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ORCIDAmar Phanishayee
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2020 – today
- 2024
[c42]Saurabh Agarwal
, Amar Phanishayee, Shivaram Venkataraman:
Blox: A Modular Toolkit for Deep Learning Schedulers. EuroSys 2024: 1093-1109- [c41]Wei Hao, Daniel Mendoza, Rafael Mendes, Deepak Narayanan, Amar Phanishayee, Asaf Cidon, Junfeng Yang:
MGit: A Model Versioning and Management System. ICML 2024 - [c40]Foteini Strati, Sara McAllister, Amar Phanishayee, Jakub Tarnawski, Ana Klimovic:
DéjàVu: KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving. ICML 2024 - [c39]Irene Wang, Jakub Tarnawski, Amar Phanishayee, Divya Mahajan:
Integrated Hardware Architecture and Device Placement Search. ICML 2024 - [i24]Foteini Strati, Sara McAllister, Amar Phanishayee, Jakub Tarnawski, Ana Klimovic:
DéjàVu: KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving. CoRR abs/2403.01876 (2024) - [i23]Muhammad Adnan, Amar Phanishayee, Janardhan Kulkarni, Prashant J. Nair, Divya Mahajan:
Workload-Aware Hardware Accelerator Mining for Distributed Deep Learning Training. CoRR abs/2404.14632 (2024) - [i22]Irene Wang, Jakub Tarnawski, Amar Phanishayee, Divya Mahajan:
Integrated Hardware Architecture and Device Placement Search. CoRR abs/2407.13143 (2024) - [i21]Seonho Lee, Amar Phanishayee, Divya Mahajan:
Data-driven Forecasting of Deep Learning Performance on GPUs. CoRR abs/2407.13853 (2024) - 2023
- [i20]Wei Hao, Daniel Mendoza, Rafael da Silva, Deepak Narayanan, Amar Phanishayee:
MGit: A Model Versioning and Management System. CoRR abs/2307.07507 (2023) - [i19]Ankit Bhardwaj, Amar Phanishayee, Deepak Narayanan, Mihail Tarta, Ryan Stutsman:
Packrat: Automatic Reconfiguration for Latency Minimization in CPU-based DNN Serving. CoRR abs/2311.18174 (2023) - [i18]Saurabh Agarwal, Amar Phanishayee, Shivaram Venkataraman:
Blox: A Modular Toolkit for Deep Learning Schedulers. CoRR abs/2312.12621 (2023) - 2022
- [j5]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): 2747-2760 (2022) - [c38]Jayashree Mohan, Amar Phanishayee, Janardhan Kulkarni, Vijay Chidambaram:
Looking Beyond GPUs for DNN Scheduling on Multi-Tenant Clusters. OSDI 2022: 579-596 - [e2]Amar Phanishayee, Vyas Sekar:
19th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2022, Renton, WA, USA, April 4-6, 2022. USENIX Association 2022, ISBN 978-1-939133-27-4 [contents] - [i17]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 abs/2202.01306 (2022) - [i16]Jack Kosaian, Amar Phanishayee:
A Study on the Intersection of GPU Utilization and CNN Inference. CoRR abs/2212.07936 (2022) - 2021
- [j4]Jayashree Mohan, Amar Phanishayee, Ashish Raniwala, Vijay Chidambaram:
Analyzing and Mitigating Data Stalls in DNN Training. Proc. VLDB Endow. 14(5): 771-784 (2021) - [c37]Jayashree Mohan, Amar Phanishayee, Vijay Chidambaram:
CheckFreq: Frequent, Fine-Grained DNN Checkpointing. FAST 2021: 203-216 - [c36]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: 119-127 - [c35]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: 5731-5741 - [c34]Deepak Narayanan, Amar Phanishayee, Kaiyu Shi, Xie Chen, Matei Zaharia:
Memory-Efficient Pipeline-Parallel DNN Training. ICML 2021: 7937-7947 - [c33]Jakub Tarnawski, Deepak Narayanan, Amar Phanishayee:
