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DeepFreeze: Towards Scalable Asynchronous Checkpointing of Deep Learning Models,” 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), Melbourne, VIC, Australia, IEEE, May 2020. DOI: 10.1109/CCGrid49817.2020.00-76“
MagmaDNN: Accelerated Deep Learning Using MAGMA,” Practice and Experience in Advanced Research Computing (PEARC ’19), Chicago, IL, ACM, July 2019.“
MagmaDNN: Towards High-Performance Data Analytics and Machine Learning for Data-Driven Scientific Computing,” ISC High Performance, Frankfurt, Germany, Springer International Publishing, June 2019. DOI: 10.1007/978-3-030-34356-9_37“
MagmaDNN 0.2 High-Performance Data Analytics for Manycore GPUs and CPUs : University of Tennessee, January 2019. DOI: 10.13140/RG.2.2.14906.64961
MagmaDNN – High-Performance Data Analytics for Manycore GPUs and CPUs , Knoxville, TN, 2017 Summer Research Experiences for Undergraduate (REU), Presentation, December 2017.
Heterogeneous Streaming,” The Sixth International Workshop on Accelerators and Hybrid Exascale Systems (AsHES), IPDPS 2016, Chicago, IL, IEEE, May 2016.“
Analyzing PAPI Performance on Virtual Machines,” VMWare Technical Journal, vol. Winter 2013, January 2014.“
Analyzing PAPI Performance on Virtual Machines,” ICL Technical Report, no. ICL-UT-13-02, August 2013.“
Evaluating Asynchronous Schwarz Solvers on GPUs,” International Journal of High Performance Computing Applications, August 2020. DOI: 10.1177/1094342020946814“
An Improved MAGMA GEMM for Fermi GPUs,” International Journal of High Performance Computing, vol. 24, no. 4, pp. 511-515, 00 2010.“
Optimizing Symmetric Dense Matrix-Vector Multiplication on GPUs,” ACM/IEEE Conference on Supercomputing (SC’11), Seattle, WA, November 2011.“
Numerical Linear Algebra on Hybrid Architectures: Recent Developments in the MAGMA Project , Portland, Oregon, The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC09), November 2009.
An Improved MAGMA GEMM for Fermi GPUs,” University of Tennessee Computer Science Technical Report, no. UT-CS-10-655 (also LAPACK working note 227), July 2010.“
Autotuning Dense Linear Algebra Libraries on GPUs , Basel, Switzerland, Sixth International Workshop on Parallel Matrix Algorithms and Applications (PMAA 2010), June 2010.
Blas for GPUs,” Scientific Computing with Multicore and Accelerators, Boca Raton, Florida, CRC Press, 2010.“
Accelerating GPU Kernels for Dense Linear Algebra,” Proc. of VECPAR'10, Berkeley, CA, June 2010.“