Amar Phanishayee

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 52

Top Topics

Scheduling Policies

Iterative Deepening

Top Venues

USENIX Annual Technical Conference

Publications

Muhammad Adnan, Amar Phanishayee, Janardhan Kulkarni, Prashant J. Nair, Divya Mahajan
Workload-Aware Hardware Accelerator Mining for Distributed Deep Learning Training. CoRR (2024)
Foteini Strati, Sara McAllister, Amar Phanishayee, Jakub Tarnawski, Ana Klimovic
DéjàVu: KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving. CoRR (2024)
Seonho Lee, Amar Phanishayee, Divya Mahajan
Data-driven Forecasting of Deep Learning Performance on GPUs. CoRR (2024)
Irene Wang, Jakub Tarnawski, Amar Phanishayee, Divya Mahajan
Integrated Hardware Architecture and Device Placement Search. CoRR (2024)
Saurabh Agarwal, Amar Phanishayee, Shivaram Venkataraman
Blox: A Modular Toolkit for Deep Learning Schedulers. EuroSys (2024)
Ankit Bhardwaj, Amar Phanishayee, Deepak Narayanan, Mihail Tarta, Ryan Stutsman
Packrat: Automatic Reconfiguration for Latency Minimization in CPU-based DNN Serving. CoRR (2023)
Wei Hao, Daniel Mendoza, Rafael da Silva, Deepak Narayanan, Amar Phanishayee
MGit: A Model Versioning and Management System. CoRR (2023)
Saurabh Agarwal, Amar Phanishayee, Shivaram Venkataraman
Blox: A Modular Toolkit for Deep Learning Schedulers. CoRR (2023)
Jayashree Mohan, Amar Phanishayee, Janardhan Kulkarni, Vijay Chidambaram
Looking Beyond GPUs for DNN Scheduling on Multi-Tenant Clusters. OSDI (2022)
Youjie Li, Amar Phanishayee, Derek Murray, Jakub Tarnawski, Nam Sung Kim
Harmony: Overcoming the hurdles of GPU memory capacity to train massive DNN models on commodity servers. Proc. VLDB Endow. 15 (11) (2022)
Youjie Li, Amar Phanishayee, Derek Murray, Jakub Tarnawski, Nam Sung Kim
Harmony: Overcoming the hurdles of GPU memory capacity to train massive DNN models on commodity servers. CoRR (2022)
Jack Kosaian, Amar Phanishayee
A Study on the Intersection of GPU Utilization and CNN Inference. CoRR (2022)
Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Catanzaro, Amar Phanishayee, Matei Zaharia
Efficient Large-Scale Language Model Training on GPU Clusters. CoRR (2021)
Deepak Narayanan, Amar Phanishayee, Kaiyu Shi, Xie Chen, Matei Zaharia
Memory-Efficient Pipeline-Parallel DNN Training. ICML (2021)
Youjie Li, Amar Phanishayee, Derek Murray, Nam Sung Kim
Doing more with less: training large DNN models on commodity servers for the masses. HotOS (2021)
Jack Kosaian, Amar Phanishayee, Matthai Philipose, Debadeepta Dey, Rashmi Vinayak
Boosting the Throughput and Accelerator Utilization of Specialized CNN Inference Beyond Increasing Batch Size. ICML (2021)
Jayashree Mohan, Amar Phanishayee, Ashish Raniwala, Vijay Chidambaram
Analyzing and Mitigating Data Stalls in DNN Training. Proc. VLDB Endow. 14 (5) (2021)
Jayashree Mohan, Amar Phanishayee, Janardhan Kulkarni, Vijay Chidambaram
Synergy: Resource Sensitive DNN Scheduling in Multi-Tenant Clusters. CoRR (2021)
Jakub Tarnawski, Deepak Narayanan, Amar Phanishayee
Piper: Multidimensional Planner for DNN Parallelization. NeurIPS (2021)
Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Catanzaro, Amar Phanishayee, Matei Zaharia
Efficient large-scale language model training on GPU clusters using megatron-LM. SC (2021)
Jayashree Mohan, Amar Phanishayee, Vijay Chidambaram
CheckFreq: Frequent, Fine-Grained DNN Checkpointing. FAST (2021)
Kshiteej Mahajan, Arjun Balasubramanian, Arjun Singhvi, Shivaram Venkataraman, Aditya Akella, Amar Phanishayee, Shuchi Chawla
Themis: Fair and Efficient GPU Cluster Scheduling. NSDI (2020)
Kevin Hsieh, Amar Phanishayee, Onur Mutlu, Phillip B. Gibbons
The Non-IID Data Quagmire of Decentralized Machine Learning. ICML (2020)
Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Jorgen Thelin, Nikhil R. Devanur, Ion Stoica
Blink: Fast and Generic Collectives for Distributed ML. MLSys (2020)
