Sergio Barrachina

1.0k total citations
44 papers, 534 citations indexed

About

Sergio Barrachina is a scholar working on Hardware and Architecture, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Sergio Barrachina has authored 44 papers receiving a total of 534 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Hardware and Architecture, 18 papers in Artificial Intelligence and 11 papers in Computer Vision and Pattern Recognition. Recurrent topics in Sergio Barrachina's work include Parallel Computing and Optimization Techniques (17 papers), Advanced Neural Network Applications (10 papers) and Natural Language Processing Techniques (7 papers). Sergio Barrachina is often cited by papers focused on Parallel Computing and Optimization Techniques (17 papers), Advanced Neural Network Applications (10 papers) and Natural Language Processing Techniques (7 papers). Sergio Barrachina collaborates with scholars based in Spain, Germany and United Kingdom. Sergio Barrachina's co-authors include Enrique S. Quintana–Ort́ı, Rafael Mayo, Maribel Castillo, Juan Miguel Vilar, Francisco D. Igual, Enrique Vidal, Francisco Casacuberta, Manuel F. Dolz, Hermann Ney and Jorge Civera and has published in prestigious journals such as IEEE Internet of Things Journal, Computational Linguistics and Journal of Parallel and Distributed Computing.

In The Last Decade

Sergio Barrachina

40 papers receiving 473 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sergio Barrachina Spain 12 256 135 103 94 60 44 534
J. Demmel United States 6 97 0.4× 312 2.3× 55 0.5× 259 2.8× 135 2.3× 16 517
Chun Huang China 10 71 0.3× 129 1.0× 31 0.3× 136 1.4× 26 0.4× 41 339
Jovan Dj. Golić Serbia 12 344 1.3× 97 0.7× 267 2.6× 152 1.6× 93 1.6× 57 577
George M. Slota United States 11 120 0.5× 53 0.4× 221 2.1× 125 1.3× 43 0.7× 27 346
Ramesh Hariharan India 17 447 1.7× 147 1.1× 68 0.7× 172 1.8× 343 5.7× 48 728
Christine Rüb Germany 6 158 0.6× 41 0.3× 33 0.3× 158 1.7× 124 2.1× 12 384
Martin Dietzfelbinger Germany 13 369 1.4× 93 0.7× 87 0.8× 302 3.2× 259 4.3× 56 629
Rezaul Chowdhury United States 14 297 1.2× 526 3.9× 53 0.5× 524 5.6× 162 2.7× 59 817
Tze Meng Low United States 11 145 0.6× 276 2.0× 101 1.0× 186 2.0× 76 1.3× 38 464
Henning Meyerhenke Germany 15 185 0.7× 71 0.5× 160 1.6× 269 2.9× 62 1.0× 54 619

Countries citing papers authored by Sergio Barrachina

Since Specialization
Citations

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

Fields of papers citing papers by Sergio Barrachina

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sergio Barrachina

This figure shows the co-authorship network connecting the top 25 collaborators of Sergio Barrachina. A scholar is included among the top collaborators of Sergio Barrachina 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 Sergio Barrachina. Sergio Barrachina 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.
Barrachina, Sergio, et al.. (2025). Deep learning inference optimisation for IoT: Conv2D-ReLU-BN layer fusion and quantisation. The Journal of Supercomputing. 81(4).
2.
Barrachina, Sergio, et al.. (2024). Optimizing Convolutions for Deep Learning Inference on ARM Cortex-M Processors. IEEE Internet of Things Journal. 11(15). 26203–26219. 3 indexed citations
3.
Barrachina, Sergio, et al.. (2023). Using Blosc2 NDim As A Fast Explorer Of The Milky Way (Or Any Other NDim Dataset). Proceedings of the Python in Science Conferences. 1–7. 1 indexed citations
4.
Dolz, Manuel F., et al.. (2023). Performance–energy trade-offs of deep learning convolution algorithms on ARM processors. The Journal of Supercomputing. 79(9). 9819–9836. 3 indexed citations
5.
Castelló, Adrián, et al.. (2022). High performance and energy efficient inference for deep learning on multicore ARM processors using general optimization techniques and BLIS. Journal of Systems Architecture. 125. 102459–102459. 6 indexed citations
6.
Barrachina, Sergio, et al.. (2022). Efficient and portable GEMM-based convolution operators for deep neural network training on multicore processors. Journal of Parallel and Distributed Computing. 167. 240–254. 12 indexed citations
7.
Barrachina, Sergio, et al.. (2020). Introduction to Programming Using Mobile Phones and MIT App Inventor. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje. 15(3). 192–201. 11 indexed citations
8.
Barrachina, Sergio, Maribel Castillo, Enrique S. Quintana–Ort́ı, et al.. (2018). FaST-LMM for Two-Way Epistasis Tests on High-Performance Clusters. Journal of Computational Biology. 25(8). 862–870. 5 indexed citations
9.
Barrachina, Sergio, et al.. (2018). Introducción a la arquitectura de computadores con QtARMSim y Arduino. Universitat Jaume I eBooks. 5 indexed citations
10.
Medina, Ignacio, Joaquín Tárraga, Sergio Barrachina, et al.. (2016). Highly sensitive and ultrafast read mapping for RNA-seq analysis. DNA Research. 23(2). 93–100. 13 indexed citations
11.
Barrachina, Sergio, et al.. (2015). Utilizando ARMSim y QtARMSim para la docencia de Arquitectura de Computadores. 8(3). 2. 2 indexed citations
12.
Barrachina, Sergio, et al.. (2015). Utilizando Arduino Due en la docencia de la entrada/salida. 58–65. 4 indexed citations
13.
Barrachina, Sergio, et al.. (2013). An Integrated Framework for Power-Performance Analysis of Parallel Scientific Workloads. 114–119. 27 indexed citations
14.
Barrachina, Sergio, et al.. (2011). AN INTERACTIVE ANIMATION FOR LEARNING HOW CACHE COHERENCE PROTOCOLS WORK. 6128–6132. 2 indexed citations
15.
Llorens, David, Andrés Marzal, Juan Miguel Vilar, et al.. (2008). The UJIpenchars Database: a Pen-Based Database of Isolated Handwritten Characters.. Language Resources and Evaluation. 32 indexed citations
16.
Barrachina, Sergio, et al.. (2005). Computer-assisted translation using finite-state transducers. Procesamiento del lenguaje natural. 35(35). 357–363. 1 indexed citations
17.
Aliaga, José I., Francisco Almeida, José M. Badía, et al.. (2005). Parallelization of GSL on Clusters of Symmetric Multiprocessors. 19(1). 333–340. 1 indexed citations
18.
Aliaga, José I., Francisco Almeida, José M. Badía, et al.. (2005). Parallelization of the GNU Scientific Library on Heterogeneous Systems. 61. 338–345. 3 indexed citations
19.
Civera, Jorge, Antonio L. Lagarda, Jorge González, et al.. (2004). From Machine Translation to Computer Assisted Translation using Finite-State Models. Empirical Methods in Natural Language Processing. 349–356. 15 indexed citations
20.
Marzal, Andrés & Sergio Barrachina. (2002). Speeding up the computation of the edit distance for cyclic strings. 2. 891–894. 14 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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