@conference {,
	title = {Surrogate ML/AI Model Benchmarking for FAIR Principles{\textquoteright} Conformance},
	booktitle = {2022 IEEE High Performance Extreme Computing Conference (HPEC)},
	year = {2022},
	month = {2022-09},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present benchmarking platform for surrogate ML/AI models that enables the essential properties for open science and allow them to be findable, accessible, interoperable, and reusable. We also present a use case of cloud cover modeling, analysis, and experimental testing based on a large dataset of multi-spectral satellite sensor data. We use this particular evaluation to highlight the plethora of choices that need resolution for the life cycle of supporting the scientific workflows with data-driven models that need to be first trained to satisfactory accuracy and later monitored during field usage for proper feedback into both computational results and future data model improvements. Unlike traditional testing, performance, or analysis efforts, we focus exclusively on science-oriented metrics as the relevant figures of merit.},
	keywords = {Analytical models, Benchmark testing, Cloud computing, Computational modeling, Data models, Measurement, Satellites},
	doi = {10.1109/HPEC55821.2022.9926401},
	url = {https://ieeexplore.ieee.org/document/9926401/},
	author = {Piotr Luszczek and Cade Brown}
}
@article {926,
	title = {The TOP500 List and Progress in High-Performance Computing},
	journal = {IEEE Computer},
	volume = {48},
	year = {2015},
	month = {2015-11},
	pages = {42-49},
	abstract = {For more than two decades, the TOP500 list has enjoyed incredible success as a metric for supercomputing performance and as a source of data for identifying technological trends. The project{\textquoteright}s editors reflect on its usefulness and limitations for guiding large-scale scientific computing into the exascale era.},
	keywords = {application performance, Benchmark testing, benchmarks, Computer architecture, High Performance Computing, High-performance computing, Linpack, Market research, Parallel computing, Program processors, Scientific computing, Supercomputers, top500},
	doi = {doi:10.1109/MC.2015.338},
	author = {Erich Strohmaier and Hans Meuer and Jack Dongarra and Horst D. Simon}
}
@article {,
	title = {Keeneland: Bringing Heterogeneous GPU Computing to the Computational Science Community},
	journal = {IEEE Computing in Science \& Engineering},
	volume = {13},
	year = {2011},
	month = {2011-08},
	pages = {90-95},
	abstract = {The Keeneland project{\textquoteright}s goal is to develop and deploy an innovative, GPU-based high-performance computing system for the NSF computational science community.},
	keywords = {Benchmark testing, Computational modeling, Computer architecture, Graphics processing unit, Hardware, Random access memory, Scientific computing},
	doi = {https://doi.org/10.1109/MCSE.2011.83},
	author = {Jeffrey Vetter and Richard Glassbrook and Jack Dongarra and Karsten Schwan and Bruce Loftis and Stephen McNally and Jeremy Meredith and James Rogers and Philip Roth and Kyle Spafford and Sudhakar Yalamanchili}
}
