Alexey Tumanov

According to our database, Alexey Tumanov authored at least 64 papers between 2007 and 2025.

Collaborative distances:

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

2025

Toward Weight Sharing Paradigm for Efficient AI: Training and Inference Serving.

Payman Behnam, Alind Khare, Dhruv Garg, Alexey Tumanov

ACM SIGOPS Oper. Syst. Rev., July, 2025

EMPIRIC: Exploring Missing Pieces in KV Cache Compression for Reducing Computation, Storage, and Latency in Long-Context LLM Inference.

Payman Behnam, Yaosheng Fu, Ritchie Zhao, Po-An Tsai, Zhiding Yu, Alexey Tumanov

ACM SIGOPS Oper. Syst. Rev., July, 2025

Efficient LLM Inference via Chunked Prefills.

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

ACM SIGOPS Oper. Syst. Rev., July, 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

RocketKV: Accelerating Long-Context LLM Inference via Two-Stage KV Cache Compression.

Payman Behnam, Yaosheng Fu, Ritchie Zhao, Po-An Tsai, Zhiding Yu, Alexey Tumanov

CoRR, February, 2025

∇QDARTS: Quantization as an Elastic Dimension to Differentiable NAS.

Payman Behnam, Uday Kamal, Sanjana Vijay Ganesh, Zhaoyi Li, Michael Jurado, Alind Khare, Igor Fedorov, Gaowen Liu, Alexey Tumanov

Trans. Mach. Learn. Res., 2025

SuperServe: Fine-Grained Inference Serving for Unpredictable Workloads.

Alind Khare, Dhruv Garg, Sukrit Kalra, Snigdha Grandhi, Ion Stoica, Alexey Tumanov

Proceedings of the 22nd USENIX Symposium on Networked Systems Design and Implementation, 2025

Client Availability in Federated Learning: It Matters!

Dhruv Garg, Debopam Sanyal, Myungjin Lee, Alexey Tumanov, Ada Gavrilovska

Proceedings of the 5th Workshop on Machine Learning and Systems, 2025

2024

PLUM: Improving Inference Efficiency By Leveraging Repetition-Sparsity Trade-Off.

Sachit Kuhar, Yash Jain, Alexey Tumanov

Trans. Mach. Learn. Res., 2024

Initialization using Update Approximation is a Silver Bullet for Extremely Efficient Low-Rank Fine-Tuning.

Kaustubh Ponkshe, Raghav Singhal, Eduard Gorbunov, Alexey Tumanov, Samuel Horváth, Praneeth Vepakomma

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

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

DεS: Delayed ε-Shrinking for Faster Once-For-All Training.

Aditya Annavajjala, Alind Khare, Animesh Agrawal, Igor Fedorov, Hugo Latapie, Myungjin Lee, 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

Harmonica: Hybrid Accelerator to Overcome Imperfections of Mixed-signal DNN Accelerators.

Payman Behnam, Uday Kamal, Ali Shafiee, Alexey Tumanov, Saibal Mukhopadhyay

Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2024

SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-device Inference.

Alind Khare, Animesh Agrawal, Aditya Annavajjala, Payman Behnam, Myungjin Lee, Hugo Latapie, Alexey Tumanov

Proceedings of the Computer Vision - ECCV 2024, 2024

DεpS: Delayed ε-Shrinking for Faster Once-for-All Training.

Aditya Annavajjala, Alind Khare, Animesh Agrawal, Igor Fedorov, Hugo Latapie, Myungjin Lee, Alexey Tumanov

Proceedings of the Computer Vision - ECCV 2024, 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

Hardware-Software Co-Design for Real-Time Latency-Accuracy Navigation in Tiny Machine Learning Applications.

Payman Behnam, Jianming Tong, Alind Khare, Yangyu Chen, Yue Pan, Pranav Gadikar, Abhimanyu Bambhaniya, Tushar Krishna, Alexey Tumanov

IEEE Micro, 2023

Signed Binarization: Unlocking Efficiency Through Repetition-Sparsity Trade-Off.

