--- title: "Amar Phanishayee — author profile and papers in our library" description: "Amar Phanishayee: 5 papers on Elamur Research (2018–2020), h-index 27 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/amar-phanishayee/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/amar-phanishayee/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Amar Phanishayee # Amar Phanishayee Amar Phanishayee has 5 papers in our library, in 2018–2020, most of them on [GPU & parallel computing](https://md.research.elamur.ai/free/en/topics/gpu-parallel-computing/), [Bias, fairness & safety](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/), [Efficient models & compression](https://md.research.elamur.ai/free/en/topics/efficient-models/); often with [Deepak Narayanan](https://md.research.elamur.ai/free/en/authors/deepak-narayanan/), [Matei Zaharia](https://md.research.elamur.ai/free/en/authors/matei-zaharia/), [Shivaram Venkataraman](https://md.research.elamur.ai/free/en/authors/shivaram-venkataraman/). 5 papers in our library · 2018–2020 [OpenAlex](https://openalex.org/A5011102459) · [ORCID](https://orcid.org/0009-0001-2777-1118) · [**h-index 27**](https://openalex.org/authors/A5011102459) · [91 works](https://openalex.org/works?filter=author.id:A5011102459) · [4,811 citations](https://openalex.org/works?filter=author.id:A5011102459&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 5 works in our library](https://md.research.elamur.ai/free/en/authors/amar-phanishayee/#papers) · [🔗 Cited 465 times in our library](https://md.research.elamur.ai/free/en/authors/amar-phanishayee/cited-by/) · [1 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #3370](https://md.research.elamur.ai/free/en/paper-stars/page/34/#star-3370) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Amar+Phanishayee) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Amar+Phanishayee) · [dblp](https://dblp.org/search?q=Amar+Phanishayee) · [ACL Anthology](https://aclanthology.org/search/?q=Amar+Phanishayee) ## Affiliations [🏢**Microsoft** Redmond · United States 2013–2024 · 33 papers](https://md.research.elamur.ai/free/en/companies/microsoft/) · [🎓**Carnegie Mellon University** Pittsburgh · United States 2007–2018 · 16 papers](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/) · [🏢**Microsoft Research** Cambridge · United Kingdom 2020–2024 · 3 papers](https://md.research.elamur.ai/free/en/companies/microsoft-research-united-kingdom/) · [🏢**Meta** Menlo Park · United States 2025 · 2 papers](https://md.research.elamur.ai/free/en/companies/meta/) · [🎓**Cornell University** Ithaca · United States 2006–2007 · 2 papers](https://md.research.elamur.ai/free/en/universities/cornell-university/) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed. ## Fields, topics and concepts Of its works in our library, with the number of works on each. 📚 Shelves 6 The field of science this work belongs to (OpenAlex fields).[Computer Science · 5 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Hardware and Architecture · 5 works](https://md.research.elamur.ai/free/en/shelves/computer-science/hardware-and-architecture/) · [Computer Networks and Communications · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-networks-and-communications/) · [Information Systems · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · [Artificial Intelligence · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Computer Vision and Pattern Recognition · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) 🧭 Topics 3 Research subjects and languages the work is about.[GPU & parallel computing · 2 works](https://md.research.elamur.ai/free/en/topics/gpu-parallel-computing/) · [Efficient models & compression · 1 work](https://md.research.elamur.ai/free/en/topics/efficient-models/) · [Bias, fairness & safety · 1 work](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/) ## Works by year ### 2020s [**2020** 2 papers](https://md.research.elamur.ai/free/en/authors/amar-phanishayee/year/2020/) ### 2010s [**2019** 2 papers](https://md.research.elamur.ai/free/en/authors/amar-phanishayee/year/2019/) · [**2018** 1 paper](https://md.research.elamur.ai/free/en/authors/amar-phanishayee/year/2018/) ## Papers The most cited first (OpenAlex and our library). 