@article {,
	title = {Translational process: Mathematical software perspective},
	journal = {Journal of Computational Science},
	volume = {52},
	year = {2021},
	pages = {101216},
	abstract = {Each successive generation of computer architecture has brought new challenges to achieving high performance mathematical solvers, necessitating development and analysis of new algorithms, which are then embodied in software libraries. These libraries hide architectural details from applications, allowing them to achieve a level of portability across platforms from desktops to world-class high performance computing (HPC) systems. Thus there has been an informal translational computer science process of developing algorithms and distributing them in open source software libraries for adoption by applications and vendors. With the move to exascale, increasing intentionality about this process will benefit the long-term sustainability of the scientific software stack.},
	keywords = {communication avoiding algorithms, DATAFLOW scheduling runtimes, hardware accelerators},
	doi = {10.1016/j.jocs.2020.101216},
	author = {Jack Dongarra and Mark Gates and Piotr Luszczek and Stanimire Tomov}
}
@article {,
	title = {Translational Process: Mathematical Software Perspective},
	journal = {Journal of Computational Science},
	year = {2020},
	month = {2020-09},
	abstract = {Each successive generation of computer architecture has brought new challenges to achieving high performance mathematical solvers, necessitating development and analysis of new algorithms, which are then embodied in software libraries. These libraries hide architectural details from applications, allowing them to achieve a level of portability across platforms from desktops to world-class high performance computing (HPC) systems. Thus there has been an informal translational computer science process of developing algorithms and distributing them in open source software libraries for adoption by applications and vendors. With the move to exascale, increasing intentionality about this process will benefit the long-term sustainability of the scientific software stack.},
	keywords = {communication avoiding algorithms, DATAFLOW scheduling runtimes, hardware accelerators},
	doi = {https://doi.org/10.1016/j.jocs.2020.101216},
	author = {Jack Dongarra and Mark Gates and Piotr Luszczek and Stanimire Tomov}
}
@techreport {,
	title = {Translational Process: Mathematical Software Perspective},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-20-11},
	year = {2020},
	month = {2020-08},
	abstract = {Each successive generation of computer architecture has brought new challenges to achieving high performance mathematical solvers, necessitating development and analysis of new algorithms, which are then embodied in software libraries. These libraries hide architectural details from applications, allowing them to achieve a level of portability across platforms from desktops to worldclass high performance computing (HPC) systems. Thus there has been an informal translational computer science process of developing algorithms and distributing them in open source software libraries for adoption by applications and vendors. With the move to exascale, increasing intentionality about this process will benefit the long-term sustainability of the scientific software stack.},
	keywords = {communication avoiding algorithms, data flow scheduling runtimes, hardware accelerators},
	author = {Jack Dongarra and Mark Gates and Piotr Luszczek and Stanimire Tomov}
}
