@article {,
	title = {Extending MAGMA Portability with OneAPI},
	year = {2022},
	month = {2022-11},
	publisher = {The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC22), ACM Student Research Competition},
	address = {Dallas, TX},
	url = {https://sc22.supercomputing.org/proceedings/src_poster/poster_files/spostu105s3-file1.pdf},
	author = {Anna Fortenberry and Stanimire Tomov and Kwai Wong}
}
@article {,
	title = {Linear Algebra Prepara.on for Emergent Neural Network Architectures: MAGMA, BLAS, and Batched GPU Computing},
	year = {2021},
	month = {2021-11},
	publisher = {LAPENNA Workshop},
	address = {Virtual},
	author = {Stanimire Tomov and Kwai Wong and Rocco Febbo and Julian Halloy}
}
@article {,
	title = {How to Build Your Own Deep Neural Network},
	year = {2020},
	month = {2020-07},
	publisher = {PEARC20},
	keywords = {AI, Deep Neural Networks, dense linear algebra, HPC, ML},
	author = {Kwai Wong and Stanimire Tomov and Daniel Nichols and Rocco Febbo and Florent Lopez and Julian Halloy and Xianfeng Ma}
}
@article {,
	title = {Integrating Deep Learning in Domain Science at Exascale (MagmaDNN)},
	year = {2020},
	month = {2020-12},
	publisher = {DOD HPCMP seminar},
	address = {virtual},
	abstract = {We will present some of the current challenges in the design and integration of deep learning AI with traditional HPC simulations. We evaluate existing packages for readiness to run efficiently deep learning models and applications on large scale HPC systems, identify challenges, and propose new asynchronous parallelization and optimization techniques for current large-scale heterogeneous systems and up-coming exascale systems. These developments, along with existing HPC AI software capabilities, have been integrated in MagmaDNN, an open source HPC deep learning framework. 
Many deep learning frameworks are targeted towards data scientists and fall short in providing quality integration into existing HPC workflows. This paper discusses the necessities of an HPC deep learning framework and how these can be provided, e.g., as in MagmaDNN, through a deep integration with existing HPC libraries such as MAGMA and its modular memory management, MPI, CuBLAS, CuDNN, MKL, and HIP. Advancements are also illustrated through the use of algorithmic enhancements in reduced and mixed-precision and asynchronous optimization methods. Finally, we present illustrations and potential solutions on enhancing traditional compute and data intensive applications at ORNL and UTK with AI. The approaches and future challenges are illustrated on materials science, imaging, and climate applications.},
	author = {Stanimire Tomov and Kwai Wong and Jack Dongarra and Rick Archibald and Edmond Chow and Eduardo D{\textquoteright}Azevedo and Markus Eisenbach and Rocco Febbo and Florent Lopez and Daniel Nichols and Junqi Yin}
}
@techreport {,
	title = {Integrating Deep Learning in Domain Sciences at Exascale},
	journal = {Innovative Computing Laboratory Technical Report},
	number = {ICL-UT-20-10},
	year = {2020},
	month = {2020-08},
	publisher = {University of Tennessee},
	abstract = {This paper presents some of the current challenges in designing deep learning artificial intelligence (AI) and integrating it with traditional high-performance computing (HPC) simulations. We evaluate existing packages for their ability to run deep learning models and applications on large-scale HPC systems e ciently, identify challenges, and propose new asynchronous parallelization and optimization techniques for current large-scale heterogeneous systems and upcoming exascale systems. These developments, along with existing HPC AI software capabilities, have been integrated into MagmaDNN, an open-source HPC deep learning framework. Many deep learning frameworks are targeted at data scientists and fall short in providing quality integration into existing HPC workflows. This paper discusses the necessities of an HPC deep learning framework and how those needs can be provided (e.g., as in MagmaDNN) through a deep integration with existing HPC libraries, such as MAGMA and its modular memory management, MPI, CuBLAS, CuDNN, MKL, and HIP. Advancements are also illustrated through the use of algorithmic enhancements in reduced- and mixed-precision, as well as asynchronous optimization methods. Finally, we present illustrations and potential solutions for enhancing traditional compute- and data-intensive applications at ORNL and UTK with AI. The approaches and future challenges are illustrated in materials science, imaging, and climate applications.},
	author = {Rick Archibald and Edmond Chow and Eduardo D{\textquoteright}Azevedo and Jack Dongarra and Markus Eisenbach and Rocco Febbo and Florent Lopez and Daniel Nichols and Stanimire Tomov and Kwai Wong and Junqi Yin}
}
@conference {,
	title = {Integrating Deep Learning in Domain Sciences at Exascale},
	booktitle = {2020 Smoky Mountains Computational Sciences and Engineering Conference (SMC 2020)},
	year = {2020},
	month = {2020-08},
