Dipti Shankar

455 total citations
23 papers, 330 citations indexed

About

Dipti Shankar is a scholar working on Computer Networks and Communications, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Dipti Shankar has authored 23 papers receiving a total of 330 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Computer Networks and Communications, 18 papers in Information Systems and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Dipti Shankar's work include Cloud Computing and Resource Management (18 papers), Advanced Data Storage Technologies (18 papers) and Caching and Content Delivery (12 papers). Dipti Shankar is often cited by papers focused on Cloud Computing and Resource Management (18 papers), Advanced Data Storage Technologies (18 papers) and Caching and Content Delivery (12 papers). Dipti Shankar collaborates with scholars based in United States. Dipti Shankar's co-authors include Dhabaleswar K. Panda, Xiaoyi Lu, Nusrat Sharmin Islam, Md. Wasi-ur-Rahman, Haiyang Shi, Tianxi Li, Xiaoyi Lü, Jithin Jose and Hari Subramoni and has published in prestigious journals such as Journal of Parallel and Distributed Computing, The Journal of Supercomputing and The MIT Press eBooks.

In The Last Decade

Dipti Shankar

22 papers receiving 305 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dipti Shankar United States 10 268 236 61 36 34 23 330
Lukas Rupprecht United States 13 329 1.2× 267 1.1× 23 0.4× 26 0.7× 41 1.2× 22 375
Claude Barthels Switzerland 7 210 0.8× 133 0.6× 58 1.0× 45 1.3× 36 1.1× 8 254
Ioannis Koltsidas Switzerland 9 266 1.0× 110 0.5× 80 1.3× 11 0.3× 35 1.0× 17 279
Raghunath Rajachandrasekar United States 12 374 1.4× 227 1.0× 124 2.0× 33 0.9× 16 0.5× 18 397
Alexander Thomson United States 9 679 2.5× 416 1.8× 132 2.2× 15 0.4× 36 1.1× 12 708
Alfredo Giménez United States 9 150 0.6× 82 0.3× 117 1.9× 38 1.1× 18 0.5× 14 231
Min Si United States 10 168 0.6× 77 0.3× 135 2.2× 47 1.3× 62 1.8× 21 264
Ann Gentile United States 10 271 1.0× 150 0.6× 93 1.5× 17 0.5× 44 1.3× 20 295
Johann Schleier-Smith United States 4 244 0.9× 240 1.0× 19 0.3× 26 0.7× 47 1.4× 7 309
Xingda Wei China 7 401 1.5× 242 1.0× 124 2.0× 27 0.8× 32 0.9× 14 416

Countries citing papers authored by Dipti Shankar

Since Specialization
Citations

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

Fields of papers citing papers by Dipti Shankar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dipti Shankar

