Tal Ben‐Nun

39 papers receiving 1.2k citations

Tal Ben‐Nun's Hit Papers

Demystifying Parallel and Distributed Deep Learning 2019 · 314 citations
3140+2+4Years since publication100200300

Peers

Tal Ben‐Nun
Comparison fields: 5 of 100
  • Hardware and Architecture 299
  • Computer Vision and Pattern Recognition 358
  • Computer Networks and Communications 355
  • Artificial Intelligence 413
  • Computational Mathematics 7
Replace Guochun Shi with:
Guochun Shi United States
Chengyong Wu China
F.J. Seinstra Netherlands
Naoya Maruyama Japan
Daehyun Kim United States
Guangwen Yang China
V. Krishna Kumar United States
Yibo Lin China
Galen Shipman United States
Timothy G. Mattson United States
Tal Ben‐Nun relative to Guochun Shi United States Guochun Shi's profile →
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00.5×4.2×
Guochun Shi · 1×
Citations per year

Countries citing papers authored by Tal Ben‐Nun

Since Specialization
Citations

This map shows the geographic impact of Tal Ben‐Nun'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 Tal Ben‐Nun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tal Ben‐Nun more than expected).

Fields of papers citing papers by Tal Ben‐Nun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tal Ben‐Nun. 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 Tal Ben‐Nun. The network helps show where Tal Ben‐Nun may publish in the future.

Co-authors

The 25 scholars most cited alongside Tal Ben‐Nun, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Tal Ben‐Nun Line = papers co-authored together Tal Ben‐Nun links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 43 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Demystifying Parallel and Distributed Deep Learning
Hit paper breakdown →
2019314
2 2021127
3 202093
4 201781
5 201064
6 201060
7 201959
8 201057
9 201849
10 201934
11 201633
12 201533
13 201727
14 201721
15 201520
16 201819
17 202118
18 202018
19 202116
20
ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations
202114

About Tal Ben‐Nun

Tal Ben‐Nun is a scholar working on Hardware and Architecture, Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition and Information Systems, having authored 43 papers that have together received 1.3k indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (21 papers), Cloud Computing and Resource Management (8 papers), Graph Theory and Algorithms (8 papers), Distributed and Parallel Computing Systems (8 papers), Advanced Neural Network Applications (6 papers), Computational Physics and Python Applications (5 papers), Enzyme Structure and Function (5 papers) and Advanced Data Storage Technologies (5 papers). The work is most often cited by research in Hardware and Architecture (299 citations), Computer Vision and Pattern Recognition (358 citations), Computer Networks and Communications (355 citations), Artificial Intelligence (413 citations) and Computational Mathematics (7 citations). Tal Ben‐Nun has collaborated with scholars based in Switzerland, Israel and United States. Frequent co-authors include Torsten Hoefler, Uri Raviv, Avi Ginsburg, Amnon Barak, Sreepathi Pai, Pablo Székely, Keshav Pingali, Shigang Li, Nikoli Dryden and Torsten Hoefler. Their work appears in journals such as Journal of Applied Crystallography, Computing in Science & Engineering, Journal of Structural Biology, Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences and ACM SIGPLAN Notices.

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.

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