Delog: A High-Performance Privacy Preserving Log Filtering Framework. - Amey Agrawal; Abhishek Dixit; Namrata A. Shettar; Darshil Kapadia; Vikram Agrawal; Rajat Gupta; Rohit Karlupia (IEEE BigData, 2019)

Logan: A Distributed Online Log Parser. - Amey Agrawal; Rohit Karlupia; Rajat Gupta (ICDE, 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)

Learning Digital Circuits: A Journey Through Weight Invariant Self-Pruning Neural Networks. - Amey Agrawal; Rohit Karlupia (CoRR, 2019)

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; et al. (CoRR, 2022)

DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization. - Amey Agrawal; Sameer Reddy; Satwik Bhattamishra; Venkata Prabhakara Sarath Nookala; Vidushi Vashishth; Kexin Rong 001; Alexey Tumanov (CoRR, 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)

Inshrinkerator: Compressing Deep Learning Training Checkpoints via Dynamic Quantization. - Amey Agrawal; Sameer Reddy; Satwik Bhattamishra; Venkata Prabhakara Sarath Nookala; Vidushi Vashishth; Kexin Rong 001; Alexey Tumanov (SoCC, 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 (MLSys, 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 (OSDI, 2024)

Metron: Holistic Performance Evaluation Framework for LLM Inference Systems. - Amey Agrawal; Anmol Agarwal; Nitin Kedia; Jayashree Mohan; Souvik Kundu 009; Nipun Kwatra; Ramachandran Ramjee; Alexey Tumanov (CoRR, 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)

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, 2025)

On Evaluating Performance of LLM Inference Serving Systems. - Amey Agrawal; Nitin Kedia; Anmol Agarwal; Jayashree Mohan; Nipun Kwatra; Souvik Kundu 009; Ramachandran Ramjee; Alexey Tumanov (CoRR, 2025)