1. **[Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads](https://md.research.elamur.ai/free/en/papers/jeon-2019-analysis-large-scale-multi-tenant/)** *(2019 · 179 citations)* The paper examines how deep learning training behaves on a shared, production GPU cluster at Microsoft. Such clusters differ from classic big-data clusters in two ways: a GPU cannot easily be split among users, and multi-GPU jobs must have all their GPUs allocated at once (gang scheduling). The authors describe… 2. **[PipeDream: Fast and Efficient Pipeline Parallel DNN Training](https://md.research.elamur.ai/free/en/papers/harlap-2018-pipedream-fast-efficient-pipeline-parallel/)** *(2018 · 162 citations)* Data-parallel DNN training gives every GPU a full model copy and synchronizes weights after each step. As models grow and GPUs get faster, network traffic increasingly dominates runtime. For VGG16, it can take up to 85% of training time. Classic model parallelism avoids exchanging all weights but leaves most workers… 3. **[Memory-Efficient Pipeline-Parallel DNN Training](https://md.research.elamur.ai/free/en/papers/narayanan-2020-memory-efficient-pipeline-parallel-dnn/)** *(2020 · 153 citations)* Very large language models often cannot fit their weights and activations on one accelerator, so training must be spread across devices. 4. **[Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads](https://md.research.elamur.ai/free/en/papers/narayanan-2020-heterogeneity-aware-cluster-scheduling-policies/)** *(2020 · 141 citations)* Deep learning training clusters now mix accelerators of different generations, and models benefit unequally from newer hardware. 5. **[Themis: Fair and Efficient GPU Cluster Scheduling](https://md.research.elamur.ai/free/en/papers/mahajan-2019-themis-fair-efficient-gpu-cluster/)** *(2019 · 87 citations)* Organizations increasingly pool GPUs into shared clusters for ML training, but fair-sharing schedulers built for big-data analytics, such as DRF, handle these workloads poorly. ## All works 6 works of theirs in our library, the most cited first; 5 with a page here, the others link their original until we summarise them. 1. [**Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads arXiv:1901.05758**Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee, et al.*2019 · 179 citations*](https://md.research.elamur.ai/free/en/papers/jeon-2019-analysis-large-scale-multi-tenant/) 2. [**PipeDream: Fast and Efficient Pipeline Parallel DNN Training arXiv:1806.03377**Aaron Harlap, Deepak Narayanan, Amar Phanishayee, et al.*2018 · 162 citations*](https://md.research.elamur.ai/free/en/papers/harlap-2018-pipedream-fast-efficient-pipeline-parallel/) 3. **[Memory-Efficient Pipeline-Parallel DNN Training](https://md.research.elamur.ai/free/en/papers/narayanan-2020-memory-efficient-pipeline-parallel-dnn/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Deepak Narayanan, Amar Phanishayee, Kaiyu Shi, et al.*2020 · 153 citations* 4. [**Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads arXiv:2008.09213**Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, et al.*2020 · 141 citations*](https://md.research.elamur.ai/free/en/papers/narayanan-2020-heterogeneity-aware-cluster-scheduling-policies/) 5. [**Themis: Fair and Efficient GPU Cluster Scheduling arXiv:1907.01484**Kshiteej Mahajan, Arjun Balasubramanian, Arjun Singhvi, et al.*2019 · 87 citations*](https://md.research.elamur.ai/free/en/papers/mahajan-2019-themis-fair-efficient-gpu-cluster/) 6. **[Blink: Fast and Generic Collectives for Distributed ML ↗](https://arxiv.org/abs/1910.04940) arXiv:1910.04940 · no summary yet**Guanhua Wang et al.*2019 · 58 citations* ## Co-authors here 9 co-authors with a page, the most papers together first. - [Deepak Narayanan](https://md.research.elamur.ai/free/en/authors/deepak-narayanan/) ×3 - [Matei Zaharia](https://md.research.elamur.ai/free/en/authors/matei-zaharia/) ×2 - [Shivaram Venkataraman](https://md.research.elamur.ai/free/en/authors/shivaram-venkataraman/) ×2 - [Aditya Akella](https://md.research.elamur.ai/free/en/authors/aditya-akella/) - [Fan Yang](https://md.research.elamur.ai/free/en/authors/fan-yang/) - [Keshav Santhanam](https://md.research.elamur.ai/free/en/authors/keshav-santhanam/) - [Vivek Seshadri](https://md.research.elamur.ai/free/en/authors/vivek-seshadri/) - [Wencong Xiao](https://md.research.elamur.ai/free/en/authors/wencong-xiao/) - [Xie Chen](https://md.research.elamur.ai/free/en/authors/xie-chen/) ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/): [ICML 2020](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/2020/) ## Cited by Papers of our library cite their works 465 times (of the 149,439 papers whose bibliographies we read); [28 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/amar-phanishayee/cited-by/). Updated 2 November 2026