Dhiraj D. Kalamkar

According to our database, Dhiraj D. Kalamkar authored at least 31 papers between 2007 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures.

Evangelos Georganas, Dhiraj D. Kalamkar, Kirill Voronin, Abhisek Kundu, Antonio Noack, Hans Pabst, Alexander Breuer, Alexander Heinecke

Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2024

2023

Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures.

Evangelos Georganas, Dhiraj D. Kalamkar, Kirill Voronin, Antonio Noack, Hans Pabst, Alexander Breuer, Alexander Heinecke

CoRR, 2023

2022

Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning and HPC Workloads.

Evangelos Georganas, Dhiraj D. Kalamkar, Sasikanth Avancha, Menachem Adelman, Deepti Aggarwal, Cristina Anderson, Alexander Breuer, Jeremy Bruestle, Narendra Chaudhary, Abhisek Kundu, Denise Kutnick, Frank Laub, Md. Vasimuddin, Sanchit Misra, Ramanarayan Mohanty, Hans Pabst, Brian Retford, Barukh Ziv, Alexander Heinecke

Frontiers Appl. Math. Stat., 2022

Accelerating Deep Learning based Identification of Chromatin Accessibility from noisy ATAC-seq Data.

Narendra Chaudhary, Sanchit Misra, Dhiraj D. Kalamkar, Alexander Heinecke, Evangelos Georganas, Barukh Ziv, Menachem Adelman, Bharat Kaul

Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2022

2021

Efficient and Generic 1D Dilated Convolution Layer for Deep Learning.

Narendra Chaudhary, Sanchit Misra, Dhiraj D. Kalamkar, Alexander Heinecke, Evangelos Georganas, Barukh Ziv, Menachem Adelman, Bharat Kaul

CoRR, 2021

Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning Workloads.

Evangelos Georganas, Dhiraj D. Kalamkar, Sasikanth Avancha, Menachem Adelman, Cristina Anderson, Alexander Breuer, Narendra Chaudhary, Abhisek Kundu, Md. Vasimuddin, Sanchit Misra, Ramanarayan Mohanty, Hans Pabst, Barukh Ziv, Alexander Heinecke

CoRR, 2021

DistGNN: scalable distributed training for large-scale graph neural networks.

Md. Vasimuddin, Sanchit Misra, Guixiang Ma, Ramanarayan Mohanty, Evangelos Georganas, Alexander Heinecke, Dhiraj D. Kalamkar, Nesreen K. Ahmed, Sasikanth Avancha

Proceedings of the International Conference for High Performance Computing, 2021

Tensor processing primitives: a programming abstraction for efficiency and portability in deep learning workloads.

Evangelos Georganas, Dhiraj D. Kalamkar, Sasikanth Avancha, Menachem Adelman, Cristina Anderson, Alexander Breuer, Jeremy Bruestle, Narendra Chaudhary, Abhisek Kundu, Denise Kutnick, Frank Laub, Md. Vasimuddin, Sanchit Misra, Ramanarayan Mohanty, Hans Pabst, Barukh Ziv, Alexander Heinecke

Proceedings of the International Conference for High Performance Computing, 2021

2020

Optimizing deep learning recommender systems training on CPU cluster architectures.

Dhiraj D. Kalamkar, Evangelos Georganas, Sudarshan Srinivasan, Jianping Chen, Mikhail Shiryaev, Alexander Heinecke

Proceedings of the International Conference for High Performance Computing, 2020

Harnessing Deep Learning via a Single Building Block.

Evangelos Georganas, Kunal Banerjee, Dhiraj D. Kalamkar, Sasikanth Avancha, Anand Venkat, Michael J. Anderson, Greg Henry, Hans Pabst, Alexander Heinecke

Proceedings of the 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2020

2019

Optimizing Deep Learning RNN Topologies on Intel Architecture.

Kunal Banerjee, Evangelos Georganas, Dhiraj D. Kalamkar, Barukh Ziv, Eden Segal, Cristina Anderson, Alexander Heinecke

Supercomput. Front. Innov., 2019

K-TanH: Hardware Efficient Activations For Deep Learning.

