Franck Cappello

12.9k total citations · 1 hit paper
238 papers, 5.5k citations indexed

About

Franck Cappello is a scholar working on Computer Networks and Communications, Hardware and Architecture and Artificial Intelligence. According to data from OpenAlex, Franck Cappello has authored 238 papers receiving a total of 5.5k indexed citations (citations by other indexed papers that have themselves been cited), including 192 papers in Computer Networks and Communications, 127 papers in Hardware and Architecture and 79 papers in Artificial Intelligence. Recurrent topics in Franck Cappello's work include Parallel Computing and Optimization Techniques (126 papers), Advanced Data Storage Technologies (118 papers) and Distributed and Parallel Computing Systems (66 papers). Franck Cappello is often cited by papers focused on Parallel Computing and Optimization Techniques (126 papers), Advanced Data Storage Technologies (118 papers) and Distributed and Parallel Computing Systems (66 papers). Franck Cappello collaborates with scholars based in United States, France and Japan. Franck Cappello's co-authors include Sheng Di, Sheng Di, Zizhong Chen, Dingwen Tao, Gilles Fedak, Xin Liang, Daniel Etiemble, Marc Snir, Vincent Néri and Thomas Hérault and has published in prestigious journals such as Future Generation Computer Systems, IEEE Transactions on Visualization and Computer Graphics and SIAM Journal on Scientific Computing.

In The Last Decade

Franck Cappello

224 papers receiving 5.3k citations

Hit Papers

Fast Error-Bounded Lossy ... 2016 2026 2019 2022 2016 50 100 150 200 250
What are hit papers?

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.

  1. Fast Error-Bounded Lossy HPC Data Compression with SZ
    2016 276 citations Sheng Di, Franck Cappello profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Franck Cappello 4.2k 2.3k 1.3k 1.3k 660 238 5.5k
John Shalf 3.9k 0.9× 3.1k 1.3× 1.1k 0.9× 660 0.5× 419 0.6× 172 6.0k
Torsten Hoefler 3.5k 0.9× 2.7k 1.1× 1.2k 1.0× 1.2k 0.9× 819 1.2× 301 5.8k
Rajeev Thakur 4.1k 1.0× 2.9k 1.2× 794 0.6× 457 0.4× 357 0.5× 167 5.0k
Jeffrey S. Vetter 3.9k 0.9× 3.4k 1.5× 1.2k 0.9× 534 0.4× 378 0.6× 253 5.4k
Henri E. Bal 3.6k 0.9× 2.3k 1.0× 1.0k 0.8× 789 0.6× 358 0.5× 246 4.4k
P. Sadayappan 5.0k 1.2× 5.5k 2.3× 1.2k 1.0× 1.2k 1.0× 667 1.0× 347 7.5k
Karsten Schwan 6.9k 1.7× 3.2k 1.4× 3.5k 2.8× 1.0k 0.8× 472 0.7× 409 8.1k
Robert Ross 4.1k 1.0× 1.9k 0.8× 1.2k 0.9× 594 0.5× 406 0.6× 206 4.9k
Samuel Williams 2.9k 0.7× 3.3k 1.4× 574 0.5× 688 0.5× 618 0.9× 135 5.4k
Katherine Yelick 4.7k 1.1× 5.0k 2.1× 851 0.7× 866 0.7× 469 0.7× 149 6.8k

Countries citing papers authored by Franck Cappello

Since Specialization
Citations

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

Fields of papers citing papers by Franck Cappello

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Franck Cappello. 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 Franck Cappello. The network helps show where Franck Cappello may publish in the future.

Co-authorship network of co-authors of Franck Cappello

This figure shows the co-authorship network connecting the top 25 collaborators of Franck Cappello. A scholar is included among the top collaborators of Franck Cappello 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 Franck Cappello. Franck Cappello is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
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Di, Sheng, Xin Liang, Guanpeng Li, et al.. (2025). Lightweight CNN-Based Artifact Reduction for Scientific Error-bounded Lossy Compression. 314–323.
3.
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Balaprakash, Prasanna, Franck Cappello, Ewa Deelman, et al.. (2025). SWARM: Reimagining scientific workflow management systems in a distributed world. The International Journal of High Performance Computing Applications. 39(5). 692–712. 2 indexed citations
5.
Zhang, Lihong, Sheng Di, Robert Underwood, et al.. (2025). LCP: Enhancing Scientific Data Management with L ossy C ompression for P articles. Proceedings of the ACM on Management of Data. 3(1). 1–27. 1 indexed citations
6.
Di, Sheng, et al.. (2025). Pushing the Limits of GPU Lossy Compression: A Hierarchical Delta Approach. 654–669. 2 indexed citations
7.
Zhai, Yujia, et al.. (2025). TurboFFT: Co-Designed High-Performance and Fault-Tolerant Fast Fourier Transform on GPUs. 70–84. 2 indexed citations
8.
Nicolae, Bogdan, et al.. (2025). DTIO: Data Stack for AI-driven Workflows. SPIRE - Sciences Po Institutional REpository. 1–12.
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Di, Sheng, Xiaodong Yu, Yujia Zhai, et al.. (2024). An Optimized Error-controlled MPI Collective Framework Integrated with Lossy Compression. 752–764. 10 indexed citations
11.
Di, Sheng, Jon C. Calhoun, Kibaek Kim, et al.. (2024). FedSZ: Leveraging Error-Bounded Lossy Compression for Federated Learning Communications. Apollo (University of Cambridge). 1187–1188.
12.
Shah, Milan, Xiaodong Yu, Sheng Di, Michela Becchi, & Franck Cappello. (2024). A Portable, Fast, DCT-based Compressor for AI Accelerators. 109–121. 2 indexed citations
13.
Di, Sheng, Jon C. Calhoun, Kibaek Kim, et al.. (2024). FedSZ: Leveraging Error-Bounded Lossy Compression for Federated Learning Communications. 577–588. 4 indexed citations
14.
Di, Sheng, et al.. (2024). Druto: Upper-Bounding Silent Data Corruption Vulnerability in GPU Applications. 582–594. 3 indexed citations
15.
Yu, Xiaodong, Weijian Zheng, Sheng Di, et al.. (2024). CereSZ: Enabling and Scaling Error-bounded Lossy Compression on Cerebras CS-2. 309–321. 8 indexed citations
16.
Di, Sheng, Kai Zhao, Xin Liang, et al.. (2024). High-performance Effective Scientific Error-bounded Lossy Compression with Auto-tuned Multi-component Interpolation. Proceedings of the ACM on Management of Data. 2(1). 1–27. 14 indexed citations
17.
Underwood, Robert, et al.. (2023). Black-box statistical prediction of lossy compression ratios for scientific data. The International Journal of High Performance Computing Applications. 37(3-4). 412–433. 6 indexed citations
18.
Liang, Xin, Kai Zhao, Sheng Di, et al.. (2022). SZ3: A Modular Framework for Composing Prediction-Based Error-Bounded Lossy Compressors. IEEE Transactions on Big Data. 9(2). 485–498. 86 indexed citations
19.
Zou, Xiangyu, Tao Lü, Wen Xia, et al.. (2020). Performance Optimization for Relative-Error-Bounded Lossy Compression on Scientific Data. IEEE Transactions on Parallel and Distributed Systems. 31(7). 1665–1680. 12 indexed citations
20.
Di, Sheng, et al.. (2017). LOGAIDER: A Tool for Mining Potential Correlations of HPC Log Events. 442–451. 38 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.

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