Bio

I am an associate professor at CITIUS in University of Santiago de Compostela (Galicia, Spain). My research interests include parallel and distributed computing, Big Data technologies, programming models and software optimization techniques for emerging architectures. I received the B.Sc. in physics and the Ph.D. in computer science (2006) from University of Santiago de Compostela (Spain). I was a visiting postdoctoral researcher at University Carlos III de Madrid (Spain) and University of Illinois at Urbana-Champaign (USA), and I also worked as researcher and project manager at Galicia Supercomputing Center (Spain).

Publications

Journals

  1. A Big Data Platform for Real Time Analysis of Signs of Depression in Social Media   
    Rodrigo Martínez-Castaño, Juan C. Pichel and David E. Losada.
    Int. Journal of Environmental Research and Public Health, Vol. 17 (3), 2020.
  2. VeryFastTree: speeding up the estimation of phylogenies for large alignments through parallelization and vectorization strategies   
    César Piñeiro, José M. Abuín and Juan C. Pichel.
    Bioinformatics, Vol. 36, Issue 17, pages 4658-4659, 2020.
  3. A Big Data Approach to Metagenomics for All-food-sequencing      
    Robin Kobus, José M. Abuín, André Müller, Sören Lukas Hellmann, Juan C. Pichel, Tomás F. Pena, Andreas Hildebrandt, Thomas Hankeln and Bertil Schmidt.
    BMC Bioinformatics, Vol. 21 (102), 2020.
  4. Ignis: An efficient and scalable multi-language Big Data framework   
    César Piñeiro, Rodrigo Martínez-Castaño and Juan C. Pichel.
    Future Generation Computer Systems, Vol. 150, pages 705-716, 2020.
  5. Sparse Matrix Classification on Imbalanced Datasets using Convolutional Neural Networks   
    Juan C. Pichel and Beatriz Pateiro-López.
    IEEE Access, Vol. 7, pages 82377-82389, 2019.
  6. PASTASpark: multiple sequence alignment meets Big Data   
    José M. Abuín, Tomás F. Pena and Juan C. Pichel.
    Bioinformatics, Vol. 33, Issue 18, pages 2948-2950, 2017.
  7. SparkBWA: Speeding Up the Alignment of High-Throughput DNA Sequencing Data   
    José M. Abuín, Juan C. Pichel, Tomás F. Pena and Jorge Amigo.
    PLoS ONE, Vol. 11, Issue 5, pages 1-21, 2016.
  8. Boosting Performance of a Statistical Machine Translation System Using Dynamic Parallelism   
    M. Fernández, Juan C. Pichel, José C. Cabaleiro and Tomás F. Pena.
    Journal of Computational Science, Vol. 13, pages 37-48, 2016.
  9. BigBWA: Approaching the Burrows-Wheeler Aligner to Big Data Technologies   
    José M. Abuín, Juan C. Pichel, Tomás F. Pena and Jorge Amigo.
    Bioinformatics, Vol. 31, Issue 24, pages 4003-4005, 2015.
  10. Power and Energy Implications of the Number of Threads Used on the Intel Xeon Phi   
    Oscar G. Lorenzo, Tomás F. Pena, José C. Cabaleiro, Juan C. Pichel, F.F. Rivera and D. S. Nikolopoulos.
    Annals of Multicore and GPU Programming, Vol. 2, Issue 1, pages 55-65, 2015.
  11. Análisis Morfosintáctico y Clasificación de Entidades Nombradas en un Entorno Big Data   
    Pablo Gamallo, Juan C. Pichel, Marcos García, José M. Abuín and Tomás F. Pena.
    Procesamiento del Lenguaje Natural, Vol. 53, pages 17-24, 2014.
  12. Using an Extended Roofline Model to Understand Data and Thread Affinities on NUMA Systems   