Hongyu Zhu, Amar Phanishayee, Gennady Pekhimenko
Daydream: Accurately Estimating the Efficacy of Optimizations for DNN Training. USENIX Annual Technical Conference (2020)
Hongyu Zhu, Amar Phanishayee, Gennady Pekhimenko
Daydream: Accurately Estimating the Efficacy of Optimizations for DNN Training. CoRR (2020)
Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, Amar Phanishayee, Matei Zaharia
Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads. CoRR (2020)
Jakub Tarnawski, Amar Phanishayee, Nikhil R. Devanur, Divya Mahajan, Fanny Nina Paravecino
Efficient Algorithms for Device Placement of DNN Graph Operators. NeurIPS (2020)
Deepak Narayanan, Amar Phanishayee, Kaiyu Shi, Xie Chen, Matei Zaharia
Memory-Efficient Pipeline-Parallel DNN Training. CoRR (2020)
Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, Amar Phanishayee, Matei Zaharia
Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads. OSDI (2020)
Jakub Tarnawski, Amar Phanishayee, Nikhil R. Devanur, Divya Mahajan, Fanny Nina Paravecino
Efficient Algorithms for Device Placement of DNN Graph Operators. CoRR (2020)
Jayashree Mohan, Amar Phanishayee, Ashish Raniwala, Vijay Chidambaram
Analyzing and Mitigating Data Stalls in DNN Training. CoRR (2020)
Aarati Kakaraparthy, Abhay Venkatesh, Amar Phanishayee, Shivaram Venkataraman
The Case for Unifying Data Loading in Machine Learning Clusters. HotCloud (2019)
Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee, Junjie Qian, Wencong Xiao, Fan Yang
Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads. CoRR (2019)
Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee, Junjie Qian, Wencong Xiao, Fan Yang
Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads. USENIX Annual Technical Conference (2019)
Kshiteej Mahajan, Arjun Singhvi, Arjun Balasubramanian, Varun Batra, Surya Teja Chavali, Shivaram Venkataraman, Aditya Akella, Amar Phanishayee, Shuchi Chawla
Themis: Fair and Efficient GPU Cluster Scheduling for Machine Learning Workloads. CoRR (2019)
Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Jorgen Thelin, Nikhil R. Devanur, Ion Stoica
Blink: Fast and Generic Collectives for Distributed ML. CoRR (2019)
Kevin Hsieh, Amar Phanishayee, Onur Mutlu, Phillip B. Gibbons
The Non-IID Data Quagmire of Decentralized Machine Learning. CoRR (2019)
Deepak Narayanan, Aaron Harlap, Amar Phanishayee, Vivek Seshadri, Nikhil R. Devanur, Gregory R. Ganger, Phillip B. Gibbons, Matei Zaharia
PipeDream: generalized pipeline parallelism for DNN training. SOSP (2019)
Liang Luo, Jacob Nelson, Luis Ceze, Amar Phanishayee, Arvind Krishnamurthy
Parameter Hub: High Performance Parameter Servers for Efficient Distributed Deep Neural Network Training. CoRR (2018)
Ankush Desai, Amar Phanishayee, Shaz Qadeer, Sanjit A. Seshia
Compositional programming and testing of dynamic distributed systems. Proc. ACM Program. Lang. 2 (OOPSLA) (2018)
Hongyu Zhu, Mohamed Akrout, Bojian Zheng, Andrew Pelegris, Anand Jayarajan, Amar Phanishayee, Bianca Schroeder, Gennady Pekhimenko
Benchmarking and Analyzing Deep Neural Network Training. IISWC (2018)
Liang Luo, Jacob Nelson, Luis Ceze, Amar Phanishayee, Arvind Krishnamurthy
Parameter Hub: a Rack-Scale Parameter Server for Distributed Deep Neural Network Training. SoCC (2018)
Hongyu Zhu, Mohamed Akrout, Bojian Zheng, Andrew Pelegris, Amar Phanishayee, Bianca Schroeder, Gennady Pekhimenko
TBD: Benchmarking and Analyzing Deep Neural Network Training. CoRR (2018)
Animesh Jain, Amar Phanishayee, Jason Mars, Lingjia Tang, Gennady Pekhimenko
Gist: Efficient Data Encoding for Deep Neural Network Training. ISCA (2018)
Liang Luo, Jacob Nelson, Luis Ceze, Amar Phanishayee, Arvind Krishnamurthy
Parameter Hub: a Rack-Scale Parameter Server for Distributed Deep Neural Network Training. CoRR (2018)
Aaron Harlap, Deepak Narayanan, Amar Phanishayee, Vivek Seshadri, Nikhil R. Devanur, Gregory R. Ganger, Phillip B. Gibbons