Sachit Kuhar, Yash Jain, Alexey Tumanov

CoRR, 2023

ABKD: Graph Neural Network Compression with Attention-Based Knowledge Distillation.

Anshul Ahluwalia, Rohit Das, Payman Behnam, Alind Khare, Pan Li, Alexey Tumanov

CoRR, 2023

Pareto-Secure Machine Learning (PSML): Fingerprinting and Securing Inference Serving Systems.

Debopam Sanyal, Jui-Tse Hung, Manav Agrawal, Prahlad Jasti, Shahab Nikkhoo, Somesh Jha, Tianhao Wang, Sibin Mohan, Alexey Tumanov

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

SuperFed: Weight Shared Federated Learning.

Alind Khare, Animesh Agrawal, Myungjin Lee, Alexey Tumanov

CoRR, 2023

Subgraph Stationary Hardware-Software Inference Co-Design.

Payman Behnam, Alexey Tumanov, Tushar Krishna, Pranav Gadikar, Yangyu Chen, Jianming Tong, Yue Pan, Abhimanyu Rajeshkumar Bambhaniya, Alind Khare

Proceedings of the Sixth Conference on Machine Learning and Systems, 2023

TransEHR: Self-Supervised Transformer for Clinical Time Series Data.

Yanbo Xu, Shangqing Xu, Manav Ramprassad, Alexey Tumanov, Chao Zhang

Proceedings of the Machine Learning for Health, 2023

2022

UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification.

Yanbo Xu, Alind Khare, Glenn Matlin, Monish Ramadoss, Rishikesan Kamaleswaran, Chao Zhang, Alexey Tumanov

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

CoDG-ReRAM: An Algorithm-Hardware Co-design to Accelerate Semi-Structured GNNs on ReRAM.

Yixuan Luo, Payman Behnam, Kiran Thorat, Zhuo Liu, Hongwu Peng, Shaoyi Huang, Shu Zhou, Omer Khan, Alexey Tumanov, Caiwen Ding, Tong Geng

Proceedings of the IEEE 40th International Conference on Computer Design, 2022

Automatic Parallelization of Python Programs for Distributed Heterogeneous Computing.

Jun Shirako, Akihiro Hayashi, Sri Raj Paul, Alexey Tumanov, Vivek Sarkar

Proceedings of the Euro-Par 2022: Parallel Processing, 2022

ESCHER: expressive scheduling with ephemeral resources.

Romil Bhardwaj, Alexey Tumanov, Stephanie Wang, Richard Liaw, Philipp Moritz, Robert Nishihara, Ion Stoica

Proceedings of the 13th Symposium on Cloud Computing, SoCC 2022, 2022

2021

CompOFA - Compound Once-For-All Networks for Faster Multi-Platform Deployment.

Manas Sahni, Shreya Varshini, Alind Khare, Alexey Tumanov

Proceedings of the 9th International Conference on Learning Representations, 2021

RubberBand: cloud-based hyperparameter tuning.

Ujval Misra, Richard Liaw, Lisa Dunlap, Romil Bhardwaj, Kirthevasan Kandasamy, Joseph E. Gonzalez, Ion Stoica, Alexey Tumanov

Proceedings of the EuroSys '21: Sixteenth European Conference on Computer Systems, 2021

2020

Cloudburst: Stateful Functions-as-a-Service.

Vikram Sreekanti, Chenggang Wu, Xiayue Charles Lin, Johann Schleier-Smith, Joseph Gonzalez, Joseph M. Hellerstein, Alexey Tumanov

Proc. VLDB Endow., 2020

Cloudburst: Stateful Functions-as-a-Service.

Vikram Sreekanti, Chenggang Wu, Xiayue Charles Lin, Johann Schleier-Smith, Jose M. Faleiro, Joseph E. Gonzalez, Joseph M. Hellerstein, Alexey Tumanov

CoRR, 2020

HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units.