    Oscar G. Lorenzo, Tomás F. Pena, José C. Cabaleiro, Juan C. Pichel and Francisco F. Rivera.
    Annals of Multicore and GPU Programming, Vol. 1, Issue 1, pages 56-67, 2014.
  13. A Hardware Counter-Based Toolkit for the Analysis of Memory Accesses in SMPs
    Oscar G. Lorenzo, Tomás F. Pena, José C. Cabaleiro, Juan C. Pichel, Juan A. Lorenzo and Francisco F. Rivera.
    Concurrency and Computation: Practice and Experience, Vol. 26, Issue 6, pages 1328-1341, 2014.
  14. Using Sampled Information, Is It Enough for the SpMV Locality Optimization?   
    Juan C. Pichel, Juan A. Lorenzo, Dora B. Heras, Francisco F. Rivera and Tomás F. Pena.
    Concurrency and Computation: Practice and Experience, Vol. 26, Issue 1, pages 98-117, 2014.
  15. 3DyRM: A Dynamic Roofline Model Including Memory Latency Information
    Oscar G. Lorenzo, Tomás F. Pena, Juan C. Pichel, José C. Cabaleiro and Francisco F. Rivera.
    Journal of Supercomputing, Vol. 70, Issue 2, pages 696-708, 2014.
  16. Sparse Matrix–Vector Multiplication on the Single-Chip Cloud Computer Many-Core Processor   
    Juan C. Pichel and Francisco F. Rivera.
    Journal of Parallel and Distributed Computing, Vol. 73, Issue 12, pages 1539-1550, 2013.
  17. A Flexible and Dynamic Page Migration Infrastructure Based on Hardware Counters   
    Juan A. Lorenzo, Juan C. Pichel, Francisco F. Rivera, Jose C. Cabaleiro and Tomás F. Pena.
    Journal of Supercomputing, Vol. 65, Issue 2, pages 930-948, 2013.
  18. Optimization of Sparse Matrix-Vector Multiplication Using Reordering Techniques on GPUs   
    Juan C. Pichel, Francisco F. Rivera, Marcos Fernández and Aurelio Rodríguez.
    Microprocessors and Microsystems, Vol. 36, Issue 2, pages 65-77, 2012.
  19. Analyzing the Execution of Sparse Matrix-Vector Product on the Finisterrae SMP-NUMA System
    Juan C. Pichel, Juan A. Lorenzo, Dora B. Heras, José C. Cabaleiro and Tomás F. Pena.
    Journal of Supercomputing, Vol. 58, Issue 2, pages 195-205, 2011.
  20. Increasing the Locality of Iterative Methods and its Application to the Simulation of Semiconductor Devices   
    Juan C. Pichel, Dora B. Heras, José C. Cabaleiro, A. J. Garcia-Loureiro and Francisco F. Rivera.
    Int. Journal of High Performance Computing Applications, Vol. 24, Issue 2, pages 136-153, 2010.
  21. Increasing Data Reuse of Sparse Algebra Codes on Simultaneous Multithreading Architectures   
    Juan C. Pichel, Dora B. Heras, José C. Cabaleiro and Francisco F. Rivera.
    Concurrency and Computation: Practice and Experience, Vol. 21, Issue 15, pages 1838-1856, 2009.
  22. A Collective I/O Implementation Based on Inspector-Executor Paradigm
    David E. Singh, Florin Isaila, Juan C. Pichel and Jesús Carretero.
    Journal of Supercomputing, Vol. 47, Issue 1, pages 53-75, 2009.
  23. Image Segmentation Based on Merging of Sub-Optimal Segmentations   
    Juan C. Pichel, David E. Singh and Francisco F. Rivera.
    Pattern Recognition Letters, Vol. 27, Issue 10, pages 1105-1116, 2006.
  24. Performance Optimization of Irregular Codes Based on the Combination of Reordering and Blocking Techniques   
    Juan C. Pichel, Dora B. Heras, José C. Cabaleiro and Francisco F. Rivera.
    Parallel Computing, Vol. 31, Issue 8-9, pages 858-876, 2005.

