Amar Phanishayee

According to our database, Amar Phanishayee authored at least 57 papers between 2006 and 2024.

Collaborative distances:

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

2024

Data-driven Forecasting of Deep Learning Performance on GPUs.

Seonho Lee, Amar Phanishayee, Divya Mahajan

CoRR, 2024

Workload-Aware Hardware Accelerator Mining for Distributed Deep Learning Training.

Muhammad Adnan, Amar Phanishayee, Janardhan Kulkarni, Prashant J. Nair, Divya Mahajan

CoRR, 2024

Integrated Hardware Architecture and Device Placement Search.

Irene Wang, Jakub Tarnawski, Amar Phanishayee, Divya Mahajan

Proceedings of the Forty-first International Conference on Machine Learning, 2024

DéjàVu: KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving.

Foteini Strati, Sara McAllister, Amar Phanishayee, Jakub Tarnawski, Ana Klimovic

Proceedings of the Forty-first International Conference on Machine Learning, 2024

MGit: A Model Versioning and Management System.

Wei Hao, Daniel Mendoza, Rafael Mendes, Deepak Narayanan, Amar Phanishayee, Asaf Cidon, Junfeng Yang

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Blox: A Modular Toolkit for Deep Learning Schedulers.

Saurabh Agarwal, Amar Phanishayee, Shivaram Venkataraman

Proceedings of the Nineteenth European Conference on Computer Systems, 2024

2023

Packrat: Automatic Reconfiguration for Latency Minimization in CPU-based DNN Serving.

Ankit Bhardwaj, Amar Phanishayee, Deepak Narayanan, Mihail Tarta, Ryan Stutsman

CoRR, 2023

MGit: A Model Versioning and Management System.

Wei Hao, Daniel Mendoza, Rafael da Silva, Deepak Narayanan, Amar Phanishayee

CoRR, 2023

2022

Harmony: Overcoming the hurdles of GPU memory capacity to train massive DNN models on commodity servers.

Youjie Li, Amar Phanishayee, Derek Murray, Jakub Tarnawski, Nam Sung Kim

Proc. VLDB Endow., 2022

A Study on the Intersection of GPU Utilization and CNN Inference.

Jack Kosaian, Amar Phanishayee

CoRR, 2022

Looking Beyond GPUs for DNN Scheduling on Multi-Tenant Clusters.

Jayashree Mohan, Amar Phanishayee, Janardhan Kulkarni, Vijay Chidambaram

Proceedings of the 16th USENIX Symposium on Operating Systems Design and Implementation, 2022

2021

Analyzing and Mitigating Data Stalls in DNN Training.

Jayashree Mohan, Amar Phanishayee, Ashish Raniwala, Vijay Chidambaram

Proc. VLDB Endow., 2021

Synergy: Resource Sensitive DNN Scheduling in Multi-Tenant Clusters.

Jayashree Mohan, Amar Phanishayee, Janardhan Kulkarni, Vijay Chidambaram

CoRR, 2021

Efficient Large-Scale Language Model Training on GPU Clusters.

Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Catanzaro, Amar Phanishayee, Matei Zaharia

CoRR, 2021

Efficient large-scale language model training on GPU clusters using megatron-LM.

Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Catanzaro, Amar Phanishayee, Matei Zaharia

Proceedings of the International Conference for High Performance Computing, 2021

Piper: Multidimensional Planner for DNN Parallelization.

Jakub Tarnawski, Deepak Narayanan, Amar Phanishayee

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Memory-Efficient Pipeline-Parallel DNN Training.

Deepak Narayanan, Amar Phanishayee, Kaiyu Shi, Xie Chen, Matei Zaharia

Proceedings of the 38th International Conference on Machine Learning, 2021

Boosting the Throughput and Accelerator Utilization of Specialized CNN Inference Beyond Increasing Batch Size.

Jack Kosaian, Amar Phanishayee, Matthai Philipose, Debadeepta Dey, Rashmi Vinayak

Proceedings of the 38th International Conference on Machine Learning, 2021

Doing more with less: training large DNN models on commodity servers for the masses.

Youjie Li, Amar Phanishayee, Derek Murray, Nam Sung Kim

Proceedings of the HotOS '21: Workshop on Hot Topics in Operating Systems, 2021

CheckFreq: Frequent, Fine-Grained DNN Checkpointing.

Jayashree Mohan, Amar Phanishayee, Vijay Chidambaram

Proceedings of the 19th USENIX Conference on File and Storage Technologies, 2021

2020

Daydream: Accurately Estimating the Efficacy of Optimizations for DNN Training.

Hongyu Zhu, Amar Phanishayee, Gennady Pekhimenko

Proceedings of the 2020 USENIX Annual Technical Conference, 2020

Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads.

Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, Amar Phanishayee, Matei Zaharia

Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation, 2020

Themis: Fair and Efficient GPU Cluster Scheduling.