Piper: Multidimensional Planner for DNN Parallelization. NeurIPS 2021: 24829-24840 - [c32]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: 58 - [i15]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 abs/2104.04473 (2021) - [i14]Jayashree Mohan, Amar Phanishayee, Janardhan Kulkarni, Vijay Chidambaram:
Synergy: Resource Sensitive DNN Scheduling in Multi-Tenant Clusters. CoRR abs/2110.06073 (2021) - 2020
- [c31]Kevin Hsieh, Amar Phanishayee, Onur Mutlu, Phillip B. Gibbons:
The Non-IID Data Quagmire of Decentralized Machine Learning. ICML 2020: 4387-4398 - [c30]Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Jorgen Thelin, Nikhil R. Devanur, Ion Stoica:
Blink: Fast and Generic Collectives for Distributed ML. MLSys 2020 - [c29]Jakub Tarnawski, Amar Phanishayee, Nikhil R. Devanur, Divya Mahajan, Fanny Nina Paravecino:
Efficient Algorithms for Device Placement of DNN Graph Operators. NeurIPS 2020 - [c28]Kshiteej Mahajan, Arjun Balasubramanian, Arjun Singhvi, Shivaram Venkataraman, Aditya Akella, Amar Phanishayee, Shuchi Chawla:
Themis: Fair and Efficient GPU Cluster Scheduling. NSDI 2020: 289-304 - [c27]Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, Amar Phanishayee, Matei Zaharia:
Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads. OSDI 2020: 481-498 - [c26]Hongyu Zhu, Amar Phanishayee, Gennady Pekhimenko:
Daydream: Accurately Estimating the Efficacy of Optimizations for DNN Training. USENIX ATC 2020: 337-352 - [e1]Amar Phanishayee, Ryan Stutsman:
12th USENIX Workshop on Hot Topics in Cloud Computing, HotCloud 2020, July 13-14, 2020. USENIX Association 2020 [contents] - [i13]Hongyu Zhu, Amar Phanishayee, Gennady Pekhimenko:
Daydream: Accurately Estimating the Efficacy of Optimizations for DNN Training. CoRR abs/2006.03318 (2020) - [i12]Deepak Narayanan, Amar Phanishayee, Kaiyu Shi, Xie Chen, Matei Zaharia:
Memory-Efficient Pipeline-Parallel DNN Training. CoRR abs/2006.09503 (2020) - [i11]Jakub Tarnawski, Amar Phanishayee, Nikhil R. Devanur, Divya Mahajan, Fanny Nina Paravecino:
Efficient Algorithms for Device Placement of DNN Graph Operators. CoRR abs/2006.16423 (2020) - [i10]Jayashree Mohan, Amar Phanishayee, Ashish Raniwala, Vijay Chidambaram:
Analyzing and Mitigating Data Stalls in DNN Training. CoRR abs/2007.06775 (2020) - [i9]Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, Amar Phanishayee, Matei Zaharia:
Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads. CoRR abs/2008.09213 (2020)
2010 – 2019
- 2019
- [c25]Aarati Kakaraparthy, Abhay Venkatesh, Amar Phanishayee, Shivaram Venkataraman:
The Case for Unifying Data Loading in Machine Learning Clusters. HotCloud 2019 - [c24]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: 1-15 - [c23]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 ATC 2019: 947-960 - [i8]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 abs/1901.05758 (2019) - [i7]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 abs/1907.01484 (2019) - [i6]Kevin Hsieh, Amar Phanishayee, Onur Mutlu, Phillip B. Gibbons:
The Non-IID Data Quagmire of Decentralized Machine Learning. CoRR abs/1910.00189 (2019) - [i5]Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Jorgen Thelin, Nikhil R. Devanur, Ion Stoica:
Blink: Fast and Generic Collectives for Distributed ML. CoRR abs/1910.04940 (2019) - 2018
- [j3]Ankush Desai, Amar Phanishayee, Shaz Qadeer, Sanjit A. Seshia:
Compositional programming and testing of dynamic distributed systems. Proc. ACM Program. Lang. 2(OOPSLA): 159:1-159:30 (2018) - [c22]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: 41-54 - [c21]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: 88-100 - [c20]Animesh Jain, Amar Phanishayee, Jason Mars, Lingjia Tang, Gennady Pekhimenko:
Gist: Efficient Data Encoding for Deep Neural Network Training. ISCA 2018: 776-789 - [i4]Liang Luo, Jacob Nelson, Luis Ceze, Amar Phanishayee, Arvind Krishnamurthy:
Parameter Hub: High Performance Parameter Servers for Efficient Distributed Deep Neural Network Training. CoRR abs/1801.09805 (2018) - [i3]Hongyu Zhu, Mohamed Akrout, Bojian Zheng, Andrew Pelegris, Amar Phanishayee, Bianca Schroeder, Gennady Pekhimenko:
TBD: Benchmarking and Analyzing Deep Neural Network Training. CoRR abs/1803.06905 (2018) - [i2]Liang Luo, Jacob Nelson, Luis Ceze, Amar Phanishayee, Arvind Krishnamurthy:
Parameter Hub: a Rack-Scale Parameter Server for Distributed Deep Neural Network Training. CoRR abs/1805.07891 (2018) - [i1]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 abs/1806.03377 (2018) - 2017
- [c19]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: 499-512 - [c18]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: 561-576 - 2016
- [c17]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: 1-3 - [c16]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: 216-229 - [c15]Chenguang Shen, Rayman Preet Singh, Amar Phanishayee, Aman Kansal, Ratul Mahajan:
Beam: Ending Monolithic Applications for Connected Devices. USENIX ATC 2016: 143-157 - 2015
- [j2]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) - [c14]Rayman Preet Singh, Chenguang Shen, Amar Phanishayee, Aman Kansal, Ratul Mahajan:
A Case for Ending Monolithic Apps for Connected Devices. HotOS 2015 - 2014
- [c13]Trinabh Gupta, Rayman Preet Singh, Amar Phanishayee, Jaeyeon Jung, Ratul Mahajan:
Bolt: Data Management for Connected Homes. NSDI 2014: 243-256 - 2013
- [c12]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: 35-38 - [c11]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: 493-494 - 2011
- [j1]David G. Andersen, Jason Franklin, Michael Kaminsky, Amar Phanishayee, Lawrence Tan, Vijay Vasudevan:
FAWN: a fast array of wimpy nodes. Commun. ACM 54(7): 101-109 (2011)
2000 – 2009
- 2009
- [c10]David A. Sontag, Yang Zhang, Amar Phanishayee, David G. Andersen, David R. Karger:
Scaling all-pairs overlay routing. CoNEXT 2009: 145-156 - [c9]Vijay Vasudevan, Jason Franklin, David G. Andersen, Amar Phanishayee, Lawrence Tan, Michael Kaminsky, Iulian Moraru:
FAWNdamentally Power-efficient Clusters. HotOS 2009 - [c8]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: 303-314 - [c7]David G. Andersen, Jason Franklin, Michael Kaminsky, Amar Phanishayee, Lawrence Tan, Vijay Vasudevan:
FAWN: a fast array of wimpy nodes. SOSP 2009: 1-14 - 2008
- [c6]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: 175-188 - [c5]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: 279-290 - 2007
- [c4]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 - [c3]Mahesh Balakrishnan, Kenneth P. Birman, Amar Phanishayee, Stefan Pleisch:
Ricochet: Lateral Error Correction for Time-Critical Multicast. NSDI 2007 - [c2]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: 1-4 - 2006
- [c1]Mahesh Balakrishnan, Ken Birman, Amar Phanishayee:
PLATO: Predictive Latency-Aware Total Ordering. SRDS 2006: 175-188
Coauthor Index
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last updated on 2024-09-28 01:26 CEST by the dblp team
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