PLATO: Predictive Latency-Aware Total Ordering. - Mahesh Balakrishnan 001; Ken Birman; Amar Phanishayee (SRDS, 2006)

Scalable Multicast Platforms for a New Generation of Robust Distributed Applications. - Ken Birman; Mahesh Balakrishnan 001; Danny Dolev; Tudor Marian; Krzysztof Ostrowski; Amar Phanishayee (COMSWARE, 2007)

Ricochet: Lateral Error Correction for Time-Critical Multicast. - Mahesh Balakrishnan 001; Kenneth P. Birman; Amar Phanishayee; Stefan Pleisch (NSDI, 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 (PDSW, 2007)

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 (FAST, 2008)

Ditto: a system for opportunistic caching in multi-hop wireless networks. - Fahad R. Dogar; Amar Phanishayee; Himabindu Pucha; Olatunji Ruwase; David G. Andersen (MobiCom, 2008)

Scaling all-pairs overlay routing. - David A. Sontag; Yang Zhang; Amar Phanishayee; David G. Andersen; David R. Karger (CoNEXT, 2009)

FAWNdamentally Power-efficient Clusters. - Vijay Vasudevan; Jason Franklin; David G. Andersen; Amar Phanishayee; Lawrence Tan; Michael Kaminsky; Iulian Moraru (HotOS, 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 (SIGCOMM, 2009)

FAWN: a fast array of wimpy nodes. - David G. Andersen; Jason Franklin; Michael Kaminsky; Amar Phanishayee; Lawrence Tan; Vijay Vasudevan (Commun. ACM, 2011)

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 (UbiComp (Adjunct Publication), 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 (SIGCOMM, 2013)

Bolt: Data Management for Connected Homes. - Trinabh Gupta; Rayman Preet Singh; Amar Phanishayee; Jaeyeon Jung; Ratul Mahajan (NSDI, 2014)

A Case for Ending Monolithic Apps for Connected Devices. - Rayman Preet Singh; Chenguang Shen; Amar Phanishayee; Aman Kansal; Ratul Mahajan (HotOS, 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)

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 (OFC, 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 (SIGCOMM, 2016)

Beam: Ending Monolithic Applications for Connected Devices. - Chenguang Shen; Rayman Preet Singh; Amar Phanishayee; Aman Kansal; Ratul Mahajan (USENIX ATC, 2016)

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 (EuroSys, 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 (NSDI, 2017)

Parameter Hub: a Rack-Scale Parameter Server for Distributed Deep Neural Network Training. - Liang Luo; Jacob Nelson 001; Luis Ceze; Amar Phanishayee; Arvind Krishnamurthy (SoCC, 2018)

Benchmarking and Analyzing Deep Neural Network Training. - Hongyu Zhu 003; Mohamed Akrout; Bojian Zheng; Andrew Pelegris; Anand Jayarajan; Amar Phanishayee; Bianca Schroeder; Gennady Pekhimenko (IISWC, 2018)

Gist: Efficient Data Encoding for Deep Neural Network Training. - Animesh Jain; Amar Phanishayee; Jason Mars; Lingjia Tang; Gennady Pekhimenko (ISCA, 2018)

Parameter Hub: High Performance Parameter Servers for Efficient Distributed Deep Neural Network Training. - Liang Luo; Jacob Nelson 001; Luis Ceze; Amar Phanishayee; Arvind Krishnamurthy (CoRR, 2018)

TBD: Benchmarking and Analyzing Deep Neural Network Training. - Hongyu Zhu 003; Mohamed Akrout; Bojian Zheng; Andrew Pelegris; Amar Phanishayee; Bianca Schroeder; Gennady Pekhimenko (CoRR, 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)

Compositional programming and testing of dynamic distributed systems. - Ankush Desai; Amar Phanishayee; Shaz Qadeer; Sanjit A. Seshia (Proc. ACM Program. Lang., 2018)

The Case for Unifying Data Loading in Machine Learning Clusters. - Aarati Kakaraparthy; Abhay Venkatesh; Amar Phanishayee; Shivaram Venkataraman (HotCloud, 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 (SOSP, 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 024 (USENIX ATC, 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 001 (CoRR, 2019)

The Non-IID Data Quagmire of Decentralized Machine Learning. - Kevin Hsieh; Amar Phanishayee; Onur Mutlu; Phillip B. Gibbons (ICML, 2020)

Blink: Fast and Generic Collectives for Distributed ML. - Guanhua Wang; Shivaram Venkataraman; Amar Phanishayee; Jorgen Thelin; Nikhil R. Devanur; Ion Stoica (MLSys, 2020)