Kshiteej Mahajan, Arjun Balasubramanian, Arjun Singhvi, Shivaram Venkataraman, Aditya Akella, Amar Phanishayee, Shuchi Chawla

Proceedings of the 17th USENIX Symposium on Networked Systems Design and Implementation, 2020

Efficient Algorithms for Device Placement of DNN Graph Operators.

Jakub Tarnawski, Amar Phanishayee, Nikhil R. Devanur, Divya Mahajan, Fanny Nina Paravecino

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Blink: Fast and Generic Collectives for Distributed ML.

Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Jorgen Thelin, Nikhil R. Devanur, Ion Stoica

Proceedings of the Third Conference on Machine Learning and Systems, 2020

The Non-IID Data Quagmire of Decentralized Machine Learning.

Kevin Hsieh, Amar Phanishayee, Onur Mutlu, Phillip B. Gibbons

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Themis: Fair and Efficient GPU Cluster Scheduling for Machine Learning Workloads.

Kshiteej Mahajan, Arjun Singhvi, Arjun Balasubramanian, Varun Batra, Surya Teja Chavali, Shivaram Venkataraman, Aditya Akella, Amar Phanishayee, Shuchi Chawla

CoRR, 2019

Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads.

Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee, Junjie Qian, Wencong Xiao, Fan Yang

Proceedings of the 2019 USENIX Annual Technical Conference, 2019

PipeDream: generalized pipeline parallelism for DNN training.

Deepak Narayanan, Aaron Harlap, Amar Phanishayee, Vivek Seshadri, Nikhil R. Devanur, Gregory R. Ganger, Phillip B. Gibbons, Matei Zaharia

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

The Case for Unifying Data Loading in Machine Learning Clusters.

Aarati Kakaraparthy, Abhay Venkatesh, Amar Phanishayee, Shivaram Venkataraman

Proceedings of the 11th USENIX Workshop on Hot Topics in Cloud Computing, 2019

2018

Compositional programming and testing of dynamic distributed systems.

Ankush Desai, Amar Phanishayee, Shaz Qadeer, Sanjit A. Seshia

Proc. ACM Program. Lang., 2018

PipeDream: Fast and Efficient Pipeline Parallel DNN Training.

Aaron Harlap, Deepak Narayanan, Amar Phanishayee, Vivek Seshadri, Nikhil R. Devanur, Gregory R. Ganger, Phillip B. Gibbons

CoRR, 2018

TBD: Benchmarking and Analyzing Deep Neural Network Training.

Hongyu Zhu, Mohamed Akrout, Bojian Zheng, Andrew Pelegris, Amar Phanishayee, Bianca Schroeder, Gennady Pekhimenko

CoRR, 2018

Parameter Hub: High Performance Parameter Servers for Efficient Distributed Deep Neural Network Training.

Liang Luo, Jacob Nelson, Luis Ceze, Amar Phanishayee, Arvind Krishnamurthy

CoRR, 2018

Gist: Efficient Data Encoding for Deep Neural Network Training.

Animesh Jain, Amar Phanishayee, Jason Mars, Lingjia Tang, Gennady Pekhimenko

Proceedings of the 45th ACM/IEEE Annual International Symposium on Computer Architecture, 2018

Benchmarking and Analyzing Deep Neural Network Training.

Hongyu Zhu, Mohamed Akrout, Bojian Zheng, Andrew Pelegris, Anand Jayarajan, Amar Phanishayee, Bianca Schroeder, Gennady Pekhimenko

Proceedings of the 2018 IEEE International Symposium on Workload Characterization, 2018

Parameter Hub: a Rack-Scale Parameter Server for Distributed Deep Neural Network Training.

Liang Luo, Jacob Nelson, Luis Ceze, Amar Phanishayee, Arvind Krishnamurthy

Proceedings of the ACM Symposium on Cloud Computing, 2018

2017

RAIL: A Case for Redundant Arrays of Inexpensive Links in Data Center Networks.

Danyang Zhuo, Monia Ghobadi, Ratul Mahajan, Amar Phanishayee, Xuan Kelvin Zou, Hang Guan, Arvind Krishnamurthy, Thomas E. Anderson

Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation, 2017

Atomic In-place Updates for Non-volatile Main Memories with Kamino-Tx.

Amir Saman Memaripour, Anirudh Badam, Amar Phanishayee, Yanqi Zhou, Ramnatthan Alagappan, Karin Strauss, Steven Swanson

Proceedings of the Twelfth European Conference on Computer Systems, 2017

2016

Beam: Ending Monolithic Applications for Connected Devices.

Chenguang Shen, Rayman Preet Singh, Amar Phanishayee, Aman Kansal, Ratul Mahajan

Proceedings of the 2016 USENIX Annual Technical Conference, 2016

ProjecToR: Agile Reconfigurable Data Center Interconnect.

