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Srinivas Sridharan 0002
Person information
- affiliation: Intel Corporation, Hillsboro, OR, USA
- affiliation: Intel Corporation, Bangalore, India
- affiliation: University of Notre Dame, IN, USA
Other persons with the same name
- Srinivas Sridharan — disambiguation page
- Srinivas Sridharan 0001 — Stevens Institute of Technology, Hoboken, NJ, USA (and 1 more)
- Srinivas Sridharan 0003 — University of California, San Diego, Department of Mechanical and Aerospace Engineering, CA, USA (and 1 more)
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2020 – today
- 2024
- [c19]Jinsun Yoo, William Won, Meghan Cowan, Nan Jiang, Benjamin Klenk, Srinivas Sridharan, Tushar Krishna:
Towards a Standardized Representation for Deep Learning Collective Algorithms. HOTI 2024: 33-36 - [i13]Jinsun Yoo, William Won, Meghan Cowan, Nan Jiang, Benjamin Klenk, Srinivas Sridharan, Tushar Krishna:
Towards a Standardized Representation for Deep Learning Collective Algorithms. CoRR abs/2408.11008 (2024) - 2023
- [c18]William Won
, Taekyung Heo, Saeed Rashidi, Srinivas Sridharan, Sudarshan Srinivasan, Tushar Krishna:
ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale. ISPASS 2023: 283-294 - [c17]Kshiteej Mahajan, Ching-Hsiang Chu, Srinivas Sridharan, Aditya Akella:
Better Together: Jointly Optimizing ML Collective Scheduling and Execution Planning using SYNDICATE. NSDI 2023: 809-824 - [i12]William Won, Taekyung Heo, Saeed Rashidi, Srinivas Sridharan, Sudarshan Srinivasan, Tushar Krishna:
ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale. CoRR abs/2303.14006 (2023) - [i11]Srinivas Sridharan, Taekyung Heo, Louis Feng, Zhaodong Wang, Matt Bergeron, Wenyin Fu, Shengbao Zheng, Brian Coutinho, Saeed Rashidi, Changhai Man, Tushar Krishna:
Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces. CoRR abs/2305.14516 (2023) - 2022
- [c16]Tarannum Khan, Saeed Rashidi, Srinivas Sridharan, Pallavi Shurpali, Aditya Akella, Tushar Krishna:
Impact of RoCE Congestion Control Policies on Distributed Training of DNNs. HOTI 2022: 39-48 - [c15]Saeed Rashidi, William Won
Themis: a network bandwidth-aware collective scheduling policy for distributed training of DL models. ISCA 2022: 581-596 - [c14]Dheevatsa Mudigere, Yuchen Hao, Jianyu Huang, Zhihao Jia, Andrew Tulloch, Srinivas Sridharan, Xing Liu, Mustafa Ozdal, Jade Nie, Jongsoo Park, Liang Luo, Jie Amy Yang, Leon Gao, Dmytro Ivchenko, Aarti Basant, Yuxi Hu, Jiyan Yang, Ehsan K. Ardestani, Xiaodong Wang, Rakesh Komuravelli, Ching-Hsiang Chu, Serhat Yilmaz, Huayu Li, Jiyuan Qian, Zhuobo Feng, Yinbin Ma, Junjie Yang, Ellie Wen
Software-hardware co-design for fast and scalable training of deep learning recommendation models. ISCA 2022: 993-1011 - [i10]Tarannum Khan, Saeed Rashidi, Srinivas Sridharan, Pallavi Shurpali, Aditya Akella, Tushar Krishna:
Impact of RoCE Congestion Control Policies on Distributed Training of DNNs. CoRR abs/2207.10898 (2022) - 2021
- [c13]Saeed Rashidi, Matthew Denton, Srinivas Sridharan, Sudarshan Srinivasan, Amoghavarsha Suresh, Jade Nie, Tushar Krishna:
Enabling Compute-Communication Overlap in Distributed Deep Learning Training Platforms. ISCA 2021: 540-553 - [i9]Dheevatsa Mudigere, Yuchen Hao, Jianyu Huang, Andrew Tulloch, Srinivas Sridharan, Xing Liu, Mustafa Ozdal, Jade Nie, Jongsoo Park, Liang Luo, Jie Amy Yang, Leon Gao, Dmytro Ivchenko, Aarti Basant, Yuxi Hu, Jiyan Yang, Ehsan K. Ardestani, Xiaodong Wang, Rakesh Komuravelli, Ching-Hsiang Chu, Serhat Yilmaz, Huayu Li, Jiyuan Qian, Zhuobo Feng, Yinbin Ma, Junjie Yang, Ellie Wen, Hong Li, Lin Yang, Chonglin Sun, Whitney Zhao, Dimitry Melts, Krishna Dhulipala, K. R. Kishore, Tyler Graf, Assaf Eisenman, Kiran Kumar Matam, Adi Gangidi, Guoqiang Jerry Chen, Manoj Krishnan, Avinash Nayak, Krishnakumar Nair, Bharath Muthiah, Mahmoud khorashadi, Pallab Bhattacharya, Petr Lapukhov, Maxim Naumov, Lin Qiao, Mikhail Smelyanskiy, Bill Jia, Vijay Rao:
High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models. CoRR abs/2104.05158 (2021) - [i8]Saeed Rashidi, William Won, Sudarshan Srinivasan, Srinivas Sridharan, Tushar Krishna:
Themis: A Network Bandwidth-Aware Collective Scheduling Policy for Distributed Training of DL Models. CoRR abs/2110.04478 (2021) - 2020
