Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
PipeDream
2019428 citationsDeepak Narayanan, Amar Phanishayee et al.profile →
FAWN
2009428 citationsDavid G. Andersen, Jason Franklin et al.profile →
Safe and effective fine-grained TCP retransmissions for datacenter communication
2009342 citationsVijay Vasudevan, Amar Phanishayee et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Amar Phanishayee
Since
Specialization
Citations
This map shows the geographic impact of Amar Phanishayee's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Amar Phanishayee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amar Phanishayee more than expected).
Fields of papers citing papers by Amar Phanishayee
This network shows the impact of papers produced by Amar Phanishayee. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Amar Phanishayee. The network helps show where Amar Phanishayee may publish in the future.
Co-authorship network of co-authors of Amar Phanishayee
This figure shows the co-authorship network connecting the top 25 collaborators of Amar Phanishayee.
A scholar is included among the top collaborators of Amar Phanishayee based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Amar Phanishayee. Amar Phanishayee is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Li, Youjie, et al.. (2022). Harmony. Proceedings of the VLDB Endowment. 15(11). 2747–2760.10 indexed citations
3.
Phanishayee, Amar, et al.. (2021). Boosting the Throughput and Accelerator Utilization of Specialized CNN Inference Beyond Increasing Batch Size. International Conference on Machine Learning. 5731–5741.6 indexed citations
4.
Narayanan, Deepak, et al.. (2021). Piper: Multidimensional Planner for DNN Parallelization. Neural Information Processing Systems. 34.9 indexed citations
5.
Phanishayee, Amar, et al.. (2021). CheckFreq: Frequent, Fine-Grained DNN Checkpointing.. File and Storage Technologies. 203–216.17 indexed citations
6.
Zhu, Hongyu, Amar Phanishayee, & Gennady Pekhimenko. (2020). Daydream: Accurately Estimating the Efficacy of Optimizations for {DNN} Training. arXiv (Cornell University). 337–352.7 indexed citations
7.
Narayanan, Deepak, Keshav Santhanam, Fiodar Kazhamiaka, Amar Phanishayee, & Matei Zaharia. (2020). Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads. Operating Systems Design and Implementation. 481–498.25 indexed citations
8.
Narayanan, Deepak, Keshav Santhanam, Fiodar Kazhamiaka, Amar Phanishayee, & Matei Zaharia. (2020). Analysis and Exploitation of Dynamic Pricing in the Public Cloud for ML Training.5 indexed citations
9.
Mahajan, Kshiteej, et al.. (2019). Themis: Fair and Efficient GPU Cluster Scheduling for Machine Learning Workloads. arXiv (Cornell University).3 indexed citations
10.
Phanishayee, Amar, et al.. (2019). The case for unifying data loading in machine learning clusters. IEEE International Conference on Cloud Computing Technology and Science. 12–12.7 indexed citations
11.
Jeon, Myeongjae, Shivaram Venkataraman, Amar Phanishayee, et al.. (2018). Multi-tenant GPU Clusters for Deep Learning Workloads: Analysis and Implications.30 indexed citations
Zhuo, Danyang, Monia Ghobadi, Ratul Mahajan, et al.. (2017). RAIL: A Case for Redundant Arrays of Inexpensive Links in Data Center Networks.. Networked Systems Design and Implementation. 561–576.3 indexed citations
Phanishayee, Amar, Elie Krevat, Vijay Vasudevan, et al.. (2008). Measurement and analysis of TCP throughput collapse in cluster-based storage systems. File and Storage Technologies. 12.203 indexed citations
20.
Balakrishnan, Mahesh, Ken Birman, Amar Phanishayee, & Stefan Pleisch. (2007). Ricochet: lateral error correction for time-critical multicast. Networked Systems Design and Implementation. 6–6.22 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.