Dhiraj D. Kalamkar

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 26

Top Topics

Conjugate Gradient

Weakly Supervised

Top Venues

Publications

Evangelos Georganas, Dhiraj D. Kalamkar, Kirill Voronin, Abhisek Kundu, Antonio Noack, Hans Pabst, Alexander Breuer, Alexander Heinecke
Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures. IPDPS (2024)
Evangelos Georganas, Dhiraj D. Kalamkar, Kirill Voronin, Antonio Noack, Hans Pabst, Alexander Breuer, Alexander Heinecke
Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures. CoRR (2023)
Narendra Chaudhary, Sanchit Misra, Dhiraj D. Kalamkar, Alexander Heinecke, Evangelos Georganas, Barukh Ziv, Menachem Adelman, Bharat Kaul
Accelerating Deep Learning based Identification of Chromatin Accessibility from noisy ATAC-seq Data. IPDPS Workshops (2022)
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, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty, Hans Pabst, Brian Retford, Barukh Ziv, Alexander Heinecke
Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning and HPC Workloads. Frontiers Appl. Math. Stat. 8 (2022)
Narendra Chaudhary, Sanchit Misra, Dhiraj D. Kalamkar, Alexander Heinecke, Evangelos Georganas, Barukh Ziv, Menachem Adelman, Bharat Kaul
Efficient and Generic 1D Dilated Convolution Layer for Deep Learning. CoRR (2021)
Vasimuddin Md, Sanchit Misra, Guixiang Ma, Ramanarayan Mohanty, Evangelos Georganas, Alexander Heinecke, Dhiraj D. Kalamkar, Nesreen K. Ahmed, Sasikanth Avancha
DistGNN: scalable distributed training for large-scale graph neural networks. SC (2021)
Vasimuddin Md, Sanchit Misra, Guixiang Ma, Ramanarayan Mohanty, Evangelos Georganas, Alexander Heinecke, Dhiraj D. Kalamkar, Nesreen K. Ahmed, Sasikanth Avancha
DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks. CoRR (2021)
Evangelos Georganas, Dhiraj D. Kalamkar, Sasikanth Avancha, Menachem Adelman, Cristina Anderson, Alexander Breuer, Narendra Chaudhary, Abhisek Kundu, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty, Hans Pabst, Barukh Ziv, Alexander Heinecke
Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning Workloads. CoRR (2021)
Evangelos Georganas, Dhiraj D. Kalamkar, Sasikanth Avancha, Menachem Adelman, Cristina Anderson, Alexander Breuer, Jeremy Bruestle, Narendra Chaudhary, Abhisek Kundu, Denise Kutnick, Frank Laub, Vasimuddin Md, Sanchit Misra, Ramanarayan Mohanty, Hans Pabst, Barukh Ziv, Alexander Heinecke
Tensor processing primitives: a programming abstraction for efficiency and portability in deep learning workloads. SC (2021)
Dhiraj D. Kalamkar, Evangelos Georganas, Sudarshan Srinivasan, Jianping Chen, Mikhail Shiryaev, Alexander Heinecke
Optimizing Deep Learning Recommender Systems' Training On CPU Cluster Architectures. CoRR (2020)
Dhiraj D. Kalamkar, Evangelos Georganas, Sudarshan Srinivasan, Jianping Chen, Mikhail Shiryaev, Alexander Heinecke
Optimizing deep learning recommender systems training on CPU cluster architectures. SC (2020)
Evangelos Georganas, Kunal Banerjee, Dhiraj D. Kalamkar, Sasikanth Avancha, Anand Venkat, Michael J. Anderson, Greg Henry, Hans Pabst, Alexander Heinecke
Harnessing Deep Learning via a Single Building Block. IPDPS (2020)
Kunal Banerjee, Evangelos Georganas, Dhiraj D. Kalamkar, Barukh Ziv, Eden Segal, Cristina Anderson, Alexander Heinecke
Optimizing Deep Learning RNN Topologies on Intel Architecture. Supercomput. Front. Innov. 6 (3) (2019)
Abhisek Kundu, Sudarshan Srinivasan, Eric C. Qin, Dhiraj D. Kalamkar, Naveen K. Mellempudi, Dipankar Das, Kunal Banerjee, Bharat Kaul, Pradeep Dubey
K-TanH: Hardware Efficient Activations For Deep Learning. CoRR (2019)
Evangelos Georganas, Kunal Banerjee, Dhiraj D. Kalamkar, Sasikanth Avancha, Anand Venkat, Michael J. Anderson, Greg Henry, Hans Pabst, Alexander Heinecke
High-Performance Deep Learning via a Single Building Block. CoRR (2019)
Dhiraj D. Kalamkar, Kunal Banerjee, Sudarshan Srinivasan, Srinivas Sridharan, Evangelos Georganas, Mikhail E. Smorkalov, Cong Xu, Alexander Heinecke
Training Google Neural Machine Translation on an Intel CPU Cluster. CLUSTER (2019)
