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.
A lightweight infrastructure for graph analytics
2013391 citationsDonald Nguyen, Keshav Pingali et al.profile →
Countries citing papers authored by Keshav Pingali
Since
Specialization
Citations
This map shows the geographic impact of Keshav Pingali'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 Keshav Pingali with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Keshav Pingali more than expected).
This network shows the impact of papers produced by Keshav Pingali. 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 Keshav Pingali. The network helps show where Keshav Pingali may publish in the future.
Co-authorship network of co-authors of Keshav Pingali
This figure shows the co-authorship network connecting the top 25 collaborators of Keshav Pingali.
A scholar is included among the top collaborators of Keshav Pingali 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 Keshav Pingali. Keshav Pingali is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Chen, Xuhao, Roshan Dathathri, Gurbinder Gill, & Keshav Pingali. (2019). Pangolin: An Efficient and Flexible Graph Mining System on CPU and GPU. arXiv (Cornell University).4 indexed citations
Bilardi, Gianfranco & Keshav Pingali. (2007). GENERALIZED DOMINANCE and CONTROL DEPENDENCE.
6.
Yotov, Kamen, Keshav Pingali, & Paul Stodghill. (2005). Automatic Measurement of Hardware Parameters for Embedded Processors. eCommons (Cornell University).
7.
Pingali, Keshav, Katherine Yelick, & Andrew Grimshaw. (2005). Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming.
8.
Chew, Peter, Gerd Heber, Keshav Pingali, et al.. (2003). Computational Science Simulations based on Web Services.2 indexed citations
9.
Marques, Daniel, et al.. (2003). Collective Operations in an Application-level Fault Tolerant MPI System.14 indexed citations
10.
Ahmed, Nawaaz, Nikolay Mateev, & Keshav Pingali. (2000). Tiling Imperfectly-nested Loop Nests. Conference on High Performance Computing (Supercomputing). 31–31.38 indexed citations
Kotlyar, Vladimir, Keshav Pingali, & Paul Stodghill. (1997). Compiling Parallel Sparse Code for User-Defined Data Structures.. eCommons (Cornell University).12 indexed citations
15.
Schreiber, Rob, Keshav Pingali, & Michael A. Berman. (1997). Proceedings of the sixth ACM SIGPLAN symposium on Principles and practice of parallel programming.6 indexed citations
Pingali, Keshav & Anne Rogers. (1990). Compiling for Locality.. Proceedings of the International Conference on Parallel Processing. 142–146.15 indexed citations
19.
Pingali, Keshav. (1990). Lazy evaluation and the logic variable. eCommons (Cornell University). 171–198.1 indexed citations
20.
Pingali, Keshav & Arvind Arvind. (1983). Efficient Demand-Driven Evaluation (II).. ACM Transactions on Programming Languages and Systems. 7(1). 109–139.1 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.