Abhisek Kundu, Sudarshan Srinivasan, Eric C. Qin, Dhiraj D. Kalamkar, Naveen K. Mellempudi, Dipankar Das, Kunal Banerjee, Bharat Kaul, Pradeep Dubey

CoRR, 2019

High-Performance Deep Learning via a Single Building Block.

Evangelos Georganas, Kunal Banerjee, Dhiraj D. Kalamkar, Sasikanth Avancha, Anand Venkat, Michael J. Anderson, Greg Henry, Hans Pabst, Alexander Heinecke

CoRR, 2019

A Study of BFLOAT16 for Deep Learning Training.

Dhiraj D. Kalamkar, Dheevatsa Mudigere, Naveen Mellempudi, Dipankar Das, Kunal Banerjee, Sasikanth Avancha, Dharma Teja Vooturi, Nataraj Jammalamadaka, Jianyu Huang, Hector Yuen, Jiyan Yang, Jongsoo Park, Alexander Heinecke, Evangelos Georganas, Sudarshan Srinivasan, Abhisek Kundu, Misha Smelyanskiy, Bharat Kaul, Pradeep Dubey

CoRR, 2019

Training Google Neural Machine Translation on an Intel CPU Cluster.

Dhiraj D. Kalamkar, Kunal Banerjee, Sudarshan Srinivasan, Srinivas Sridharan, Evangelos Georganas, Mikhail E. Smorkalov, Cong Xu, Alexander Heinecke

Proceedings of the 2019 IEEE International Conference on Cluster Computing, 2019

2018

On Scale-out Deep Learning Training for Cloud and HPC.

Srinivas Sridharan, Karthikeyan Vaidyanathan, Dhiraj D. Kalamkar, Dipankar Das, Mikhail E. Smorkalov, Mikhail Shiryaev, Dheevatsa Mudigere, Naveen Mellempudi, Sasikanth Avancha, Bharat Kaul, Pradeep Dubey

CoRR, 2018

Anatomy of high-performance deep learning convolutions on SIMD architectures.

Evangelos Georganas, Sasikanth Avancha, Kunal Banerjee, Dhiraj D. Kalamkar, Greg Henry, Hans Pabst, Alexander Heinecke

Proceedings of the International Conference for High Performance Computing, 2018

Mixed Precision Training of Convolutional Neural Networks using Integer Operations.

Dipankar Das, Naveen Mellempudi, Dheevatsa Mudigere, Dhiraj D. Kalamkar, Sasikanth Avancha, Kunal Banerjee, Srinivas Sridharan, Karthik Vaidyanathan, Bharat Kaul, Evangelos Georganas, Alexander Heinecke, Pradeep Dubey, Jesús Corbal, Nikita Shustrov, Roman Dubtsov, Evarist Fomenko, Vadim O. Pirogov

Proceedings of the 6th International Conference on Learning Representations, 2018

2016

Optimizations in a high-performance conjugate gradient benchmark for IA-based multi- and many-core processors.

Jongsoo Park, Mikhail Smelyanskiy, Karthikeyan Vaidyanathan, Alexander Heinecke, Dhiraj D. Kalamkar, Md. Mostofa Ali Patwary, Vadim O. Pirogov, Pradeep Dubey, Xing Liu, Carlos Rosales, Cyril Mazauric, Christopher S. Daley

Int. J. High Perform. Comput. Appl., 2016

Distributed Deep Learning Using Synchronous Stochastic Gradient Descent.

Dipankar Das, Sasikanth Avancha, Dheevatsa Mudigere, Karthikeyan Vaidyanathan, Srinivas Sridharan, Dhiraj D. Kalamkar, Bharat Kaul, Pradeep Dubey

CoRR, 2016

Optimizing Wilson-Dirac Operator and Linear Solvers for Intel® KNL.

Bálint Joó, Dhiraj D. Kalamkar, Thorsten Kurth, Karthikeyan Vaidyanathan, Aaron Walden

Proceedings of the High Performance Computing, 2016

2015

Improving concurrency and asynchrony in multithreaded MPI applications using software offloading.

