@techreport {1400,
	title = {BDEC2 Platform White Paper},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-19-11},
	year = {2019},
	month = {2019-09},
	publisher = {University of Tennessee},
	author = {Todd Gamblin and Pete Beckman and Kate Keahey and Kento Sato and Masaaki Kondo and Gerofi Balazs}
}
@techreport {1408,
	title = {A Collection of White Papers from the BDEC2 Workshop in San Diego, CA},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-19-13},
	year = {2019},
	month = {2019-10},
	publisher = {University of Tennessee},
	author = {Ilkay Altintas and Kyle Marcus and Volkan Vural and Shweta Purawat and Daniel Crawl and Gabriel Antoniu and Alexandru Costan and Ovidiu Marcu and Prasanna Balaprakash and Rongqiang Cao and Yangang Wang and Franck Cappello and Robert Underwood and Sheng Di and Justin M. Wozniak and Jon C. Calhoun and Cong Xu and Antonio Lain and Paolo Faraboschi and Nic Dube and Dejan Milojicic and Balazs Gerofi and Maria Girone and Viktor Khristenko and Tony Hey and Erza Kissel and Yu Liu and Richard Loft and Pekka Manninen and Sebastian von Alfthan and Takemasa Miyoshi and Bruno Raffin and Olivier Richard and Denis Trystram and Maryam Rahnemoonfar and Robin Murphy and Joel Saltz and Kentaro Sano and Rupak Roy and Kento Sato and Jian Guo and Jen s Domke and Weikuan Yu and Takaki Hatsui and Yasumasa Joti and Alex Szalay and William M. Tang and Michael R. Wyatt II and Michela Taufer and Todd Gamblin and Stephen Herbein and Adam Moody and Dong H. Ahn and Rich Wolski and Chandra Krintz and Fatih Bakir and Wei-tsung Lin and Gareth George}
}
@article {1268,
	title = {Autotuning in High-Performance Computing Applications},
	journal = {Proceedings of the IEEE},
	volume = {106},
	year = {2018},
	month = {2018-11},
	pages = {2068{\textendash}2083},
	abstract = {Autotuning refers to the automatic generation of a search space of possible implementations of a computation that are evaluated through models and/or empirical measurement to identify the most desirable implementation. Autotuning has the potential to dramatically improve the performance portability of petascale and exascale applications. To date, autotuning has been used primarily in high-performance applications through tunable libraries or previously tuned application code that is integrated directly into the application. This paper draws on the authors{\textquoteright} extensive experience applying autotuning to high-performance applications, describing both successes and future challenges. If autotuning is to be widely used in the HPC community, researchers must address the software engineering challenges, manage configuration overheads, and continue to demonstrate significant performance gains and portability across architectures. In particular, tools that configure the application must be integrated into the application build process so that tuning can be reapplied as the application and target architectures evolve.},
	keywords = {High-performance computing, performance tuning programming systems},
	doi = {10.1109/JPROC.2018.2841200},
	author = {Prasanna Balaprakash and Jack Dongarra and Todd Gamblin and Mary Hall and Jeffrey Hollingsworth and Boyana Norris and Richard Vuduc}
}
@inproceedings {icl:602,
	title = {Modeling the Office of Science Ten Year Facilities Plan: The PERI Architecture Tiger Team},
	journal = {SciDAC 2009, Journal of Physics: Conference Series},
	volume = {180(2009)012039},
	year = {2009},
	month = {2009-07},
	publisher = {IOP Publishing},
	address = {San Diego, California},
	keywords = {test},
	author = {Bronis R. de Supinski and Sadaf Alam and David Bailey and Laura Carrington and Chris Daley and Anshu Dubey and Todd Gamblin and Dan Gunter and Paul D. Hovland and Heike Jagode and Karen Karavanic and Gabriel Marin and John Mellor-Crummey and Shirley Moore and Boyana Norris and Leonid Oliker and Catherine Olschanowsky and Philip C. Roth and Martin Schulz and Sameer Shende and Allan Snavely}
}
