@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}
}
@techreport {,
	title = {SLATE Port to AMD and Intel Platforms},
	journal = {SLATE Working Notes},
	number = {16, ICL-UT-21-01},
	year = {2021},
	month = {2021-04},
	author = {Ahmad Abdelfattah and Mohammed Al Farhan and Cade Brown and Mark Gates and Dalal Sukkari and Asim YarKhan and Jack Dongarra}
}
@conference {,
	title = {Design, Optimization, and Benchmarking of Dense Linear Algebra Algorithms on AMD GPUs},
	booktitle = {2020 IEEE High Performance Extreme Computing Virtual Conference},
	year = {2020},
	month = {2020-09},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Dense linear algebra (DLA) has historically been in the vanguard of software that must be adapted first to hardware changes. This is because DLA is both critical to the accuracy and performance of so many different types of applications, and because they have proved to be outstanding vehicles for finding and implementing solutions to the problems that novel architectures pose. Therefore, in this paper we investigate the portability of the MAGMA DLA library to the latest AMD GPUs. We use auto tools to convert the CUDA code in MAGMA to the HeterogeneousComputing Interface for Portability (HIP) language. MAGMA provides LAPACK for GPUs and benchmarks for fundamental DLA routines ranging from BLAS to dense factorizations, linear systems and eigen-problem solvers. We port these routines to HIP and quantify currently achievable performance through the MAGMA benchmarks for the main workload algorithms on MI25 and MI50 AMD GPUs. Comparison with performance roofline models and theoretical expectations are used to identify current limitations and directions for future improvements.},
	author = {Cade Brown and Ahmad Abdelfattah and Stanimire Tomov and Jack Dongarra}
}
@techreport {,
	title = {Design, Optimization, and Benchmarking of Dense Linear Algebra Algorithms on AMD GPUs},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-20-12},
	year = {2020},
	month = {2020-08},
	publisher = {University of Tennessee},
	abstract = {Dense linear algebra (DLA) has historically been in the vanguard of software that must be adapted first to hardware changes. This is because DLA is both critical to the accuracy and performance of so many different types of applications, and because they have proved to be outstanding vehicles for finding and implementing solutions to the problems that novel architectures pose. Therefore, in this paper we investigate the portability of the MAGMA DLA library to the latest AMD GPUs. We use auto tools to convert the CUDA code in MAGMA to the HeterogeneousComputing Interface for Portability (HIP) language. MAGMA provides LAPACK for GPUs and benchmarks for fundamental DLA routines ranging from BLAS to dense factorizations, linear systems and eigen-problem solvers. We port these routines to HIP and quantify currently achievable performance through the MAGMA benchmarks for the main workload algorithms on MI25 and MI50 AMD GPUs. Comparison with performance roofline models and theoretical expectations are used to identify current limitations and directions for future improvements.},
	keywords = {AMD GPUs, GPU computing, HIP Runtime, HPC, numerical linear algebra, Portability},
	author = {Cade Brown and Ahmad Abdelfattah and Stanimire Tomov and Jack Dongarra}
}
@booklet {,
	title = {hipMAGMA v1.0},
	year = {2020},
	month = {2020-03},
	publisher = {Zenodo},
	doi = {10.5281/zenodo.3908549},
	url = {https://doi.org/10.5281/zenodo.3908549},
	author = {Cade Brown and Ahmad Abdelfattah and Stanimire Tomov and Jack Dongarra}
}
@booklet {,
	title = {hipMAGMA v2.0},
	year = {2020},
	month = {2020-07},
	publisher = {Zenodo},
	doi = {10.5281/zenodo.3928667},
	url = {https://doi.org/10.5281/zenodo.3928667},
	author = {Cade Brown and Ahmad Abdelfattah and Stanimire Tomov and Jack Dongarra}
}