Conferences

  1. Comparing Traditional and Neural Approaches for detecting Health-related Misinformation   
    Marcos Fernández-Pichel, David E. Losada, Juan C. Pichel and David Elsweiler.
    Conference and Lab of the Evaluation Forum (CLEF). Bucharest, Romania, 2021.
  2. Reliability Prediction for Health-related Content: A Replicability Study   
    Marcos Fernández-Pichel, David E. Losada, Juan C. Pichel and David Elsweiler.
    European Conference on Information Retrieval (ECIR). Lucca, Italy, 2021.
  3. Colaboración entre docentes de una universidad alemana y una española para el desarrollo de seminarios prácticos acerca de la credibilidad de la información   
    Marcos Fernández-Pichel, David Elsweiler, David E. Losada and Juan C. Pichel.
    XXVII Jornadas sobre la Enseñanza Universitaria de la Informática (JENUI). Valencia, Spain, 2021.
  4. CiTIUS at the TREC 2020 Health Misinformation Track   
    Marcos Fernández-Pichel, David E. Losada, Juan C. Pichel and David Elsweiler.
    Text Retrieval Conference (TREC). Gaithersburg, USA, 2020.
  5. eXtream: a System for Real-time Monitoring of Dynamic Web Sources    
    Marcos Fernández-Pichel, Rodrigo Martínez-Castaño, David E. Losada and Juan C. Pichel.
    Joint Conference of the Information Retrieval Communities in Europe (CIRCLE). Samatan, France, July 2020.
  6. Dataflow Execution of Hierarchically Tiled Arrays   
    Chih-Chieh Yang, Juan C. Pichel and David A. Padua.
    European Conference on Parallel and Distributed Computing (Euro-Par). Göttingen, Germany, August 2019.
  7. LinguaKit: a Big Data-based multilingual tool for linguistic analysis and information extraction      
    Pablo Gamallo, Marcos Garcia, César Piñeiro, Rodrigo Martínez-Castaño and Juan C. Pichel.
    Int. Workshop on Advances in Natural Language Processing (ANLP). Valencia, Spain, October 2018.
  8. A New Approach for Sparse Matrix Classification Based on Deep Learning Techniques      
    Juan C. Pichel and Beatriz Pateiro-López.
    IEEE Cluster (CLUSTER). Belfast, UK, September 2018.
  9. Towards a Big Data Multi-language Framework using Docker Containers
    César Piñeiro, Rodrigo Martínez-Castaño and Juan C. Pichel.
    Jornadas Sarteco (JP). Teruel, Spain, September 2018.
  10. Building Python-Based Topologies for Massive Processing of Social Media Data in Real Time   
    Rodrigo Martínez-Castaño, Juan C. Pichel and David E. Losada.
    5th Spanish Conference in Information Retrieval (CERI). Zaragoza, Spain, June 2018.
  11. A Micromodule Approach for Building Real-Time Systems with Python-Based Models: Application to Early Risk Detection of Depression on Social Media   
    Rodrigo Martínez-Castaño, Juan C. Pichel, David E. Losada and Fabio Crestani.
    40th European Conference on Information Retrieval (ECIR). Grenoble, France, March 2018.
  12. Perldoop2: a Big Data-oriented source-to-source Perl-Java compiler     
    César Piñeiro, José M. Abuín and Juan C. Pichel.
    IEEE Int. Conference on Big Data Intelligence and Computing (DataCom). Orlando, USA, November 2017.
  13. Sentiment Analysis on Multilingual Tweets using Big Data Technologies   
    Rodrigo Martínez-Castaño, Juan C. Pichel and Pablo Gamallo.
    Jornadas Sarteco (JP). Salamanca, Spain, September 2016.
  14. Power and Energy Implications of the Number of Threads Used on the Intel Xeon Phi
    Oscar G. Lorenzo, Tomás F. Pena, José C. Cabaleiro, Juan C. Pichel, F.F. Rivera and D. S. Nikolopoulos.
    2nd Congress on Multicore and GPU Programming. Cáceres, Spain, March 2015.
  15. Perldoop: Efficient Execution of Perl Scripts on Hadoop Clusters   
    José M. Abuín, Juan C. Pichel, Tomás F. Pena, Pablo Gamallo and Marcos García.
    IEEE Int. Conference on Big Data (IEEE Big Data). Washington D.C., USA, October 2014.
  16. Thread Migration Techniques Based on Dynamic Roofline Models and Latency Information
    Oscar G. Lorenzo, Tomás F. Pena, José C. Cabaleiro, Juan C. Pichel and F.F. Rivera.
    XXV Jornadas de Paralelismo. Valladolid, Spain, September 2014.
  17. Multiobjective Optimization Technique Based on Monitoring Information to Increase the Performance of Thread Migration on Multicores
    Oscar G. Lorenzo, Tomás F. Pena, José C. Cabaleiro, Juan C. Pichel and F.F. Rivera
    IEEE Cluster (CLUSTER). Madrid, Spain, September 2014.
  18. Hierarchically Tiled Array as a High-Level Abstraction for Codelets   
    Chih-Chieh Yang, Juan C. Pichel, Adam R. Smith and David A. Padua.
    4th Int. Workshop on Data-Flow Models for Extreme Scale Computing (DFM). Edmonton, Alberta, Canada, August 2014.
  19. DyRM: A Dynamic Roofline Model Based on Runtime Information
    Oscar G. Lorenzo, Tomás F. Pena, José C. Cabaleiro, Juan C. Pichel and Francisco F. Rivera.
    13th Int. Conference on Computational and Mathematical Methods in Science and Engineering (CMMSE). Almería, Spain, June 2013.
  20. Hardware Counters Based Analysis of Memory Accesses in SMPs