Karthikeyan Vaidyanathan, Dhiraj D. Kalamkar, Kiran Pamnany, Jeff R. Hammond, Pavan Balaji, Dipankar Das, Jongsoo Park, Bálint Joó

Proceedings of the International Conference for High Performance Computing, 2015

2014

Enabling Efficient Multithreaded MPI Communication through a Library-Based Implementation of MPI Endpoints.

Srinivas Sridharan, James Dinan, Dhiraj D. Kalamkar

Proceedings of the International Conference for High Performance Computing, 2014

Efficient Shared-Memory Implementation of High-Performance Conjugate Gradient Benchmark and its Application to Unstructured Matrices.

Jongsoo Park, Mikhail Smelyanskiy, Karthikeyan Vaidyanathan, Alexander Heinecke, Dhiraj D. Kalamkar, Xing Liu, Md. Mostofa Ali Patwary, Yutong Lu, Pradeep Dubey

Proceedings of the International Conference for High Performance Computing, 2014

Lattice QCD with Domain Decomposition on Intel® Xeon Phi Co-Processors.

Simon Heybrock, Bálint Joó, Dhiraj D. Kalamkar, Mikhail Smelyanskiy, Karthikeyan Vaidyanathan, Tilo Wettig, Pradeep Dubey

Proceedings of the International Conference for High Performance Computing, 2014

Improving Communication Performance and Scalability of Native Applications on Intel Xeon Phi Coprocessor Clusters.

Karthikeyan Vaidyanathan, Kiran Pamnany, Dhiraj D. Kalamkar, Alexander Heinecke, Mikhail Smelyanskiy, Jongsoo Park, Daehyun Kim, Aniruddha G. Shet, Bharat Kaul, Bálint Joó, Pradeep Dubey

Proceedings of the 2014 IEEE 28th International Parallel and Distributed Processing Symposium, 2014

2013

Lattice QCD on Intel® Xeon PhiTM Coprocessors.

Bálint Joó, Dhiraj D. Kalamkar, Karthikeyan Vaidyanathan, Mikhail Smelyanskiy, Kiran Pamnany, Victor W. Lee, Pradeep Dubey, William A. Watson III

Proceedings of the Supercomputing - 28th International Supercomputing Conference, 2013

2012

Optimization of geometric multigrid for emerging multi- and manycore processors.

Samuel Williams, Dhiraj D. Kalamkar, Amik Singh, Anand M. Deshpande, Brian van Straalen, Mikhail Smelyanskiy, Ann S. Almgren, Pradeep Dubey, John Shalf, Leonid Oliker

Proceedings of the SC Conference on High Performance Computing Networking, 2012

Analysis and Optimization of Financial Analytics Benchmark on Modern Multi- and Many-core IA-Based Architectures.

Mikhail Smelyanskiy, Jason Sewall, Dhiraj D. Kalamkar, Nadathur Satish, Pradeep Dubey, Nikita Astafiev, Ilya Burylov, Andrey Nikolaev, Sergey Maidanov, Shuo Li, Sunil Kulkarni, Charles H. Finan, Ekaterina Gonina

Proceedings of the 2012 SC Companion: High Performance Computing, 2012

High Performance Non-uniform FFT on Modern X86-based Multi-core Systems.

Dhiraj D. Kalamkar, Joshua D. Trzasko, Srinivas Sridharan, Mikhail Smelyanskiy, Daehyun Kim, Armando Manduca, Yunhong Shu, Matt A. Bernstein, Bharat Kaul, Pradeep Dubey

Proceedings of the 26th IEEE International Parallel and Distributed Processing Symposium, 2012

2007

Simplifying Active Memory Clusters by Leveraging Directory Protocol Threads.

Dhiraj D. Kalamkar, Mainak Chaudhuri, Mark A. Heinrich

Proceedings of the 2007 IEEE International Symposium on Performance Analysis of Systems and Software, 2007