Saeed Rashidi

Georgia Institute of Technology

H-index: 6

North America-United States

About Saeed Rashidi

Saeed Rashidi, With an exceptional h-index of 6 and a recent h-index of 6 (since 2020), a distinguished researcher at Georgia Institute of Technology, specializes in the field of Computer Architecture, Networking, Compilers, Scalable DNN Training Platforms, Memory Systems.

His recent articles reflect a diverse array of research interests and contributions to the field:

Efficient distributed inference of deep neural networks via restructuring and pruning

Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces

Astra-sim2. 0: Modeling hierarchical networks and disaggregated systems for large-model training at scale

Impact of RoCE congestion control policies on distributed training of DNNs

Themis: A network bandwidth-aware collective scheduling policy for distributed training of dl models

COMET: A comprehensive cluster design methodology for distributed deep learning training

Exploring multi-dimensional hierarchical network topologies for efficient distributed training of trillion parameter dl models

Enabling compute-communication overlap in distributed deep learning training platforms

Saeed Rashidi Information

University	Georgia Institute of Technology
Position	___
Citations(all)	140
Citations(since 2020)	136
Cited By	25
hIndex(all)	6
hIndex(since 2020)	6
i10Index(all)	5
i10Index(since 2020)	5
Email	Access Email
University Profile Page	Georgia Institute of Technology
Google Scholar	View Google Scholar Profile

Saeed Rashidi Skills & Research Interests

Computer Architecture

Networking

Compilers

Scalable DNN Training Platforms

Memory Systems

Top articles of Saeed Rashidi

Title	Journal	Author(s)	Publication Date
Efficient distributed inference of deep neural networks via restructuring and pruning	Proceedings of the AAAI Conference on Artificial Intelligence	Afshin Abdi Saeed Rashidi Faramarz Fekri Tushar Krishna	2023/6/26
Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces	arXiv preprint arXiv:2305.14516	Srinivas Sridharan Taekyung Heo Louis Feng Zhaodong Wang Matt Bergeron ...	2023/5/23
Astra-sim2. 0: Modeling hierarchical networks and disaggregated systems for large-model training at scale		William Won Taekyung Heo Saeed Rashidi Srinivas Sridharan Sudarshan Srinivasan ...	2023/4/23
Impact of RoCE congestion control policies on distributed training of DNNs		Tarannum Khan Saeed Rashidi Srinivas Sridharan Pallavi Shurpali Aditya Akella ...	2022/8/17
Themis: A network bandwidth-aware collective scheduling policy for distributed training of dl models		Saeed Rashidi William Won Sudarshan Srinivasan Srinivas Sridharan Tushar Krishna	2022/6/18
COMET: A comprehensive cluster design methodology for distributed deep learning training	arXiv preprint arXiv:2211.16648	Divya Kiran Kadiyala Saeed Rashidi Taekyung Heo Abhimanyu Rajeshkumar Bambhaniya Tushar Krishna ...	2022/11/30
Exploring multi-dimensional hierarchical network topologies for efficient distributed training of trillion parameter dl models	arXiv preprint arXiv:2109.11762	William Won Saeed Rashidi Sudarshan Srinivasan Tushar Krishna	2021/9/24
Enabling compute-communication overlap in distributed deep learning training platforms		Saeed Rashidi Matthew Denton Srinivas Sridharan Sudarshan Srinivasan Amoghavarsha Suresh ...	2021/6/14
Scalable distributed training of recommendation models: An astra-sim+ ns3 case-study with tcp/ip transport		Saeed Rashidi Pallavi Shurpali Srinivas Sridharan Naader Hassani Dheevatsa Mudigere ...	2020/8/19
Astra-sim: Enabling sw/hw co-design exploration for distributed dl training platforms		Saeed Rashidi Srinivas Sridharan Sudarshan Srinivasan Tushar Krishna	2020/8/23