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.
OmpSs: A PROPOSAL FOR PROGRAMMING HETEROGENEOUS MULTI-CORE ARCHITECTURES
2011354 citationsEduard Ayguadé, Rosa M. Badía et al.profile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
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This map shows the geographic impact of Jesús Labarta'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 Jesús Labarta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jesús Labarta more than expected).
This network shows the impact of papers produced by Jesús Labarta. 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 Jesús Labarta. The network helps show where Jesús Labarta may publish in the future.
Co-authorship network of co-authors of Jesús Labarta
This figure shows the co-authorship network connecting the top 25 collaborators of Jesús Labarta.
A scholar is included among the top collaborators of Jesús Labarta 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 Jesús Labarta. Jesús Labarta is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
García-Gasulla, Marta, Guillaume Houzeaux, Roger Ferrer, et al.. (2019). MPI+X: task-based parallelisation and dynamic load balance of finite element assembly. LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas).9 indexed citations
Parés, Ferran, Dario García-Gasulla, Eduard Ayguadé, et al.. (2017). Fluid Communities: A Community Detection Algorithm.. arXiv (Cornell University).3 indexed citations
9.
Parés, Ferran, Dario García-Gasulla, J. L. Moreno, et al.. (2017). Fluid Communities: A Competitive and Highly Scalable Community Detection Algorithm. arXiv (Cornell University).1 indexed citations
Etsion, Yoav, Alex Ramírez, Rosa M. Badía, et al.. (2010). Task superscalar: using processors as functional units. 16–16.6 indexed citations
15.
Casas, Marc, Rosa M. Badía, & Jesús Labarta. (2008). 2008 IEEE International Conference on Cluster Computing. 242–251.1 indexed citations
16.
Corbalán, Julita, et al.. (2004). Implementing Malleability on MPI Jobs. International Conference on Parallel Architectures and Compilation Techniques. 215–224.25 indexed citations
17.
Corbalán, Julita, A. Duran, & Jesús Labarta. (2004). Dynamic load balancing of MPI+OpenMP applications. Proceedings of the International Conference on Parallel Processing. 195–202.21 indexed citations
18.
Nikolopoulos, Dimitrios S., et al.. (2000). Is Data Distribution Necessary in OpenMP. Conference on High Performance Computing (Supercomputing). 47–47.29 indexed citations
19.
Corbalán, Julita, Xavier Martorell, & Jesús Labarta. (2000). Performance-driven processor allocation. Operating Systems Design and Implementation. 5.16 indexed citations
20.
Barrado, Cristina, Jesús Labarta, Eduard Ayguadé, & Mateo Valero. (1995). Automatic generation of loop scheduling for VLIW. International Conference on Parallel Architectures and Compilation Techniques. 306–309.
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.