Amey Agrawal

According to our database, Amey Agrawal authored at least 16 papers between 2019 and 2026.

Collaborative distances:

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

2026

Revati: Transparent GPU-Free Time-Warp Emulation for LLM Serving.

Amey Agrawal, Mayank Yadav, Sukrit Kumar, Anirudha Agrawal, Garv Ghai, Souradeep Bera, Elton Pinto, Sirish Gambhira, Mohammad Adain, Kasra Sohrab, Chus Antonanzas, Alexey Tumanov

CoRR, January, 2026

Maya: Optimizing Deep Learning Training Workloads using GPU Runtime Emulation.

Srihas Yarlagadda, Amey Agrawal, Elton Pinto, Hakesh Darapaneni, Mitali Meratwal, Shivam Mittal, Pranavi Bajjuri, Srinivas Sridharan, Alexey Tumanov

Proceedings of the 21st European Conference on Computer Systems, 2026

2025

On Evaluating Performance of LLM Inference Serving Systems.

Amey Agrawal, Nitin Kedia, Anmol Agarwal, Jayashree Mohan, Nipun Kwatra, Souvik Kundu, Ramachandran Ramjee, Alexey Tumanov

CoRR, July, 2025

Maya: Optimizing Deep Learning Training Workloads using Emulated Virtual Accelerators.

Srihas Yarlagadda, Amey Agrawal, Elton Pinto, Hakesh Darapaneni, Mitali Meratwal, Shivam Mittal, Pranavi Bajjuri, Srinivas Sridharan, Alexey Tumanov

CoRR, March, 2025

2024

Mnemosyne: Parallelization Strategies for Efficiently Serving Multi-Million Context Length LLM Inference Requests Without Approximations.

Amey Agrawal, Junda Chen, Íñigo Goiri, Ramachandran Ramjee, Chaojie Zhang, Alexey Tumanov, Esha Choukse

CoRR, 2024

Metron: Holistic Performance Evaluation Framework for LLM Inference Systems.

Amey Agrawal, Anmol Agarwal, Nitin Kedia, Jayashree Mohan, Souvik Kundu, Nipun Kwatra, Ramachandran Ramjee, Alexey Tumanov

CoRR, 2024

Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve.

Amey Agrawal, Nitin Kedia, Ashish Panwar, Jayashree Mohan, Nipun Kwatra, Bhargav S. Gulavani, Alexey Tumanov, Ramachandran Ramjee

Proceedings of the 18th USENIX Symposium on Operating Systems Design and Implementation, 2024

VIDUR: A Large-Scale Simulation Framework for LLM Inference.

Amey Agrawal, Nitin Kedia, Jayashree Mohan, Ashish Panwar, Nipun Kwatra, Bhargav S. Gulavani, Ramachandran Ramjee, Alexey Tumanov

Proceedings of the Seventh Annual Conference on Machine Learning and Systems, 2024

Inshrinkerator: Compressing Deep Learning Training Checkpoints via Dynamic Quantization.

Amey Agrawal, Sameer Reddy, Satwik Bhattamishra, Venkata Prabhakara Sarath Nookala, Vidushi Vashishth, Kexin Rong, Alexey Tumanov

Proceedings of the 2024 ACM Symposium on Cloud Computing, 2024

2023

SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills.

Amey Agrawal, Ashish Panwar, Jayashree Mohan, Nipun Kwatra, Bhargav S. Gulavani, Ramachandran Ramjee

CoRR, 2023

DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization.

Amey Agrawal, Sameer Reddy, Satwik Bhattamishra, Venkata Prabhakara Sarath Nookala, Vidushi Vashishth, Kexin Rong, Alexey Tumanov

CoRR, 2023

2022

Singularity: Planet-Scale, Preemptive and Elastic Scheduling of AI Workloads.

Dharma Shukla, Muthian Sivathanu, Srinidhi Viswanatha, Bhargav S. Gulavani, Rimma Nehme, Amey Agrawal, Chen Chen, Nipun Kwatra, Ramachandran Ramjee, Pankaj Sharma, Atul Katiyar, Vipul Modi, Vaibhav Sharma, Abhishek Singh, Shreshth Singhal, Kaustubh Welankar, Lu Xun, Ravi Anupindi, Karthik Elangovan, Hasibur Rahman, Zhou Lin, Rahul Seetharaman, Cheng Xu, Eddie Ailijiang, Suresh Krishnappa, Mark Russinovich

CoRR, 2022

2019

Learning Digital Circuits: A Journey Through Weight Invariant Self-Pruning Neural Networks.

Amey Agrawal, Rohit Karlupia

CoRR, 2019

Delog: A Privacy Preserving Log Filtering Framework for Online Compute Platforms.

Amey Agrawal, Abhishek Dixit, Darshil Kapadia, Rohit Karlupia, Vikram Agrawal, Rajat Gupta

CoRR, 2019

Logan: A Distributed Online Log Parser.

Amey Agrawal, Rohit Karlupia, Rajat Gupta

Proceedings of the 35th IEEE International Conference on Data Engineering, 2019

Delog: A High-Performance Privacy Preserving Log Filtering Framework.

Amey Agrawal, Abhishek Dixit, Namrata A. Shettar, Darshil Kapadia, Vikram Agrawal, Rajat Gupta, Rohit Karlupia

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019