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ORCIDAmey Agrawal
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2020 – today
- 2024
[c5]Amey Agrawal, Sameer Reddy, Satwik Bhattamishra, Venkata Prabhakara Sarath Nookala, Vidushi Vashishth, Kexin Rong, Alexey Tumanov:
Inshrinkerator: Compressing Deep Learning Training Checkpoints via Dynamic Quantization. SoCC 2024: 1012-1031- [c4]Amey Agrawal, Nitin Kedia, Jayashree Mohan, Ashish Panwar, Nipun Kwatra, Bhargav S. Gulavani, Ramachandran Ramjee, Alexey Tumanov:
VIDUR: A Large-Scale Simulation Framework for LLM Inference. MLSys 2024 - [c3]Amey Agrawal, Nitin Kedia, Ashish Panwar, Jayashree Mohan, Nipun Kwatra, Bhargav S. Gulavani, Alexey Tumanov, Ramachandran Ramjee:
Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve. OSDI 2024: 117-134 - [i9]Amey Agrawal, Nitin Kedia, Ashish Panwar, Jayashree Mohan, Nipun Kwatra, Bhargav S. Gulavani, Alexey Tumanov, Ramachandran Ramjee:
Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve. CoRR abs/2403.02310 (2024) - [i8]Amey Agrawal, Nitin Kedia, Jayashree Mohan, Ashish Panwar, Nipun Kwatra, Bhargav S. Gulavani, Ramachandran Ramjee, Alexey Tumanov:
Vidur: A Large-Scale Simulation Framework For LLM Inference. CoRR abs/2405.05465 (2024) - [i7]Amey Agrawal, Anmol Agarwal, Nitin Kedia, Jayashree Mohan, Souvik Kundu, Nipun Kwatra, Ramachandran Ramjee, Alexey Tumanov:
Metron: Holistic Performance Evaluation Framework for LLM Inference Systems. CoRR abs/2407.07000 (2024) - [i6]Amey Agrawal, Junda Chen, Íñigo Goiri, Ramachandran Ramjee, Chaojie Zhang, Alexey Tumanov, Esha Choukse:
Mnemosyne: Parallelization Strategies for Efficiently Serving Multi-Million Context Length LLM Inference Requests Without Approximations. CoRR abs/2409.17264 (2024) - 2023
- [i5]Amey Agrawal
, Sameer Reddy, Satwik Bhattamishra, Venkata Prabhakara Sarath Nookala, Vidushi Vashishth, Kexin Rong, Alexey Tumanov:
DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization. CoRR abs/2306.11800 (2023) - [i4]Amey Agrawal, Ashish Panwar, Jayashree Mohan, Nipun Kwatra, Bhargav S. Gulavani, Ramachandran Ramjee:
SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills. CoRR abs/2308.16369 (2023) - 2022
- [i3]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:
Singularity: Planet-Scale, Preemptive and Elastic Scheduling of AI Workloads. CoRR abs/2202.07848 (2022)
2010 – 2019
- 2019
- [c2]Amey Agrawal, Abhishek Dixit, Namrata A. Shettar, Darshil Kapadia, Vikram Agrawal, Rajat Gupta, Rohit Karlupia:
Delog: A High-Performance Privacy Preserving Log Filtering Framework. IEEE BigData 2019: 1739-1748 - [c1]Amey Agrawal, Rohit Karlupia, Rajat Gupta:
Logan: A Distributed Online Log Parser. ICDE 2019: 1946-1951 - [i2]Amey Agrawal, Abhishek Dixit, Darshil Kapadia, Rohit Karlupia, Vikram Agrawal, Rajat Gupta:
Delog: A Privacy Preserving Log Filtering Framework for Online Compute Platforms. CoRR abs/1902.04843 (2019) - [i1]Amey Agrawal, Rohit Karlupia:
Learning Digital Circuits: A Journey Through Weight Invariant Self-Pruning Neural Networks. CoRR abs/1909.00052 (2019)
Coauthor Index
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