Saeed Rashidi

According to our database, Saeed Rashidi authored at least 14 papers between 2018 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

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

Srinivas Sridharan, Taekyung Heo, Louis Feng, Zhaodong Wang, Matt Bergeron, Wenyin Fu, Shengbao Zheng, Brian Coutinho, Saeed Rashidi, Changhai Man, Tushar Krishna

CoRR, 2023

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, Tushar Krishna

Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software, 2023

Efficient Distributed Inference of Deep Neural Networks via Restructuring and Pruning.

Afshin Abdi, Saeed Rashidi, Faramarz Fekri, Tushar Krishna

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

COMET: A Comprehensive Cluster Design Methodology for Distributed Deep Learning Training.

Divya Kiran Kadiyala, Saeed Rashidi, Taekyung Heo, Abhimanyu Rajeshkumar Bambhaniya, Tushar Krishna, Alexandros Daglis

CoRR, 2022

Themis: a network bandwidth-aware collective scheduling policy for distributed training of DL models.

Saeed Rashidi, William Won, Sudarshan Srinivasan, Srinivas Sridharan, Tushar Krishna

Proceedings of the ISCA '22: The 49th Annual International Symposium on Computer Architecture, New York, New York, USA, June 18, 2022

Impact of RoCE Congestion Control Policies on Distributed Training of DNNs.

Tarannum Khan, Saeed Rashidi, Srinivas Sridharan, Pallavi Shurpali, Aditya Akella, Tushar Krishna

Proceedings of the IEEE Symposium on High-Performance Interconnects, 2022

2021

Exploring Multi-dimensional Hierarchical Network Topologies for Efficient Distributed Training of Trillion Parameter DL Models.

William Won, Saeed Rashidi, Sudarshan Srinivasan, Tushar Krishna

CoRR, 2021

Enabling Compute-Communication Overlap in Distributed Deep Learning Training Platforms.

Saeed Rashidi, Matthew Denton, Srinivas Sridharan, Sudarshan Srinivasan, Amoghavarsha Suresh, Jade Nie, Tushar Krishna

Proceedings of the 48th ACM/IEEE Annual International Symposium on Computer Architecture, 2021

2020

Restructuring, Pruning, and Adjustment of Deep Models for Parallel Distributed Inference.

Afshin Abdi, Saeed Rashidi, Faramarz Fekri, Tushar Krishna

CoRR, 2020

Efficient Communication Acceleration for Next-Gen Scale-up Deep Learning Training Platforms.

Saeed Rashidi, Srinivas Sridharan, Sudarshan Srinivasan, Matthew Denton, Tushar Krishna

CoRR, 2020

ASTRA-SIM: Enabling SW/HW Co-Design Exploration for Distributed DL Training Platforms.

Saeed Rashidi, Srinivas Sridharan, Sudarshan Srinivasan, Tushar Krishna

Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software, 2020

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, Krishnakumar Nair, Misha Smelyanski, Tushar Krishna

Proceedings of the IEEE Symposium on High-Performance Interconnects, 2020

2019

A Survey on PCM Lifetime Enhancement Schemes.

Saeed Rashidi, Majid Jalili, Hamid Sarbazi-Azad

ACM Comput. Surv., 2019

2018

Improving MLC PCM Performance through Relaxed Write and Read for Intermediate Resistance Levels.

Saeed Rashidi, Majid Jalili, Hamid Sarbazi-Azad

ACM Trans. Archit. Code Optim., 2018