|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
|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
|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
|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
|Abstraction layers for scalable distributed machine learning|
DD Kalamkar, K Vaidyanathan, S Sridharan, D Das
US Patent App. 15/482,953, 2018
|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
|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: 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
|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
|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