2021
DOIOSTI

Interpreting write performance of supercomputer I/O systems with regression models

Proceedings - 2021 IEEE 35th International Parallel and Distributed Processing Symposium, IPDPS 2021

Xie, Bing; Tan, Zilong; Carns, Philip; Chase, Jeff; Harms, Kevin; Lofstead, Gerald (Jay) F.; Oral, Sarp; Vazhkudai, Sudharshan S.; Wang, Feiyi

This work seeks to advance the state of the art in HPC I/O performance analysis and interpretation. In particular, we demonstrate effective techniques to: (1) model output performance in the presence of I/O interference from production loads; (2) build features from write patterns and key parameters of the system architecture and configurations; (3) employ suitable machine learning algorithms to improve model accuracy. We train models with five popular regression algorithms and conduct experiments on two distinct production HPC platforms. We find that the lasso and random forest models predict output performance with high accuracy on both of the target systems. We also explore use of the models to guide adaptation in I/O middleware systems, and show potential for improvements of at least 15% from model-guided adaptation on 70% of samples, and improvements up to 10 × on some samples for both of the target systems.

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TYPE Conference Paper YEAR 2021
DOIOSTIScopus
Results 2276–2300 of 12,511
Results 2276–2300 of 12,511
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