@techreport {,
	title = {Communication Avoiding LU with Tournament Pivoting in SLATE},
	journal = {SLATE Working Notes},
	number = {18, ICL-UT-22-01},
	year = {2022},
	month = {2022-01},
	author = {Rabab Alomairy and Mark Gates and Sebastien Cayrols and Dalal Sukkari and Kadir Akbudak and Asim YarKhan and Paul Bagwell and Jack Dongarra}
}
@article {,
	title = {Clover: Computational Libraries Optimized via Exascale Research},
	year = {2020},
	month = {2020-02},
	publisher = {2020 Exascale Computing Project Annual Meeting},
	address = {Houston, TX},
	author = {Mark Gates and Stanimire Tomov and Hartwig Anzt and Piotr Luszczek and Jack Dongarra}
}
@article {1263,
	title = {Computational Benefit of GPU Optimization for Atmospheric Chemistry Modeling},
	journal = {Journal of Advances in Modeling Earth Systems},
	volume = {10},
	year = {2018},
	month = {2018-08},
	pages = {1952{\textendash}1969},
	abstract = {Global chemistry-climate models are computationally burdened as the chemical mechanisms become more complex and realistic. Optimization for graphics processing units (GPU) may make longer global simulation with regional detail possible, but limited study has been done to explore the potential benefit for the atmospheric chemistry modeling. Hence, in this study, the second-order Rosenbrock solver of the chemistry module of CAM4-Chem is ported to the GPU to gauge potential speed-up. We find that on the CPU, the fastest performance is achieved using the Intel compiler with a block interleaved memory layout. Different combinations of compiler and memory layout lead to ~11.02{\texttimes} difference in the computational time. In contrast, the GPU version performs the best when using a combination of fully interleaved memory layout with block size equal to the warp size, CUDA streams for independent kernels, and constant memory. Moreover, the most efficient data transfer between CPU and GPU is gained by allocating the memory contiguously during the data initialization on the GPU. Compared to one CPU core, the speed-up of using one GPU alone reaches a factor of ~11.7{\texttimes} for the computation alone and ~3.82{\texttimes} when the data transfer between CPU and GPU is considered. Using one GPU alone is also generally faster than the multithreaded implementation for 16 CPU cores in a compute node and the single-source solution (OpenACC). The best performance is achieved by the implementation of the hybrid CPU/GPU version, but rescheduling the workload among the CPU cores is required before the practical CAM4-Chem simulation.},
	keywords = {compiler, CUDA, data transfer, gpu, hybrid, memory layout},
	doi = {https://doi.org/10.1029/2018MS001276},
	author = {Jian Sun and Joshua Fu and John Drake and Qingzhao Zhu and Azzam Haidar and Mark Gates and Stanimire Tomov and Jack Dongarra}
}
@techreport {1175,
	title = {C++ API for Batch BLAS},
	journal = {SLATE Working Notes},
	number = {04, ICL-UT-17-12},
	year = {2017},
	month = {2017-12},
	publisher = {University of Tennessee},
	author = {Ahmad Abdelfattah and Konstantin Arturov and Cris Cecka and Jack Dongarra and Chip Freitag and Mark Gates and Azzam Haidar and Jakub Kurzak and Piotr Luszczek and Stanimire Tomov and Panruo Wu}
}
@techreport {1081,
	title = {C++ API for BLAS and LAPACK},
	journal = {SLATE Working Notes},
	number = {02, ICL-UT-17-03},
	year = {2017},
	note = {Revision 02-21-2018},
	month = {2017-06},
	publisher = {Innovative Computing Laboratory, University of Tennessee},
	author = {Mark Gates and Piotr Luszczek and Ahmad Abdelfattah and Jakub Kurzak and Jack Dongarra and Konstantin Arturov and Cris Cecka and Chip Freitag}
}
@conference {896,
	title = {Comparing Hybrid CPU-GPU and Native GPU-only Acceleration for Linear Algebra},
	booktitle = {2015 SIAM Conference on Applied Linear Algebra},
	year = {2015},
	month = {2015-10},
	publisher = {SIAM},
	organization = {SIAM},
	address = {Atlanta, GA},
	abstract = {Accelerating dense linear algebra using GPUs admits two models: hybrid CPU-GPU and GPU-only. The hybrid model factors the panel on the CPU while updating the trailing matrix on the GPU, concentrating the GPU on high-performance matrix multiplies. The GPU-only model performs the entire computation on the GPU, avoiding costly data transfers to the CPU. We compare these two approaches for three QR-based algorithms: QR factorization, rank revealing QR, and reduction to Hessenberg.},
	author = {Mark Gates and Stanimire Tomov and Azzam Haidar}
}
@conference {836,
	title = {clMAGMA: High Performance Dense Linear Algebra with OpenCL },
	booktitle = {International Workshop on OpenCL},
	year = {2014},
	month = {2014-05},
	address = {Bristol University, England},
	abstract = {This paper presents the design and implementation of several fundamental dense linear algebra (DLA) algorithms in OpenCL. In particular, these are linear system solvers and eigenvalue problem solvers. Further, we give an overview of the clMAGMA library, an open source, high performance OpenCL library that incorporates the developments presented, and in general provides to heterogeneous architectures the DLA functionality of the popular LAPACK library. The LAPACK-compliance and use of OpenCL simplify the use of clMAGMA in applications, while providing them with portably performant DLA. High performance is obtained through use of the high-performance OpenCL BLAS, hardware and OpenCL-specific tuning, and a hybridization methodology where we split the algorithm into computational tasks of various granularities. Execution of those tasks is properly scheduled over the heterogeneous hardware components by minimizing data movements and mapping algorithmic requirements to the architectural strengths of the various heterogeneous hardware components.},
	author = {Chongxiao Cao and Jack Dongarra and Peng Du and Mark Gates and Piotr Luszczek and Stanimire Tomov}
}
@techreport {681,
	title = {clMAGMA: High Performance Dense Linear Algebra with OpenCL},
	journal = {University of Tennessee Technical Report (Lawn 275)},
	number = {UT-CS-13-706},
	year = {2013},
	month = {2013-03},
	publisher = {University of Tennessee},
	abstract = {This paper presents the design and implementation of sev- eral fundamental dense linear algebra (DLA) algorithms in OpenCL. In particular, these are linear system solvers and eigenvalue problem solvers. Further, we give an overview of the clMAGMA library, an open source, high performance OpenCL library that incorporates the developments pre- sented, and in general provides to heterogeneous architec- tures the DLA functionality of the popular LAPACK library. The LAPACK-compliance and use of OpenCL simplify the use of clMAGMA in applications, while providing them with portably performant DLA. High performance is ob- tained through use of the high-performance OpenCL BLAS, hardware and OpenCL-speci c tuning, and a hybridization methodology where we split the algorithm into computa- tional tasks of various granularities. Execution of those tasks is properly scheduled over the heterogeneous hardware components by minimizing data movements and mapping algorithmic requirements to the architectural strengths of the various heterogeneous hardware components.},
	author = {Chongxiao Cao and Jack Dongarra and Peng Du and Mark Gates and Piotr Luszczek and Stanimire Tomov}
}
