‪Gopinath Chennupati (గోపినాథ్ చెన్నుపాటి)‬

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Stephan EidenbenzComputer Science, Los Alamos National LaboratoryDirección de correo verificada de lanl.gov
Nandakishore SanthiStaff Scientist, Los Alamos National Laboratory; Affiliate Graduate Faculty at ECE Dept, NMSUDirección de correo verificada de lanl.gov
Abdel-Hameed A. BadawyAssociate Professor, Klipsch School of Electrical & Computer Engineering, New Mexico State Univ.Dirección de correo verificada de nmsu.edu
Sunil ThulasidasanAmazonDirección de correo verificada de amazon.com
Yehia ArafaQualcomm Cloud Computing, PhD in Computer EngineeringDirección de correo verificada de qualcomm.com
Hristo DjidjevScientist at IICT, Bulgarian Academy of Sciences and Los Alamos National LaboratoryDirección de correo verificada de lanl.gov
Conor RyanUniversity of LimerickDirección de correo verificada de ul.ie
R. Muhammad Atif AzadBirmingham City UniversityDirección de correo verificada de bcu.ac.uk
Atanu Barai (অতনু বাড়ৈ)ISTI Postdoc Fellow at Los Alamos National LaboratoryDirección de correo verificada de lanl.gov
Amarda ShehuProfessor of Computer Science, George Mason UniversityDirección de correo verificada de gmu.edu
Nasrin AkhterAssistant Professor of Teaching, University at BuffaloDirección de correo verificada de buffalo.edu
Abu ObaidaSoftware Development Engineer 2, Amazon Aurora, Amazon Web Services, AmazonDirección de correo verificada de fiu.edu
Satyajayant "Jay" MisraProfessor of Computer Science and Electrical and Computer Engineering, New Mexico State UniversityDirección de correo verificada de cs.nmsu.edu
BT NadigaLANLDirección de correo verificada de lanl.gov
Richard J ZamoraNVIDIADirección de correo verificada de nvidia.com

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Gopinath Chennupati (గోపినాథ్ చెన్నుపాటి)

Applied Scientist, Amazon

Dirección de correo verificada de amazon.com - Página principal

Deep Learning Parallel Programming Performance Modeling Nonnegative Matrix/Tensor Factorizations Program Synthesis


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
On mixup training: Improved calibration and predictive uncertainty for deep neural networks S Thulasidasan, G Chennupati, JA Bilmes, T Bhattacharya, S Michalak Advances in neural information processing systems 32, 2019	501	2019
Combating label noise in deep learning using abstention S Thulasidasan, T Bhattacharya, J Bilmes, G Chennupati, J Mohd-Yusof arXiv preprint arXiv:1905.10964, 2019	183	2019
Quantum algorithm implementations for beginners PJ Coles, S Eidenbenz, S Pakin, A Adedoyin, J Ambrosiano, P Anisimov, ... arXiv, arXiv: 1804.03719, 2018	144	2018
Quantum algorithm implementations for beginners A Adedoyin, J Ambrosiano, P Anisimov, W Casper, G Chennupati, ... arXiv preprint arXiv:1804.03719, 2018	142	2018
Verified instruction-level energy consumption measurement for nvidia gpus Y Arafa, A ElWazir, A ElKanishy, Y Aly, A Elsayed, AH Badawy, ... Proceedings of the 17th ACM International Conference on Computing Frontiers …, 2020	41	2020
Ppt-gpu: Scalable gpu performance modeling Y Arafa, AHA Badawy, G Chennupati, N Santhi, S Eidenbenz IEEE Computer Architecture Letters 18 (1), 55-58, 2019	39	2019
Low overhead instruction latency characterization for nvidia gpgpus Y Arafa, AHA Badawy, G Chennupati, N Santhi, S Eidenbenz 2019 IEEE High Performance Extreme Computing Conference (HPEC), 1-8, 2019	31	2019
An effective baseline for robustness to distributional shift S Thulasidasan, S Thapa, S Dhaubhadel, G Chennupati, T Bhattacharya, ... 2021 20th IEEE International Conference on Machine Learning and Applications …, 2021	29	2021
Finding the number of latent topics with semantic non-negative matrix factorization R Vangara, M Bhattarai, E Skau, G Chennupati, H Djidjev, T Tierney, ... IEEE Access 9, 117217-117231, 2021	28	2021
Parallel application performance prediction using analysis based models and HPC simulations MA Obaida, J Liu, G Chennupati, N Santhi, S Eidenbenz Proceedings of the 2018 ACM SIGSIM Conference on Principles of Advanced …, 2018	23	2018
Distributed non-negative matrix factorization with determination of the number of latent features G Chennupati, R Vangara, E Skau, H Djidjev, B Alexandrov The Journal of Supercomputing 76, 7458-7488, 2020	22	2020
Hybrid, scalable, trace-driven performance modeling of GPGPUs Y Arafa, AH Badawy, A ElWazir, A Barai, A Eker, G Chennupati, N Santhi, ... Proceedings of the International Conference for High Performance Computing …, 2021	21	2021
Code characterization with graph convolutions and capsule networks P Haridas, G Chennupati, N Santhi, P Romero, S Eidenbenz IEEE Access 8, 136307-136315, 2020	20	2020
Fast, accurate, and scalable memory modeling of GPGPUs using reuse profiles Y Arafa, AH Badawy, G Chennupati, A Barai, N Santhi, S Eidenbenz Proceedings of the 34th ACM International Conference on Supercomputing, 1-12, 2020	20	2020
A scalable analytical memory model for CPU performance prediction G Chennupati, N Santhi, R Bird, S Thulasidasan, AHA Badawy, S Misra, ... High Performance Computing Systems. Performance Modeling, Benchmarking, and …, 2018	20	2018
An analytical memory hierarchy model for performance prediction G Chennupati, N Santhi, S Eidenbenz, S Thulasidasan 2017 Winter Simulation Conference (WSC), 908-919, 2017	19	2017
Performance optimization of multi-core grammatical evolution generated parallel recursive programs G Chennupati, RMA Azad, C Ryan Proceedings of the 2015 Annual Conference on Genetic and Evolutionary …, 2015	19	2015
Distributed non-negative tensor train decomposition M Bhattarai, G Chennupati, E Skau, R Vangara, H Djidjev, BS Alexandrov 2020 IEEE High Performance Extreme Computing Conference (HPEC), 1-10, 2020	17	2020
Scalable performance prediction of codes with memory hierarchy and pipelines G Chennupati, N Santhi, S Eidenbenz Proceedings of the 2019 ACM SIGSIM Conference on Principles of Advanced …, 2019	17	2019
Multi-core GE: automatic evolution of CPU based multi-core parallel programs G Chennupati, RMA Azad, C Ryan Proceedings of the 2014 conference companion on Genetic and evolutionary …, 2014	17	2014

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