Srinivas Sridharan
Srinivas Sridharan
Research Scientist, Intel
Verified email at
Cited by
Cited by
Distributed deep learning using synchronous stochastic gradient descent
D Das, S Avancha, D Mudigere, K Vaidynathan, S Sridharan, D Kalamkar, ...
arXiv preprint arXiv:1602.06709, 2016
Mixed precision training of convolutional neural networks using integer operations
D Das, N Mellempudi, D Mudigere, D Kalamkar, S Avancha, K Banerjee, ...
arXiv preprint arXiv:1802.00930, 2018
Deep learning at 15pf: supervised and semi-supervised classification for scientific data
T Kurth, J Zhang, N Satish, E Racah, I Mitliagkas, MMA Patwary, T Malas, ...
Proceedings of the International Conference for High Performance Computing …, 2017
Enabling efficient multithreaded MPI communication through a library-based implementation of MPI endpoints
S Sridharan, J Dinan, DD Kalamkar
SC'14: Proceedings of the International Conference for High Performance …, 2014
Thread migration to improve synchronization performance
S Sridharan, B Keck, R Murphy, S Chandra, P Kogge
Workshop on Operating System Interference in High Performance Applications, 2006
On scale-out deep learning training for cloud and hpc
S Sridharan, K Vaidyanathan, D Kalamkar, D Das, ME Smorkalov, ...
arXiv preprint arXiv:1801.08030, 2018
Deep learning training in facebook data centers: Design of scale-up and scale-out systems
M Naumov, J Kim, D Mudigere, S Sridharan, X Wang, W Zhao, S Yilmaz, ...
arXiv preprint arXiv:2003.09518, 2020
Memory in processor: A novel design paradigm for supercomputing architectures
N Venkateswaran, WR Foundation, A Krishnan, SN Kumar, A Shriraman, ...
ACM SIGARCH Computer Architecture News 32 (3), 19-26, 2003
Abstraction layers for scalable distributed machine learning
DD Kalamkar, K Vaidyanathan, S Sridharan, D Das
US Patent 11,094,029, 2021
Exploring shared-memory optimizations for an unstructured mesh CFD application on modern parallel systems
D Mudigere, S Sridharan, A Deshpande, J Park, A Heinecke, ...
2015 IEEE International Parallel and Distributed Processing Symposium, 723-732, 2015
Comparing runtime systems with exascale ambitions using the parallel research kernels
RF Van der Wijngaart, A Kayi, JR Hammond, G Jost, TS John, ...
International Conference on High Performance Computing, 321-339, 2016
Fine-grain compute communication execution for deep learning frameworks
S Sridharan, D Mudigere
US Patent App. 15/869,502, 2018
Extending the BT NAS parallel benchmark to exascale computing
RF Van der Wijngaart, S Sridharan, VW Lee
SC'12: Proceedings of the International Conference on High Performance …, 2012
Evaluating synchronization techniques for light-weight multithreaded/multicore architectures
S Sridharan, A Rodrigues, P Kogge
Proceedings of the nineteenth annual ACM symposium on Parallel algorithms …, 2007
TensorFlow at Scale: Performance and productivity analysis of distributed training with Horovod, MLSL, and Cray PE ML
T Kurth, M Smorkalov, P Mendygral, S Sridharan, A Mathuriya
Concurrency and Computation: Practice and Experience 31 (16), e4989, 2019
Planning for performance: Enhancing achievable performance for MPI through persistent collective operations
DJ Holmes, B Morgan, A Skjellum, PV Bangalore, S Sridharan
Parallel Computing 81, 32-57, 2019
Planning for performance: persistent collective operations for MPI
B Morgan, DJ Holmes, A Skjellum, P Bangalore, S Sridharan
Proceedings of the 24th European MPI Users' Group Meeting, 1-11, 2017
Using the Parallel Research Kernels to study PGAS models
RF Van der Wijngaart, S Sridharan, A Kayi, G Jost, JR Hammond, ...
2015 9th International Conference on Partitioned Global Address Space …, 2015
High performance non-uniform FFT on modern x86-based multi-core systems
DD Kalamkar, JD Trzaskoz, S Sridharan, M Smelyanskiy, D Kim, ...
2012 IEEE 26th International Parallel and Distributed Processing Symposium …, 2012
Communication optimizations for distributed machine learning
S Sridharan, K Vaidyanathan, D Das, C Sakthivel, ME Smorkalov
US Patent App. 15/859,180, 2019
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