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
A Study of BFLOAT16 for Deep Learning Training. CoRR (2019)
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
Mixed Precision Training of Convolutional Neural Networks using Integer Operations. ICLR (Poster) (2018)
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
Mixed Precision Training of Convolutional Neural Networks using Integer Operations. CoRR (2018)
Srinivas Sridharan, Karthikeyan Vaidyanathan, Dhiraj D. Kalamkar, Dipankar Das, Mikhail E. Smorkalov, Mikhail Shiryaev, Dheevatsa Mudigere, Naveen Mellempudi, Sasikanth Avancha, Bharat Kaul, Pradeep Dubey
On Scale-out Deep Learning Training for Cloud and HPC. CoRR (2018)
Evangelos Georganas, Sasikanth Avancha, Kunal Banerjee, Dhiraj D. Kalamkar, Greg Henry, Hans Pabst, Alexander Heinecke
Anatomy Of High-Performance Deep Learning Convolutions On SIMD Architectures. CoRR (2018)
Evangelos Georganas, Sasikanth Avancha, Kunal Banerjee, Dhiraj D. Kalamkar, Greg Henry, Hans Pabst, Alexander Heinecke
Anatomy of high-performance deep learning convolutions on SIMD architectures. SC (2018)
Bálint Joó, Dhiraj D. Kalamkar, Thorsten Kurth, Karthikeyan Vaidyanathan, Aaron Walden
Optimizing Wilson-Dirac Operator and Linear Solvers for Intel® KNL. ISC Workshops (2016)
Dipankar Das, Sasikanth Avancha, Dheevatsa Mudigere, Karthikeyan Vaidyanathan, Srinivas Sridharan, Dhiraj D. Kalamkar, Bharat Kaul, Pradeep Dubey
Distributed Deep Learning Using Synchronous Stochastic Gradient Descent. CoRR (2016)
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
Optimizations in a high-performance conjugate gradient benchmark for IA-based multi- and many-core processors. Int. J. High Perform. Comput. Appl. 30 (1) (2016)
Karthikeyan Vaidyanathan, Dhiraj D. Kalamkar, Kiran Pamnany, Jeff R. Hammond, Pavan Balaji, Dipankar Das, Jongsoo Park, Bálint Joó
Improving concurrency and asynchrony in multithreaded MPI applications using software offloading. SC (2015)
Karthikeyan Vaidyanathan, Kiran Pamnany, Dhiraj D. Kalamkar, Alexander Heinecke, Mikhail Smelyanskiy, Jongsoo Park, Daehyun Kim, Aniruddha G. Shet, Bharat Kaul, Bálint Joó, Pradeep Dubey
Improving Communication Performance and Scalability of Native Applications on Intel Xeon Phi Coprocessor Clusters. IPDPS (2014)
Srinivas Sridharan, James Dinan, Dhiraj D. Kalamkar
Enabling Efficient Multithreaded MPI Communication through a Library-Based Implementation of MPI Endpoints. SC (2014)
Jongsoo Park, Mikhail Smelyanskiy, Karthikeyan Vaidyanathan, Alexander Heinecke, Dhiraj D. Kalamkar, Xing Liu, Md. Mostofa Ali Patwary, Yutong Lu, Pradeep Dubey
Efficient Shared-Memory Implementation of High-Performance Conjugate Gradient Benchmark and its Application to Unstructured Matrices. SC (2014)
Simon Heybrock, Bálint Joó, Dhiraj D. Kalamkar, Mikhail Smelyanskiy, Karthikeyan Vaidyanathan, Tilo Wettig, Pradeep Dubey
Lattice QCD with Domain Decomposition on Intel® Xeon Phi Co-Processors. SC (2014)
Bálint Joó, Dhiraj D. Kalamkar, Karthikeyan Vaidyanathan, Mikhail Smelyanskiy, Kiran Pamnany, Victor W. Lee, Pradeep Dubey, William A. Watson III
Lattice QCD on Intel® Xeon PhiTM Coprocessors. ISC (2013)
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
Analysis and Optimization of Financial Analytics Benchmark on Modern Multi- and Many-core IA-Based Architectures. SC Companion (2012)
Samuel Williams, Dhiraj D. Kalamkar, Amik Singh, Anand M. Deshpande, Brian van Straalen, Mikhail Smelyanskiy, Ann S. Almgren, Pradeep Dubey, John Shalf, Leonid Oliker
Optimization of geometric multigrid for emerging multi- and manycore processors. SC (2012)
Dhiraj D. Kalamkar, Joshua D. Trzasko, Srinivas Sridharan, Mikhail Smelyanskiy, Daehyun Kim, Armando Manduca, Yunhong Shu, Matt A. Bernstein, Bharat Kaul, Pradeep Dubey
High Performance Non-uniform FFT on Modern X86-based Multi-core Systems. IPDPS (2012)
Dhiraj D. Kalamkar, Mainak Chaudhuri, Mark A. Heinrich
Simplifying Active Memory Clusters by Leveraging Directory Protocol Threads. ISPASS (2007)