    Oscar G. Lorenzo, Tomás F. Pena, Jose C. Cabaleiro, Juan C. Pichel, Juan A. Lorenzo and Francisco F. Rivera.
    10th IEEE Int. Symposium on Parallel and Distributed Processing with Applications (ISPA). Leganés, Spain, July 2012.
  21. A Graphical Tool for Performance Analysis of Multicore Systems Based on the Roofline Model
    Francisco F. Rivera, R. Iglesias, Juan A. Lorenzo, Juan C. Pichel, Tomás F. Pena and Jose C. Cabaleiro.
    10th IEEE Int. Symposium on Parallel and Distributed Processing with Applications (ISPA). Leganés, Spain, July 2012.
  22. Experiences with the Sparse Matrix-Vector Multiplication on a Many-Core Processor      
    Juan C. Pichel and Francisco F. Rivera.
    21st Int. Heterogeneity in Computing Workshop (HCW, together with IPDPS). Shanghai, China, May 2012.
  23. Herramientas para la Monitorización de los Accesos a Memoria de Códigos Paralelos Mediante Contadores Hardware
    Oscar G. Lorenzo, Juan A. Lorenzo, Dora B. Heras, Juan C. Pichel and Francsico F. Rivera.
    XXII Jornadas de Paralelismo. La Laguna, Spain, September 2011.
  24. Study of Performance Issues on a SMP-NUMA System Using the Roofline Model
    Juan A. Lorenzo, Juan C. Pichel, Tomás F. Pena, Marcos Suarez and Francisco F. Rivera.
    Int. Conf. on Parallel and Distributed Processing Techniques and Applications (PDPTA). Las Vegas, USA, July 2011.
  25. A Study of Memory Access Patterns in Irregular Parallel Codes Using Hardware Counter-Based Tools
    Oscar G. Lorenzo, Juan A. Lorenzo, José C. Cabaleiro, Dora B. Heras, Marcos Suarez, and Juan C. Pichel.
    Int. Conf. on Parallel and Distributed Processing Techniques and Applications (PDPTA). Las Vegas, USA, July 2011.
  26. Lessons Learnt Porting Parallelisation Techniques for Irregular Codes to NUMA Systems   
    Juan A. Lorenzo, Juan C. Pichel, David LaFrance-Linden, Francisco F. Rivera and David E. Singh.
    18th Euromicro Conference on Parallel, Distributed and Network based Processing (PDP). Pisa, Italia, February 2010.
  27. On the Influence of Thread Allocation for Irregular Codes in NUMA Systems   
    Juan A. Lorenzo, Francisco F. Rivera, Petr Tuma and Juan C. Pichel.
    10th Int. Conf. on Parallel and Distributed Computing, Applications and Technologies (PDCAT). Hiroshima, Japan, December 2009.
  28. Thread Allocation Issues for Irregular Codes in the Finisterrae System
    Juan A. Lorenzo, Francisco F. Rivera, Dora B. Heras, José C. Cabaleiro, Tomás F. Pena, Juan C. Pichel and David E. Singh.
    XX Jornadas de Paralelismo. A Coruña, Galicia, Spain, September 2009.
  29. Evaluating Sparse Matrix-Vector Product on the FinisTerrae Supercomputer   
    Juan C. Pichel, Juan A. Lorenzo, Dora B. Heras and José C. Cabaleiro.
    9th Int. Conference on Computational and Mathematical Methods in Science and Engineering (CMMSE). Gijón, Spain, June 2009.
  30. Exploiting Data Compression in Collective I/O Techniques   
    Rosa Filgueira, David E. Singh, Juan C. Pichel and Jesús Carretero.
    IEEE Int. Conference on Cluster Computing. Tsukuba, Japan, September 2008.
  31. Reordering Algorithms for Increasing Locality on Multicore Processors   
    Juan C. Pichel, David E. Singh and Jesús Carretero.
    10th IEEE Int. Conference on High Performance Computing and Communications (HPCC). Dalian, China, September 2008.
  32. Data Locality Aware Strategy for Two-Phase Collective I/O   
    Rosa Filgueira, David E. Singh, Juan C. Pichel, Florin Isaila and Jesús Carretero.
    Int. Meeting High Performance Computing for Computational Science (VECPAR). Toulouse, France, June 2008.
  33. A Collective I/O Implementation Based on Inspector-Executor Paradigm   
    David E. Singh, Florin Isaila, Juan C. Pichel and Jesús Carretero.
    Int. Workshop on Scalable Data Management Applications and Systems (SDMAS). Las Vegas, USA, June 2007.
  34. A New Technique to Reduce False Sharing in Irregular Codes Based on Distance Functions   
    Juan C. Pichel, Dora B. Heras, José C. Cabaleiro and Francisco F. Rivera.
    8th Int. Symposium on Parallel Architectures, Algorithms and Networks (I-SPAN). pp. 306-311. Las Vegas, USA, December 2005.
  35. Mejora de la Localidad en SMPs: el Producto Matriz Dispersa-Vector como Caso de Estudio
    Juan C. Pichel, Dora B. Heras, José C. Cabaleiro, Marcos Boullón, David E. Singh and Francsico F. Rivera.
    XV Jornadas de Paralelismo. Almería, Spain, September 2004.
  36. Improving the Locality of the Sparse Matrix-Vector product on Shared Memory Multiprocessors   
    Juan C. Pichel, Dora B. Heras, José C. Cabaleiro and Francisco F. Rivera.
    12th Euromicro Conference on Parallel, Distributed and Network based Processing (PDP). A Coruña, Galicia, February 2004.
  37. Algoritmo Paralelo de Segmentación de Imágenes Basado en el Crecimiento Desacoplado de Regiones
    Juan C. Pichel, David E. Singh and Francisco F. Rivera.
    Conferencia Iberoamericana en Sistemas, Cibernética e Informática (CISCI). pp. 134-139. Orlando, USA, July 2002.