PipeDream: Fast and Efficient Pipeline Parallel DNN Training. CoRR (2018)
Amir Saman Memaripour, Anirudh Badam, Amar Phanishayee, Yanqi Zhou, Ramnatthan Alagappan, Karin Strauss, Steven Swanson
Atomic In-place Updates for Non-volatile Main Memories with Kamino-Tx. EuroSys (2017)
Danyang Zhuo, Monia Ghobadi, Ratul Mahajan, Amar Phanishayee, Xuan Kelvin Zou, Hang Guan, Arvind Krishnamurthy, Thomas E. Anderson
RAIL: A Case for Redundant Arrays of Inexpensive Links in Data Center Networks. NSDI (2017)
Monia Ghobadi, Ratul Mahajan, Amar Phanishayee, Nikhil R. Devanur, Janardhan Kulkarni, Gireeja Ranade, Pierre-Alexandre Blanche, Houman Rastegarfar, Madeleine Glick, Daniel C. Kilper
ProjecToR: Agile Reconfigurable Data Center Interconnect. SIGCOMM (2016)
Chenguang Shen, Rayman Preet Singh, Amar Phanishayee, Aman Kansal, Ratul Mahajan
Beam: Ending Monolithic Applications for Connected Devices. USENIX Annual Technical Conference (2016)
Monia Ghobadi, Jamie Gaudette, Ratul Mahajan, Amar Phanishayee, Buddy Klinkers, Daniel C. Kilper
Evaluation of elastic modulation gains in microsoft's optical backbone in North America. OFC (2016)
Rayman Preet Singh, Chenguang Shen, Amar Phanishayee, Aman Kansal, Ratul Mahajan
It's Time to End Monolithic Apps for Connected Devices. login Usenix Mag. 40 (5) (2015)
Rayman Preet Singh, Chenguang Shen, Amar Phanishayee, Aman Kansal, Ratul Mahajan
A Case for Ending Monolithic Apps for Connected Devices. HotOS (2015)
Trinabh Gupta, Rayman Preet Singh, Amar Phanishayee, Jaeyeon Jung, Ratul Mahajan
Bolt: Data Management for Connected Homes. NSDI (2014)
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
Lab of things: a platform for conducting studies with connected devices in multiple homes. UbiComp (Adjunct Publication) (2013)
Rayman Preet Singh, A. J. Bernheim Brush, Evgeni Filippov, Danny Huang, Ratul Mahajan, Khurshed Mazhar, Amar Phanishayee, Arjmand Samuel
HomeLab: a platform for conducting experiments with connected devices in the home. SIGCOMM (2013)
David G. Andersen, Jason Franklin, Michael Kaminsky, Amar Phanishayee, Lawrence Tan, Vijay Vasudevan
FAWN: a fast array of wimpy nodes. Commun. ACM 54 (7) (2011)
Vijay Vasudevan, Jason Franklin, David G. Andersen, Amar Phanishayee, Lawrence Tan, Michael Kaminsky, Iulian Moraru
FAWNdamentally Power-efficient Clusters. HotOS (2009)
David G. Andersen, Jason Franklin, Michael Kaminsky, Amar Phanishayee, Lawrence Tan, Vijay Vasudevan
FAWN: a fast array of wimpy nodes. SOSP (2009)
David A. Sontag, Yang Zhang, Amar Phanishayee, David G. Andersen, David R. Karger
Scaling all-pairs overlay routing. CoNEXT (2009)
Vijay Vasudevan, Amar Phanishayee, Hiral Shah, Elie Krevat, David G. Andersen, Gregory R. Ganger, Garth A. Gibson, Brian Mueller
Safe and effective fine-grained TCP retransmissions for datacenter communication. SIGCOMM (2009)
Fahad R. Dogar, Amar Phanishayee, Himabindu Pucha, Olatunji Ruwase, David G. Andersen
Ditto: a system for opportunistic caching in multi-hop wireless networks. MobiCom (2008)
Amar Phanishayee, Elie Krevat, Vijay Vasudevan, David G. Andersen, Gregory R. Ganger, Garth A. Gibson, Srinivasan Seshan
Measurement and Analysis of TCP Throughput Collapse in Cluster-based Storage Systems. FAST (2008)
Elie Krevat, Vijay Vasudevan, Amar Phanishayee, David G. Andersen, Gregory R. Ganger, Garth A. Gibson, Srinivasan Seshan
On application-level approaches to avoiding TCP throughput collapse in cluster-based storage systems. PDSW (2007)
Ken Birman, Mahesh Balakrishnan, Danny Dolev, Tudor Marian, Krzysztof Ostrowski, Amar Phanishayee
Scalable Multicast Platforms for a New Generation of Robust Distributed Applications. COMSWARE (2007)
Mahesh Balakrishnan, Kenneth P. Birman, Amar Phanishayee, Stefan Pleisch
Ricochet: Lateral Error Correction for Time-Critical Multicast. NSDI (2007)
Mahesh Balakrishnan, Ken Birman, Amar Phanishayee
PLATO: Predictive Latency-Aware Total Ordering. SRDS (2006)