Monia Ghobadi, Ratul Mahajan, Amar Phanishayee, Nikhil R. Devanur, Janardhan Kulkarni, Gireeja Ranade, Pierre-Alexandre Blanche, Houman Rastegarfar, Madeleine Glick, Daniel C. Kilper

Proceedings of the ACM SIGCOMM 2016 Conference, Florianopolis, Brazil, August 22-26, 2016, 2016

Evaluation of elastic modulation gains in microsoft's optical backbone in North America.

Monia Ghobadi, Jamie Gaudette, Ratul Mahajan, Amar Phanishayee, Buddy Klinkers, Daniel C. Kilper

Proceedings of the Optical Fiber Communications Conference and Exhibition, 2016

2015

It's Time to End Monolithic Apps for Connected Devices.

Rayman Preet Singh, Chenguang Shen, Amar Phanishayee, Aman Kansal, Ratul Mahajan

login Usenix Mag., 2015

A Case for Ending Monolithic Apps for Connected Devices.

Rayman Preet Singh, Chenguang Shen, Amar Phanishayee, Aman Kansal, Ratul Mahajan

Proceedings of the 15th Workshop on Hot Topics in Operating Systems, 2015

2014

Bolt: Data Management for Connected Homes.

Trinabh Gupta, Rayman Preet Singh, Amar Phanishayee, Jaeyeon Jung, Ratul Mahajan

Proceedings of the 11th USENIX Symposium on Networked Systems Design and Implementation, 2014

2013

HomeLab: a platform for conducting experiments with connected devices in the home.

Rayman Preet Singh, A. J. Bernheim Brush, Evgeni Filippov, Danny Huang, Ratul Mahajan, Khurshed Mazhar, Amar Phanishayee, Arjmand Samuel

Proceedings of the ACM SIGCOMM 2013 Conference, 2013

Lab of things: a platform for conducting studies with connected devices in multiple homes.

A. J. Bernheim Brush, Evgeni Filippov, Danny Huang, Jaeyeon Jung, Ratul Mahajan, Frank Martinez, Khurshed Mazhar, Amar Phanishayee, Arjmand Samuel, James Scott, Rayman Preet Singh

Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2013

2011

FAWN: a fast array of wimpy nodes.

David G. Andersen, Jason Franklin, Michael Kaminsky, Amar Phanishayee, Lawrence Tan, Vijay Vasudevan

Commun. ACM, 2011

2009

Safe and effective fine-grained TCP retransmissions for datacenter communication.

Vijay Vasudevan, Amar Phanishayee, Hiral Shah, Elie Krevat, David G. Andersen, Gregory R. Ganger, Garth A. Gibson, Brian Mueller

Proceedings of the ACM SIGCOMM 2009 Conference on Applications, 2009

FAWNdamentally Power-efficient Clusters.

Vijay Vasudevan, Jason Franklin, David G. Andersen, Amar Phanishayee, Lawrence Tan, Michael Kaminsky, Iulian Moraru

Proceedings of HotOS'09: 12th Workshop on Hot Topics in Operating Systems, 2009

Scaling all-pairs overlay routing.

David A. Sontag, Yang Zhang, Amar Phanishayee, David G. Andersen, David R. Karger

Proceedings of the 2009 ACM Conference on Emerging Networking Experiments and Technology, 2009

2008

Ditto: a system for opportunistic caching in multi-hop wireless networks.

Fahad R. Dogar, Amar Phanishayee, Himabindu Pucha, Olatunji Ruwase, David G. Andersen

Proceedings of the 14th Annual International Conference on Mobile Computing and Networking, 2008

Measurement and Analysis of TCP Throughput Collapse in Cluster-based Storage Systems.

Amar Phanishayee, Elie Krevat, Vijay Vasudevan, David G. Andersen, Gregory R. Ganger, Garth A. Gibson, Srinivasan Seshan

Proceedings of the 6th USENIX Conference on File and Storage Technologies, 2008

2007

On application-level approaches to avoiding TCP throughput collapse in cluster-based storage systems.

Elie Krevat, Vijay Vasudevan, Amar Phanishayee, David G. Andersen, Gregory R. Ganger, Garth A. Gibson, Srinivasan Seshan

Proceedings of the 2nd International Petascale Data Storage Workshop (PDSW '07), 2007

Ricochet: Lateral Error Correction for Time-Critical Multicast.

Mahesh Balakrishnan, Kenneth P. Birman, Amar Phanishayee, Stefan Pleisch

Proceedings of the 4th Symposium on Networked Systems Design and Implementation (NSDI 2007), 2007

Scalable Multicast Platforms for a New Generation of Robust Distributed Applications.

Ken Birman, Mahesh Balakrishnan, Danny Dolev, Tudor Marian, Krzysztof Ostrowski, Amar Phanishayee

Proceedings of the Second International Conference on COMmunication System softWAre and MiddlewaRE (COMSWARE 2007), 2007

2006

PLATO: Predictive Latency-Aware Total Ordering.

Mahesh Balakrishnan, Ken Birman, Amar Phanishayee

Proceedings of the 25th IEEE Symposium on Reliable Distributed Systems (SRDS 2006), 2006