	abstract = {This paper presents some of the current challenges in designing deep learning artificial intelligence (AI) and integrating it with traditional high-performance computing (HPC) simulations. We evaluate existing packages for their ability to run deep learning models and applications on large-scale HPC systems e ciently, identify challenges, and propose new asynchronous parallelization and optimization techniques for current large-scale heterogeneous systems and upcoming exascale systems. These developments, along with existing HPC AI software capabilities, have been integrated into MagmaDNN, an open-source HPC deep learning framework. Many deep learning frameworks are targeted at data scientists and fall short in providing quality integration into existing HPC workflows. This paper discusses the necessities of an HPC deep learning framework and how those needs can be provided (e.g., as in MagmaDNN) through a deep integration with existing HPC libraries, such as MAGMA and its modular memory management, MPI, CuBLAS, CuDNN, MKL, and HIP. Advancements are also illustrated through the use of algorithmic enhancements in reduced- and mixed-precision, as well as asynchronous optimization methods. Finally, we present illustrations and potential solutions for enhancing traditional compute- and data-intensive applications at ORNL and UTK with AI. The approaches and future challenges are illustrated in materials science, imaging, and climate applications.},
	author = {Rick Archibald and Edmond Chow and Eduardo D{\textquoteright}Azevedo and Jack Dongarra and Markus Eisenbach and Rocco Febbo and Florent Lopez and Daniel Nichols and Stanimire Tomov and Kwai Wong and Junqi Yin}
}
@conference {1325,
	title = {Hands-on Research and Training in High-Performance Data Sciences, Data Analytics, and Machine Learning for Emerging Environments},
	booktitle = {ISC High Performance},
	year = {2019},
	month = {2019-06},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Frankfurt, Germany},
	author = {Kwai Wong and Stanimire Tomov and Jack Dongarra}
}
@article {1366,
	title = {MagmaDNN 0.2 High-Performance Data Analytics for Manycore GPUs and CPUs},
	year = {2019},
	month = {2019-01},
	publisher = {University of Tennessee},
	doi = {10.13140/RG.2.2.14906.64961},
	author = {Lucien Ng and Sihan Chen and Alex Gessinger and Daniel Nichols and Sophia Cheng and Anu Meenasorna and Kwai Wong and Stanimire Tomov and Azzam Haidar and Eduardo D{\textquoteright}Azevedo and Jack Dongarra}
}
@conference {1326,
	title = {MagmaDNN: Accelerated Deep Learning Using MAGMA},
	booktitle = {Practice and Experience in Advanced Research Computing (PEARC {\textquoteright}19)},
	year = {2019},
	month = {2019-07},
	publisher = {ACM},
	organization = {ACM},
	address = {Chicago, IL},
	author = {Daniel Nichols and Kwai Wong and Stanimire Tomov and Lucien Ng and Sihan Chen and Alex Gessinger}
}
@conference {1324,
	title = {MagmaDNN: Towards High-Performance Data Analytics and Machine Learning for Data-Driven Scientific Computing},
	booktitle = {ISC High Performance},
	year = {2019},
	month = {2019-06},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Frankfurt, Germany},
	abstract = {In this paper, we present work towards the development of a new data analytics and machine learning (ML) framework, called MagmaDNN. Our main goal is to provide scalable, high-performance data analytics and ML solutions for scientific applications running on current and upcoming heterogeneous many-core GPU-accelerated architectures. To this end, since many of the functionalities needed are based on standard linear algebra (LA) routines, we designed MagmaDNN to derive its performance power from the MAGMA library. The close integration provides the fundamental (scalable high-performance) LA routines available in MAGMA as a backend to MagmaDNN. We present some design issues for performance and scalability that are specific to ML using Deep Neural Networks (DNN), as well as the MagmaDNN designs towards overcoming them. In particular, MagmaDNN uses well established HPC techniques from the area of dense LA, including task-based parallelization, DAG representations, scheduling, mixed-precision algorithms, asynchronous solvers, and autotuned hyperparameter optimization. We illustrate these techniques and their incorporation and use to outperform other frameworks, currently available.},
	doi = {https://doi.org/10.1007/978-3-030-34356-9_37},
	author = {Daniel Nichols and Natalie-Sofia Tomov and Frank Betancourt and Stanimire Tomov and Kwai Wong and Jack Dongarra}
}
@conference {1327,
	title = {OpenDIEL: A Parallel Workflow Engine and DataAnalytics Framework},
	booktitle = {Practice and Experience in Advanced Research Computing (PEARC {\textquoteright}19)},
	year = {2019},
	month = {2019-07},
	publisher = {ACM},
	organization = {ACM},
	address = {Chicago, IL},
	author = {Frank Betancourt and Kwai Wong and Efosa Asemota and Quindell Marshall and Daniel Nichols and Stanimire Tomov}
}
@article {1331,
	title = {Accelerating 2D FFT: Exploit GPU Tensor Cores through Mixed-Precision},
	year = {2018},
	month = {2018-11},
	publisher = {The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC18), ACM Student Research Poster},
	address = {Dallas, TX},
	author = {Xaiohe Cheng and Anumeena Soma and Eduardo D{\textquoteright}Azevedo and Kwai Wong and Stanimire Tomov}
}
@article {1337,
	title = {MagmaDNN {\textendash} High-Performance Data Analytics for Manycore GPUs and CPUs},
	year = {2017},
	month = {2017-12},
	publisher = {2017 Summer Research Experiences for Undergraduate (REU), Presentation},
	address = {Knoxville, TN},
	author = {Lucien Ng and Kwai Wong and Azzam Haidar and Stanimire Tomov and Jack Dongarra}
}