Shenda Hong, Yanbo Xu, Alind Khare, Satria Priambada, Kevin O. Maher, Alaa Aljiffry, Jimeng Sun, Alexey Tumanov

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

InferLine: latency-aware provisioning and scaling for prediction serving pipelines.

Daniel Crankshaw, Gur-Eyal Sela, Xiangxi Mo, Corey Zumar, Ion Stoica, Joseph Gonzalez, Alexey Tumanov

Proceedings of the SoCC '20: ACM Symposium on Cloud Computing, 2020

2019

The OoO VLIW JIT Compiler for GPU Inference.

Paras Jain, Xiangxi Mo, Ajay Jain, Alexey Tumanov, Joseph E. Gonzalez, Ion Stoica

CoRR, 2019

Dynamic Space-Time Scheduling for GPU Inference.

Paras Jain, Xiangxi Mo, Ajay Jain, Harikaran Subbaraj, Rehan Sohail Durrani, Alexey Tumanov, Joseph Gonzalez, Ion Stoica

CoRR, 2019

Lineage stash: fault tolerance off the critical path.

Stephanie Wang, John Liagouris, Robert Nishihara, Philipp Moritz, Ujval Misra, Alexey Tumanov, Ion Stoica

Proceedings of the 27th ACM Symposium on Operating Systems Principles, 2019

HyperSched: Dynamic Resource Reallocation for Model Development on a Deadline.

Richard Liaw, Romil Bhardwaj, Lisa Dunlap, Yitian Zou, Joseph E. Gonzalez, Ion Stoica, Alexey Tumanov

Proceedings of the ACM Symposium on Cloud Computing, SoCC 2019, 2019

Cirrus: a Serverless Framework for End-to-end ML Workflows.

João Carreira, Pedro Fonseca, Alexey Tumanov, Andrew Zhang, Randy H. Katz

Proceedings of the ACM Symposium on Cloud Computing, SoCC 2019, 2019

Serverless Computing: One Step Forward, Two Steps Back.

Joseph M. Hellerstein, Jose M. Faleiro, Joseph Gonzalez, Johann Schleier-Smith, Vikram Sreekanti, Alexey Tumanov, Chenggang Wu

Proceedings of the 9th Biennial Conference on Innovative Data Systems Research, 2019

2018

InferLine: ML Inference Pipeline Composition Framework.

Daniel Crankshaw, Gur-Eyal Sela, Corey Zumar, Xiangxi Mo, Joseph E. Gonzalez, Ion Stoica, Alexey Tumanov

CoRR, 2018

Tributary: spot-dancing for elastic services with latency SLOs.

Aaron Harlap, Andrew Chung, Alexey Tumanov, Gregory R. Ganger, Phillip B. Gibbons

Proceedings of the 2018 USENIX Annual Technical Conference, 2018

IDK Cascades: Fast Deep Learning by Learning not to Overthink.

Xin Wang, Yujia Luo, Daniel Crankshaw, Alexey Tumanov, Fisher Yu, Joseph E. Gonzalez

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Ray: A Distributed Framework for Emerging AI Applications.

Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, Melih Elibol, Zongheng Yang, William Paul, Michael I. Jordan, Ion Stoica

Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation, 2018

3Sigma: distribution-based cluster scheduling for runtime uncertainty.

Jun Woo Park, Alexey Tumanov, Angela H. Jiang, Michael A. Kozuch, Gregory R. Ganger

Proceedings of the Thirteenth EuroSys Conference, 2018

2017

Ray: A Distributed Framework for Emerging AI Applications.

Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Eric Liang, William Paul, Michael I. Jordan, Ion Stoica

CoRR, 2017

IDK Cascades: Fast Deep Learning by Learning not to Overthink.

