Evaluating synchronization techniques for light-weight multithreaded/multicore architectures. - Srinivas Sridharan 002; Arun Rodrigues; Peter M. Kogge (SPAA, 2007)

High Performance Non-uniform FFT on Modern X86-based Multi-core Systems. - Dhiraj D. Kalamkar; Joshua D. Trzasko; Srinivas Sridharan 002; Mikhail Smelyanskiy; Daehyun Kim 001; Armando Manduca; Yunhong Shu; Matt A. Bernstein; Bharat Kaul; Pradeep Dubey (IPDPS, 2012)

Extending the BT NAS parallel benchmark to exascale computing. - Rob F. Van der Wijngaart; Srinivas Sridharan 002; Victor W. Lee (SC, 2012)

Enabling Efficient Multithreaded MPI Communication through a Library-Based Implementation of MPI Endpoints. - Srinivas Sridharan 002; James Dinan; Dhiraj D. Kalamkar (SC, 2014)

Exploring Shared-Memory Optimizations for an Unstructured Mesh CFD Application on Modern Parallel Systems. - Dheevatsa Mudigere; Srinivas Sridharan 002; Anand M. Deshpande; Jongsoo Park; Alexander Heinecke; Mikhail Smelyanskiy; Bharat Kaul; Pradeep Dubey; Dinesh K. Kaushik; David E. Keyes (IPDPS, 2015)

Comparing Runtime Systems with Exascale Ambitions Using the Parallel Research Kernels. - Rob F. Van der Wijngaart; Abdullah Kayi; Jeff R. Hammond; Gabriele Jost; Tom St. John; Srinivas Sridharan 002; Timothy G. Mattson; John Abercrombie; Jacob Nelson 001 (ISC, 2016)

Distributed Deep Learning Using Synchronous Stochastic Gradient Descent. - Dipankar Das 002; Sasikanth Avancha; Dheevatsa Mudigere; Karthikeyan Vaidyanathan; Srinivas Sridharan 002; Dhiraj D. Kalamkar; Bharat Kaul; Pradeep Dubey (CoRR, 2016)

Planning for performance: persistent collective operations for MPI. - Bradley Morgan; Daniel J. Holmes; Anthony Skjellum; Purushotham V. Bangalore; Srinivas Sridharan 002 (EuroMPI/USA, 2017)

Deep learning at 15PF: supervised and semi-supervised classification for scientific data. - Thorsten Kurth; Jian Zhang 049; Nadathur Satish; Evan Racah; Ioannis Mitliagkas; Md. Mostofa Ali Patwary; Tareq M. Malas; Narayanan Sundaram; Wahid Bhimji; Mikhail Smorkalov; Jack Deslippe; Mikhail Shiryaev; et al. (SC, 2017)

Mixed Precision Training of Convolutional Neural Networks using Integer Operations. - Dipankar Das 002; Naveen Mellempudi; Dheevatsa Mudigere; Dhiraj D. Kalamkar; Sasikanth Avancha; Kunal Banerjee 001; Srinivas Sridharan 002; Karthik Vaidyanathan; Bharat Kaul; Evangelos Georganas; Alexander Heinecke; Pradeep Dubey; et al. (ICLR (Poster), 2018)

On Scale-out Deep Learning Training for Cloud and HPC. - Srinivas Sridharan 002; Karthikeyan Vaidyanathan; Dhiraj D. Kalamkar; Dipankar Das 002; Mikhail E. Smorkalov; Mikhail Shiryaev; Dheevatsa Mudigere; Naveen Mellempudi; Sasikanth Avancha; Bharat Kaul; Pradeep Dubey (CoRR, 2018)

Training Google Neural Machine Translation on an Intel CPU Cluster. - Dhiraj D. Kalamkar; Kunal Banerjee 001; Sudarshan Srinivasan; Srinivas Sridharan 002; Evangelos Georganas; Mikhail E. Smorkalov; Cong Xu; Alexander Heinecke (CLUSTER, 2019)

TensorFlow at Scale: Performance and productivity analysis of distributed training with Horovod, MLSL, and Cray PE ML. - Thorsten Kurth; Mikhail Smorkalov; Peter Mendygral; Srinivas Sridharan 002; Amrita Mathuriya (Concurr. Comput. Pract. Exp., 2019)

Automatic Model Parallelism for Deep Neural Networks with Compiler and Hardware Support. - Sanket Tavarageri; Srinivas Sridharan 002; Bharat Kaul (CoRR, 2019)

Planning for performance: Enhancing achievable performance for MPI through persistent collective operations. - Daniel J. Holmes; Bradley Morgan; Anthony Skjellum; Purushotham V. Bangalore; Srinivas Sridharan 002 (Parallel Comput., 2019)

Scalable Distributed Training of Recommendation Models: An ASTRA-SIM + NS3 case-study with TCP/IP transport. - Saeed Rashidi; Pallavi Shurpali; Srinivas Sridharan 002; Naader Hassani; Dheevatsa Mudigere; Krishnakumar Nair; Misha Smelyanskiy; Tushar Krishna (Hot Interconnects, 2020)