- [c12]Saeed Rashidi, Pallavi Shurpali, Srinivas Sridharan, Naader Hassani, Dheevatsa Mudigere, Krishnakumar Nair, Misha Smelyanski, Tushar Krishna:
Scalable Distributed Training of Recommendation Models: An ASTRA-SIM + NS3 case-study with TCP/IP transport. Hot Interconnects 2020: 33-42 - [c11]Saeed Rashidi, Srinivas Sridharan, Sudarshan Srinivasan, Tushar Krishna:
ASTRA-SIM: Enabling SW/HW Co-Design Exploration for Distributed DL Training Platforms. ISPASS 2020: 81-92 - [i7]Maxim Naumov, John Kim, Dheevatsa Mudigere, Srinivas Sridharan, Xiaodong Wang, Whitney Zhao, Serhat Yilmaz, Changkyu Kim, Hector Yuen, Mustafa Ozdal, Krishnakumar Nair, Isabel Gao, Bor-Yiing Su, Jiyan Yang, Mikhail Smelyanskiy:
Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems. CoRR abs/2003.09518 (2020) - [i6]Saeed Rashidi, Srinivas Sridharan, Sudarshan Srinivasan, Matthew Denton, Tushar Krishna:
Efficient Communication Acceleration for Next-Gen Scale-up Deep Learning Training Platforms. CoRR abs/2007.00156 (2020)
2010 – 2019
- 2019
- [j2]Thorsten Kurth
TensorFlow at Scale: Performance and productivity analysis of distributed training with Horovod, MLSL, and Cray PE ML. Concurr. Comput. Pract. Exp. 31(16) (2019) - [j1]Daniel J. Holmes
Planning for performance: Enhancing achievable performance for MPI through persistent collective operations. Parallel Comput. 81: 32-57 (2019) - [c10]Dhiraj D. Kalamkar, Kunal Banerjee, Sudarshan Srinivasan, Srinivas Sridharan, Evangelos Georganas, Mikhail E. Smorkalov
Training Google Neural Machine Translation on an Intel CPU Cluster. CLUSTER 2019: 1-10 - [i5]Sanket Tavarageri, Srinivas Sridharan, Bharat Kaul:
Automatic Model Parallelism for Deep Neural Networks with Compiler and Hardware Support. CoRR abs/1906.08168 (2019) - 2018
- [c9]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 - [i4]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 abs/1801.08030 (2018) - [i3]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 abs/1802.00930 (2018) - 2017
- [c8]Bradley Morgan, Daniel J. Holmes, Anthony Skjellum, Purushotham V. Bangalore, Srinivas Sridharan:
Planning for performance: persistent collective operations for MPI. EuroMPI/USA 2017: 4:1-4:11 - [c7]Thorsten Kurth, Jian Zhang, Nadathur Satish, Evan Racah, Ioannis Mitliagkas, Md. Mostofa Ali Patwary, Tareq M. Malas, Narayanan Sundaram, Wahid Bhimji, Mikhail Smorkalov
Deep learning at 15PF: supervised and semi-supervised classification for scientific data. SC 2017: 7 - [i2]Thorsten Kurth, Jian Zhang, Nadathur Satish, Ioannis Mitliagkas, Evan Racah, Md. Mostofa Ali Patwary, Tareq M. Malas, Narayanan Sundaram, Wahid Bhimji, Mikhail Smorkalov, Jack Deslippe, Mikhail Shiryaev, Srinivas Sridharan, Prabhat, Pradeep Dubey:
Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data. CoRR abs/1708.05256 (2017) - 2016
- [c6]Rob F. Van der Wijngaart, Abdullah Kayi, Jeff R. Hammond, Gabriele Jost, Tom St. John, Srinivas Sridharan, Timothy G. Mattson, John Abercrombie, Jacob Nelson:
Comparing Runtime Systems with Exascale Ambitions Using the Parallel Research Kernels. ISC 2016: 321-339 - [i1]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 abs/1602.06709 (2016) - 2015
- [c5]Dheevatsa Mudigere, Srinivas Sridharan, Anand M. Deshpande, Jongsoo Park, Alexander Heinecke, Mikhail Smelyanskiy, Bharat Kaul, Pradeep Dubey, Dinesh K. Kaushik, David E. Keyes
Exploring Shared-Memory Optimizations for an Unstructured Mesh CFD Application on Modern Parallel Systems. IPDPS 2015: 723-732 - 2014
- [c4]Srinivas Sridharan, James Dinan, Dhiraj D. Kalamkar:
Enabling Efficient Multithreaded MPI Communication through a Library-Based Implementation of MPI Endpoints. SC 2014: 487-498 - 2012
- [c3]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: 449-460 - [c2]Rob F. Van der Wijngaart, Srinivas Sridharan, Victor W. Lee:
Extending the BT NAS parallel benchmark to exascale computing. SC 2012: 94
2000 – 2009
- 2007
- [c1]Srinivas Sridharan, Arun Rodrigues, Peter M. Kogge:
Evaluating synchronization techniques for light-weight multithreaded/multicore architectures. SPAA 2007: 57-58
Coauthor Index
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last updated on 2024-09-28 01:26 CEST by the dblp team
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