Xin Wang, Yujia Luo, Daniel Crankshaw, Alexey Tumanov, Joseph E. Gonzalez

CoRR, 2017

Real-Time Machine Learning: The Missing Pieces.

Robert Nishihara, Philipp Moritz, Stephanie Wang, Alexey Tumanov, William Paul, Johann Schleier-Smith, Richard Liaw, Mehrdad Niknami, Michael I. Jordan, Ion Stoica

Proceedings of the 16th Workshop on Hot Topics in Operating Systems, 2017

Proteus: agile ML elasticity through tiered reliability in dynamic resource markets.

Aaron Harlap, Alexey Tumanov, Andrew Chung, Gregory R. Ganger, Phillip B. Gibbons

Proceedings of the Twelfth European Conference on Computer Systems, 2017

2016

Scheduling with Space-Time Soft Constraints In Heterogeneous Cloud Datacenters.

Alexey Tumanov

PhD thesis, 2016

Morpheus: Towards Automated SLOs for Enterprise Clusters.

Sangeetha Abdu Jyothi, Carlo Curino, Ishai Menache, Shravan Matthur Narayanamurthy, Alexey Tumanov, Jonathan Yaniv, Ruslan Mavlyutov, Iñigo Goiri, Subru Krishnan, Janardhan Kulkarni, Sriram Rao

Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, 2016

TetriSched: global rescheduling with adaptive plan-ahead in dynamic heterogeneous clusters.

Alexey Tumanov, Timothy Zhu, Jun Woo Park, Michael A. Kozuch, Mor Harchol-Balter, Gregory R. Ganger

Proceedings of the Eleventh European Conference on Computer Systems, 2016

2014

Agility and Performance in Elastic Distributed Storage.

Lianghong Xu, James Cipar, Elie Krevat, Alexey Tumanov, Nitin Gupta, Michael A. Kozuch, Gregory R. Ganger

ACM Trans. Storage, 2014

SpringFS: bridging agility and performance in elastic distributed storage.

Lianghong Xu, James Cipar, Elie Krevat, Alexey Tumanov, Nitin Gupta, Michael A. Kozuch, Gregory R. Ganger

Proceedings of the 12th USENIX conference on File and Storage Technologies, 2014

PriorityMeister: Tail Latency QoS for Shared Networked Storage.

Timothy Zhu, Alexey Tumanov, Michael A. Kozuch, Mor Harchol-Balter, Gregory R. Ganger

Proceedings of the ACM Symposium on Cloud Computing, 2014

Exploiting iterative-ness for parallel ML computations.

Henggang Cui, Alexey Tumanov, Jinliang Wei, Lianghong Xu, Wei Dai, Jesse Haber-Kucharsky, Qirong Ho, Gregory R. Ganger, Phillip B. Gibbons, Garth A. Gibson, Eric P. Xing

Proceedings of the ACM Symposium on Cloud Computing, 2014

2012

alsched: algebraic scheduling of mixed workloads in heterogeneous clouds.

Alexey Tumanov, James Cipar, Gregory R. Ganger, Michael A. Kozuch

Proceedings of the ACM Symposium on Cloud Computing, SOCC '12, 2012

Heterogeneity and dynamicity of clouds at scale: Google trace analysis.

Charles Reiss, Alexey Tumanov, Gregory R. Ganger, Randy H. Katz, Michael A. Kozuch

Proceedings of the ACM Symposium on Cloud Computing, SOCC '12, 2012

2011

Kaleidoscope: cloud micro-elasticity via VM state coloring.

Roy Bryant, Alexey Tumanov, Olga Irzak, Adin Scannell, Kaustubh R. Joshi, Matti A. Hiltunen, H. Andrés Lagar-Cavilla, Eyal de Lara

Proceedings of the European Conference on Computer Systems, 2011

2007

Variability-Aware Latency Amelioration in Distributed Environments.

Alexey Tumanov, Robert S. Allison, Wolfgang Stürzlinger

Proceedings of the IEEE Virtual Reality Conference, 2007