@article {1270,
	title = {PLASMA: Parallel Linear Algebra Software for Multicore Using OpenMP},
	journal = {ACM Transactions on Mathematical Software},
	volume = {45},
	year = {2019},
	month = {2019-06},
	doi = {https://doi.org/10.1145/3264491},
	author = {Jack Dongarra and Mark Gates and Azzam Haidar and Jakub Kurzak and Piotr Luszczek and Panruo Wu and Ichitaro Yamazaki and Asim YarKhan and Maksims Abalenkovs and Negin Bagherpour and Sven Hammarling and Jakub Sistek}
}
@techreport {1173,
	title = {PLASMA 17 Performance Report},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-17-11},
	year = {2017},
	month = {2017-06},
	publisher = {University of Tennessee},
	abstract = {PLASMA (Parallel Linear Algebra for Multicore Architectures) is a dense linear algebra package at the forefront of multicore computing. PLASMA is designed to deliver the highest possible performance from a system with multiple sockets of multicore processors. PLASMA achieves this objective by combining state of the art solutions in parallel algorithms, scheduling, and software engineering. PLASMA currently offers a collection of routines for solving linear systems of equations and least square problems.},
	author = {Maksims Abalenkovs and Negin Bagherpour and Jack Dongarra and Mark Gates and Azzam Haidar and Jakub Kurzak and Piotr Luszczek and Samuel Relton and Jakub Sistek and David Stevens and Panruo Wu and Ichitaro Yamazaki and Asim YarKhan and Mawussi Zounon}
}
@techreport {1172,
	title = {PLASMA 17.1 Functionality Report},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-17-10},
	year = {2017},
	month = {2017-06},
	publisher = {University of Tennessee},
	abstract = {PLASMA (Parallel Linear Algebra for Multicore Architectures) is a dense linear algebra package at the forefront of multicore computing. PLASMA is designed to deliver the highest possible performance from a system with multiple sockets of multicore processors. PLASMA achieves this objective by combining state of the art solutions in parallel algorithms, scheduling, and software engineering. PLASMA currently offers a collection of routines for solving linear systems of equations and least square problems.},
	author = {Maksims Abalenkovs and Negin Bagherpour and Jack Dongarra and Mark Gates and Azzam Haidar and Jakub Kurzak and Piotr Luszczek and Samuel Relton and Jakub Sistek and David Stevens and Panruo Wu and Ichitaro Yamazaki and Asim YarKhan and Mawussi Zounon}
}
@article {936,
	title = {Parallel Programming Models for Dense Linear Algebra on Heterogeneous Systems},
	journal = {Supercomputing Frontiers and Innovations},
	volume = {2},
	number = {4},
	year = {2015},
	month = {2015-10},
	abstract = {We present a review of the current best practices in parallel programming models for dense linear algebra (DLA) on heterogeneous architectures. We consider multicore CPUs, stand alone manycore coprocessors, GPUs, and combinations of these. Of interest is the evolution of the programming models for DLA libraries {\textendash} in particular, the evolution from the popular LAPACK and ScaLAPACK libraries to their modernized counterparts PLASMA (for multicore CPUs) and MAGMA (for heterogeneous architectures), as well as other programming models and libraries. Besides providing insights into the programming techniques of the libraries considered, we outline our view of the current strengths and weaknesses of their programming models {\textendash} especially in regards to hardware trends and ease of programming high-performance numerical software that current applications need {\textendash} in order to motivate work and future directions for the next generation of parallel programming models for high-performance linear algebra libraries on heterogeneous systems.},
	keywords = {dense linear algebra, gpu, HPC, Multicore, plasma, Programming models, runtime},
	doi = {10.14529/jsfi1504},
	author = {Maksims Abalenkovs and Ahmad Abdelfattah and Jack Dongarra and Mark Gates and Azzam Haidar and Jakub Kurzak and Piotr Luszczek and Stanimire Tomov and Ichitaro Yamazaki and Asim YarKhan}
}
