Jon C. Calhoun

705 total citations
51 papers, 338 citations indexed

About

Jon C. Calhoun is a scholar working on Computer Networks and Communications, Hardware and Architecture and Artificial Intelligence. According to data from OpenAlex, Jon C. Calhoun has authored 51 papers receiving a total of 338 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Computer Networks and Communications, 27 papers in Hardware and Architecture and 15 papers in Artificial Intelligence. Recurrent topics in Jon C. Calhoun's work include Parallel Computing and Optimization Techniques (27 papers), Advanced Data Storage Technologies (24 papers) and Distributed systems and fault tolerance (13 papers). Jon C. Calhoun is often cited by papers focused on Parallel Computing and Optimization Techniques (27 papers), Advanced Data Storage Technologies (24 papers) and Distributed systems and fault tolerance (13 papers). Jon C. Calhoun collaborates with scholars based in United States, China and United Kingdom. Jon C. Calhoun's co-authors include Luke N. Olson, Franck Cappello, Robert Underwood, Marc Snir, William Gropp, Ignacio Laguna, David F. Richards, Martin Schulz, Jiannan Tian and Sheng Di and has published in prestigious journals such as Future Generation Computer Systems, IEEE Transactions on Visualization and Computer Graphics and IEEE Transactions on Parallel and Distributed Systems.

In The Last Decade

Jon C. Calhoun

42 papers receiving 334 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Jon C. Calhoun United States	9	160	124	110	86	75	51	338
Ravi Teja Mullapudi United States	7	114 0.7×	220 1.8×	110 1.0×	54 0.6×	192 2.6×	10	386
Emanuele Del Sozzo Italy	12	104 0.7×	202 1.6×	110 1.0×	80 0.9×	94 1.3×	43	372
Richard Membarth Germany	10	99 0.6×	184 1.5×	32 0.3×	63 0.7×	99 1.3×	33	304
Henry Selvaraj United States	10	120 0.8×	156 1.3×	54 0.5×	92 1.1×	49 0.7×	53	372
Lipeng Wan United States	11	148 0.9×	120 1.0×	107 1.0×	80 0.9×	97 1.3×	36	348
Chun-Xun Lin United States	14	198 1.2×	297 2.4×	82 0.7×	242 2.8×	98 1.3×	25	561
Ching-Hsiang Chu United States	13	208 1.3×	154 1.2×	78 0.7×	89 1.0×	89 1.2×	34	362
E. Arnould United States	6	306 1.9×	261 2.1×	69 0.6×	99 1.2×	68 0.9×	9	454
Satnam Singh United Kingdom	11	155 1.0×	221 1.8×	62 0.6×	46 0.5×	15 0.2×	28	332
Steven Derrien France	11	111 0.7×	152 1.2×	57 0.5×	83 1.0×	66 0.9×	49	340

Countries citing papers authored by Jon C. Calhoun

Since Specialization

Citations

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

Fields of papers citing papers by Jon C. Calhoun

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jon C. Calhoun. 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 Jon C. Calhoun. The network helps show where Jon C. Calhoun may publish in the future.

Co-authorship network of co-authors of Jon C. Calhoun

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

All Works

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20 of 20 papers shown

Taylor, Chris, et al.. (2025). Real-Time Inference for Unmanned Ground Vehicles Using Lossy Compression and Deep Learning. 46(1).

Calhoun, Jon C., et al.. (2024). Towards resilient and energy efficient scalable Krylov solvers. Parallel Computing. 123. 103122–103122. 1 indexed citations

Smith, Melissa C., et al.. (2024). Efficient Vision Transformers for Autonomous Off-Road Perception Systems. Journal of Computer and Communications. 12(9). 188–207. 1 indexed citations

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

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.

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

Biswas, Ayan, et al.. (2023). Accelerated dynamic data reduction using spatial and temporal properties. The International Journal of High Performance Computing Applications. 37(5). 539–559.

Nicolae, Bogdan, et al.. (2023). Modeling Multi-Threaded Aggregated I/O for Asynchronous Checkpointing on HPC Systems. SPIRE - Sciences Po Institutional REpository. 101–105. 1 indexed citations

Underwood, Robert, Jon C. Calhoun, Sheng Di, Amy Apon, & Franck Cappello. (2022). OptZConfig: Efficient Parallel Optimization of Lossy Compression Configuration. IEEE Transactions on Parallel and Distributed Systems. 33(12). 3505–3519. 10 indexed citations

10.

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

11.

Nicolae, Bogdan, et al.. (2022). Analyzing the Energy Consumption of Synchronous and Asynchronous Checkpointing Strategies. 8. 1–9.

12.

Di, Sheng, et al.. (2022). Exploring Light-weight Cryptography for Efficient and Secure Lossy Data Compression. 23–34. 2 indexed citations

13.

Underwood, Robert, et al.. (2021). Table of Contents. iii–iv. 1 indexed citations

14.

Rahman, Mizanur, et al.. (2021). Dynamic error-bounded lossy compression to reduce the bandwidth requirement for real-time vision-based pedestrian safety applications. Journal of Real-Time Image Processing. 19(1). 117–131. 4 indexed citations

15.

Calhoun, Jon C., et al.. (2019). Analyzing the Impact of Lossy Compressor Variability on Checkpointing Scientific Simulations. 1–5. 3 indexed citations

16.

Poulos, Alexandra, et al.. (2019). Exploring Lossy Compression of Gene Expression Matrices. 309. 28–34. 1 indexed citations

17.

Calhoun, Jon C., Franck Cappello, Luke N. Olson, Marc Snir, & William Gropp. (2018). Exploring the feasibility of lossy compression for PDE simulations. The International Journal of High Performance Computing Applications. 33(2). 397–410. 27 indexed citations

18.

Laguna, Ignacio, Martin Schulz, David F. Richards, Jon C. Calhoun, & Luke N. Olson. (2016). IPAS: intelligent protection against silent output corruption in scientific applications. 227–238. 50 indexed citations

19.

Calhoun, Jon C., Luke N. Olson, Marc Snir, & William Gropp. (2015). Towards a more fault resilient multigrid solver. IEEE International Conference on High Performance Computing, Data, and Analytics. 47(4). 1–8. 4 indexed citations

20.

Calhoun, Jon C., Marc Snir, Luke N. Olson, & María Jesús Garzarán. (2015). Understanding the Propagation of Error Due to a Silent Data Corruption in a Sparse Matrix Vector Multiply. 541–542. 6 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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