ASTRA-SIM: Enabling SW/HW Co-Design Exploration for Distributed DL Training Platforms. - Saeed Rashidi; Srinivas Sridharan 002; Sudarshan Srinivasan; Tushar Krishna (ISPASS, 2020)

Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems. - Maxim Naumov; John Kim 001; Dheevatsa Mudigere; Srinivas Sridharan 002; Xiaodong Wang 020; Whitney Zhao; Serhat Yilmaz; Changkyu Kim; Hector Yuen; Mustafa Ozdal; Krishnakumar Nair; Isabel Gao; et al. (CoRR, 2020)

Efficient Communication Acceleration for Next-Gen Scale-up Deep Learning Training Platforms. - Saeed Rashidi; Srinivas Sridharan 002; Sudarshan Srinivasan; Matthew Denton; Tushar Krishna (CoRR, 2020)

Enabling Compute-Communication Overlap in Distributed Deep Learning Training Platforms. - Saeed Rashidi; Matthew Denton; Srinivas Sridharan 002; Sudarshan Srinivasan; Amoghavarsha Suresh; Jade Nie; Tushar Krishna (ISCA, 2021)

High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models. - Dheevatsa Mudigere; Yuchen Hao; Jianyu Huang; Andrew Tulloch; Srinivas Sridharan 002; Xing Liu; Mustafa Ozdal; Jade Nie; Jongsoo Park; Liang Luo; Jie Amy Yang; Leon Gao; et al. (CoRR, 2021)

Impact of RoCE Congestion Control Policies on Distributed Training of DNNs. - Tarannum Khan; Saeed Rashidi; Srinivas Sridharan 002; Pallavi Shurpali; Aditya Akella; Tushar Krishna (HOTI, 2022)

Software-hardware co-design for fast and scalable training of deep learning recommendation models. - Dheevatsa Mudigere; Yuchen Hao; Jianyu Huang; Zhihao Jia; Andrew Tulloch; Srinivas Sridharan 002; Xing Liu; Mustafa Ozdal; Jade Nie; Jongsoo Park; Liang Luo; Jie Amy Yang; et al. (ISCA, 2022)

Themis: a network bandwidth-aware collective scheduling policy for distributed training of DL models. - Saeed Rashidi; William Won; Sudarshan Srinivasan; Srinivas Sridharan 002; Tushar Krishna (ISCA, 2022)

Mystique: Enabling Accurate and Scalable Generation of Production AI Benchmarks. - Mingyu Liang; Wenyin Fu; Louis Feng; Zhongyi Lin; Pavani Panakanti; Shengbao Zheng; Srinivas Sridharan 002; Christina Delimitrou (ISCA, 2023)

ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale. - William Won; Taekyung Heo; Saeed Rashidi; Srinivas Sridharan 002; Sudarshan Srinivasan; Tushar Krishna (ISPASS, 2023)

Better Together: Jointly Optimizing ML Collective Scheduling and Execution Planning using SYNDICATE. - Kshiteej Mahajan; Ching-Hsiang Chu; Srinivas Sridharan 002; Aditya Akella (NSDI, 2023)

Mystique: Accurate and Scalable Production AI Benchmarks Generation. - Mingyu Liang; Wenyin Fu; Louis Feng; Zhongyi Lin; Pavani Panakanti; Srinivas Sridharan 002; Christina Delimitrou (CoRR, 2023)

Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces. - Srinivas Sridharan 002; Taekyung Heo; Louis Feng; Zhaodong Wang; Matt Bergeron; Wenyin Fu; Shengbao Zheng; Brian Coutinho; Saeed Rashidi; Changhai Man; Tushar Krishna (CoRR, 2023)

Towards a Standardized Representation for Deep Learning Collective Algorithms. - Jinsun Yoo; William Won; Meghan Cowan; Nan Jiang; Benjamin Klenk; Srinivas Sridharan 002; Tushar Krishna (HOTI, 2024)

LayerDAG: A Layerwise Autoregressive Diffusion Model for Directed Acyclic Graph Generation. - Mufei Li; Viraj Shitole; Eli Chien; Changhai Man; Zhaodong Wang; Srinivas Sridharan 002; Ying Zhang; Tushar Krishna; Pan Li 005 (CoRR, 2024)

Toward a Standardized Representation for Deep Learning Collective Algorithms. - Jinsun Yoo; William Won; Meghan Cowan; Nan Jiang; Benjamin Klenk; Srinivas Sridharan 002; Tushar Krishna (IEEE Micro, 2025)

COSMIC: Enabling Full-Stack Co-Design and Optimization of Distributed Machine Learning Systems. - Aditi Raju; Jared Ni; William Won; Changhai Man; Srivatsan Krishnan; Srinivas Sridharan 002; Amir Yazdanbakhsh; Tushar Krishna; Vijay Janapa Reddi (CoRR, 2025)