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.
PVM-Parallel Virtual Machine: AUsers' Guide and Tutorial for Networked Parallel Computing
1995878 citationsAl Geist, Adam Beguelin et al.Computers in Physicsprofile →
PVM
1994701 citationsAl Geist, Adam Beguelin et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Al Geist'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 Al Geist with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Al Geist more than expected).
This network shows the impact of papers produced by Al Geist. 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 Al Geist. The network helps show where Al Geist may publish in the future.
Co-authorship network of co-authors of Al Geist
This figure shows the co-authorship network connecting the top 25 collaborators of Al Geist.
A scholar is included among the top collaborators of Al Geist 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 Al Geist. Al Geist is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Yoginath, Srikanth B., Nagiza F. Samatova, David W. Bauer, et al.. (2005). RScaLAPACK: High-Performance Parallel Statistical Computing with R and ScaLAPACK.. 61–67.6 indexed citations
Abu-Khzam, Faisal N., Nagiza F. Samatova, George Ostrouchov, Michael A. Langston, & Al Geist. (2002). Distributed Dimension Reduction Algorithms for Widely Dispersed Data.. 167–174.6 indexed citations
Fagg, Graham E., Jack Dongarra, & Al Geist. (1997). PVMPI provides interoperability between MPI implementations. University of North Texas Digital Library (University of North Texas).6 indexed citations
15.
Brown, Jeffrey S., Al Geist, Cherri M. Pancake, & Diane T. Rover. (1997). Software tools for developing parallel applications. Part 2: Interactive control and performance tuning. University of North Texas Digital Library (University of North Texas).2 indexed citations
16.
Beguelin, Adam, Jack Dongarra, Al Geist, & Vaidy Sunderam. (1995). Visualization and debugging in a heterogeneous environment. Research Explorer (The University of Manchester). 132–139.13 indexed citations
17.
Geist, Al, et al.. (1995). PVM-Parallel Virtual Machine: AUsers' Guide and Tutorial for Networked Parallel Computing. Computers in Physics. 9(6). 607–607.878 indexed citations breakdown →
18.
Dongarra, Jack, et al.. (1993). Using PVM 3.0 to Run Grand Challenge Applications on a Heterogeneous Network of Parallel Computers. Research Explorer (The University of Manchester). 873–877.2 indexed citations
19.
Beguelin, Adam, Jack Dongarra, Al Geist, Robert Manchek, & Vaidy Sunderam. (1991). Solving Computational Grand Challenges Using a Network of Heterogeneous Supercomputers. Research Explorer (The University of Manchester). 596–601.14 indexed citations
20.
Beguelin, Adam, Jack Dongarra, Al Geist, Robert Manchek, & Vaidy Sunderam. (1991). A User''s Guide to PVM Parallel Virtual Machine. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information).205 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.