Efficient Algorithms for Device Placement of DNN Graph Operators. - Jakub Tarnawski; Amar Phanishayee; Nikhil R. Devanur; Divya Mahajan 001; Fanny Nina Paravecino (NeurIPS, 2020)

Themis: Fair and Efficient GPU Cluster Scheduling. - Kshiteej Mahajan; Arjun Balasubramanian; Arjun Singhvi; Shivaram Venkataraman; Aditya Akella; Amar Phanishayee; Shuchi Chawla 001 (NSDI, 2020)

Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads. - Deepak Narayanan; Keshav Santhanam; Fiodar Kazhamiaka; Amar Phanishayee; Matei Zaharia (OSDI, 2020)

Daydream: Accurately Estimating the Efficacy of Optimizations for DNN Training. - Hongyu Zhu 003; Amar Phanishayee; Gennady Pekhimenko (USENIX ATC, 2020)

CheckFreq: Frequent, Fine-Grained DNN Checkpointing. - Jayashree Mohan; Amar Phanishayee; Vijay Chidambaram (FAST, 2021)

Doing more with less: training large DNN models on commodity servers for the masses. - Youjie Li; Amar Phanishayee; Derek Murray; Nam Sung Kim (HotOS, 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 (ICML, 2021)

Memory-Efficient Pipeline-Parallel DNN Training. - Deepak Narayanan; Amar Phanishayee; Kaiyu Shi; Xie Chen 001; Matei Zaharia (ICML, 2021)

Piper: Multidimensional Planner for DNN Parallelization. - Jakub Tarnawski; Deepak Narayanan; Amar Phanishayee (NeurIPS, 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 (SC, 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)

Synergy: Resource Sensitive DNN Scheduling in Multi-Tenant Clusters. - Jayashree Mohan; Amar Phanishayee; Janardhan Kulkarni; Vijay Chidambaram (CoRR, 2021)

Analyzing and Mitigating Data Stalls in DNN Training. - Jayashree Mohan; Amar Phanishayee; Ashish Raniwala; Vijay Chidambaram (Proc. VLDB Endow., 2021)

Looking Beyond GPUs for DNN Scheduling on Multi-Tenant Clusters. - Jayashree Mohan; Amar Phanishayee; Janardhan Kulkarni; Vijay Chidambaram (OSDI, 2022)

A Study on the Intersection of GPU Utilization and CNN Inference. - Jack Kosaian; Amar Phanishayee (CoRR, 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)

MGit: A Model Versioning and Management System. - Wei Hao; Daniel Mendoza; Rafael da Silva; Deepak Narayanan; Amar Phanishayee (CoRR, 2023)

Packrat: Automatic Reconfiguration for Latency Minimization in CPU-based DNN Serving. - Ankit Bhardwaj; Amar Phanishayee; Deepak Narayanan; Mihail Tarta; Ryan Stutsman (CoRR, 2023)

Blox: A Modular Toolkit for Deep Learning Schedulers. - Saurabh Agarwal; Amar Phanishayee; Shivaram Venkataraman (EuroSys, 2024)

MGit: A Model Versioning and Management System. - Wei Hao; Daniel Mendoza; Rafael Mendes; Deepak Narayanan; Amar Phanishayee; Asaf Cidon; Junfeng Yang (ICML, 2024)

DéjàVu: KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving. - Foteini Strati; Sara McAllister; Amar Phanishayee; Jakub Tarnawski; Ana Klimovic (ICML, 2024)

Integrated Hardware Architecture and Device Placement Search. - Irene Wang; Jakub Tarnawski; Amar Phanishayee; Divya Mahajan 001 (ICML, 2024)

Workload-Aware Hardware Accelerator Mining for Distributed Deep Learning Training. - Muhammad Adnan; Amar Phanishayee; Janardhan Kulkarni; Prashant J. Nair; Divya Mahajan 001 (CoRR, 2024)

Data-driven Forecasting of Deep Learning Performance on GPUs. - Seonho Lee; Amar Phanishayee; Divya Mahajan 001 (CoRR, 2024)

Forecasting GPU Performance for Deep Learning Training and Inference. - Seonho Lee; Amar Phanishayee; Divya Mahajan 001 (ASPLOS (1), 2025)

Scaling Llama 3 Training with Efficient Parallelism Strategies. - Weiwei Chu; Xinfeng Xie; Jiecao Yu; Jie Wang 022; Amar Phanishayee; Chunqiang Tang; Yuchen Hao; Jianyu Huang; Mustafa Ozdal; Jun Wang; Vedanuj Goswami; Naman Goyal 001; et al. (ISCA, 2025)