This figure shows the co-authorship network connecting the top 25 collaborators of Dipti Shankar. A scholar is included among the top collaborators of Dipti Shankar 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 Dipti Shankar. Dipti Shankar 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.
Panda, Dhabaleswar K., Xiaoyi Lü, & Dipti Shankar. (2022). High-Performance Big Data Computing. The MIT Press eBooks. 4 indexed citations
2.
Shi, Haiyang, et al.. (2019). UMR-EC A Unified and Multi-Rail Erasure Coding Library for High-Performance Distributed Storage Systems. 219–230. 1 indexed citations
3.
Shi, Haiyang, Xiaoyi Lu, Dipti Shankar, & Dhabaleswar K. Panda. (2019). UMR-EC. 219–230. 5 indexed citations
4.
Shankar, Dipti, Xiaoyi Lu, & Dhabaleswar K. Panda. (2019). SCOR-KV: SIMD-Aware Client-Centric and Optimistic RDMA-Based Key-Value Store for Emerging CPU Architectures. 257–266. 2 indexed citations
5.
Shankar, Dipti, Xiaoyi Lu, & Dhabaleswar K. Panda. (2019). SimdHT-Bench: Characterizing SIMD-Aware Hash Table Designs on Emerging CPU Architectures. 178–188. 4 indexed citations
6.
Shi, Haiyang, Xiaoyi Lu, Dipti Shankar, & Dhabaleswar K. Panda. (2018). High-Performance Multi-Rail Erasure Coding Library over Modern Data Center Architectures. 530–531. 2 indexed citations
7.
Lu, Xiaoyi, Dipti Shankar, & Dhabaleswar K. Panda. (2017). Scalable and Distributed Key-Value Store-based Data Management Using RDMA-Memcached.. IEEE Data(base) Engineering Bulletin. 40. 50–61. 9 indexed citations
8.
Lu, Xiaoyi, Haiyang Shi, Dipti Shankar, & Dhabaleswar K. Panda. (2017). Performance characterization and acceleration of big data workloads on OpenPOWER system. 213–222. 4 indexed citations
9.
Shankar, Dipti, Xiaoyi Lu, & Dhabaleswar K. Panda. (2017). High-Performance and Resilient Key-Value Store with Online Erasure Coding for Big Data Workloads. 527–537. 14 indexed citations
10.
Islam, Nusrat Sharmin, et al.. (2017). MR-Advisor: A comprehensive tuning, profiling, and prediction tool for MapReduce execution frameworks on HPC clusters. Journal of Parallel and Distributed Computing. 120. 237–250. 17 indexed citations
11.
Lu, Xiaoyi, et al.. (2016). High-performance design of apache spark with RDMA and its benefits on various workloads. 253–262. 36 indexed citations
12.
Shankar, Dipti, Xiaoyi Lu, Md. Wasi-ur-Rahman, Nusrat Sharmin Islam, & Dhabaleswar K. Panda. (2016). Characterizing and benchmarking stand-alone Hadoop MapReduce on modern HPC clusters. The Journal of Supercomputing. 72(12). 4573–4600. 1 indexed citations
13.
Lu, Xiaoyi, et al.. (2016). Impact of HPC Cloud Networking Technologies on Accelerating Hadoop RPC and HBase. 310–317. 6 indexed citations
14.
Shankar, Dipti, Xiaoyi Lu, Nusrat Sharmin Islam, Md. Wasi-ur-Rahman, & Dhabaleswar K. Panda. (2016). High-Performance Hybrid Key-Value Store on Modern Clusters with RDMA Interconnects and SSDs: Non-blocking Extensions, Designs, and Benefits. 393–402. 20 indexed citations
15.
Islam, Nusrat Sharmin, Md. Wasi-ur-Rahman, Xiaoyi Lu, Dipti Shankar, & Dhabaleswar K. Panda. (2015). Performance characterization and acceleration of in-memory file systems for Hadoop and Spark applications on HPC clusters. 243–252. 16 indexed citations
16.
Shankar, Dipti, Xiaoyi Lu, Md. Wasi-ur-Rahman, Nusrat Sharmin Islam, & Dhabaleswar K. Panda. (2015). Benchmarking key-value stores on high-performance storage and interconnects for web-scale workloads. 539–544. 4 indexed citations
17.
Shankar, Dipti, Xiaoyi Lu, Jithin Jose, et al.. (2015). Can RDMA benefit online data processing workloads on memcached and MySQL?. 159–160. 5 indexed citations
18.
Islam, Nusrat Sharmin, Dipti Shankar, Xiaoyi Lu, Md. Wasi-ur-Rahman, & Dhabaleswar K. Panda. (2015). Accelerating I/O Performance of Big Data Analytics on HPC Clusters through RDMA-Based Key-Value Store. 280–289. 23 indexed citations
19.
Islam, Nusrat Sharmin, Xiaoyi Lu, Md. Wasi-ur-Rahman, Dipti Shankar, & Dhabaleswar K. Panda. (2015). Triple-H: A Hybrid Approach to Accelerate HDFS on HPC Clusters with Heterogeneous Storage Architecture. 101–110. 58 indexed citations
20.
Lu, Xiaoyi, et al.. (2014). Accelerating Spark with RDMA for Big Data Processing: Early Experiences. 74 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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