Preprints

  1. A unified framework to improve the interoperability between HPC and Big Data languages and programming models     
    César Piñeiro and Juan C. Pichel
    arXiv:2112.00467, 2021.
  2. Polypus: a Big Data Self-Deployable Architecture for Microblogging Text Extraction and Real-Time Sentiment Analysis     
    Rodrigo Martínez-Castaño, Juan C. Pichel and Pablo Gamallo
    arXiv:1801.03710, 2018.

Book chapters

  1. A Parallel Framework for Image Segmentation Using Region Based Techniques   
    Juan C. Pichel, David E. Singh and Francisco F. Rivera
    Vision Systems: Segmentation and Pattern Recognition, edited by Goro Obinata and Ashish Dutta, 2007.

Software

VeryFastTree

  

VeryFastTree is a highly-tuned implementation of the FastTree-2 tool that takes advantage of parallelization and vectorization strategies to speed up the inference of phylogenies for huge alignments. It is important to highlight that VeryFastTree keeps unchanged the phases, methods and heuristics used by FastTree-2 to estimate the phylogenetic tree. In this way, it produces trees with the same topological accuracy than FastTree-2. In addition, unlike the parallel version of FastTree-2, VeryFastTree is deterministic. Regarding the performance, for example, VeryFastTree is able to construct a tree on a standard server (12-core Intel Xeon E5-2680v3 processor and 128 GiB of memory) using double precision arithmetic from an ultra-large 330k alignment in only 4.5 hours, which is 7.8× and 3.5× faster than the sequential and best parallel FastTree-2 times, respectively.

Citation:
César Piñeiro, José M. Abuín and Juan C. Pichel. VeryFastTree: speeding up the estimation of phylogenies for large alignments through parallelization and vectorization strategies. Bioinformatics, Vol. 36, Issue 17, pages 4658-4659, 2020.

Perldoop2

  

The most relevant Big Data frameworks do not support natively the Perl language. To take advantage of these Big Data engines Perl programmers should port their applications to Java or Scala, which requires a huge effort, or use utilities as Hadoop Streaming with the corresponding degradation in the performance. For this reason we introduce Perldoop2, a Big Data-oriented Perl-Java source-to-source compiler. The compiler is able to generate Java code from Perl applications for sequential execution, but also for running on clusters taking advantage of Hadoop, Spark and Storm engines.

Citation:
César Piñeiro, José M. Abuín and Juan C. Pichel. Perldoop2: a Big Data-oriented source-to-source Perl-Java compiler. IEEE Int. Conference on Big Data Intelligence and Computing (DataCom), pp. 933-940, 2017.

PASTASpark

  

PASTASpark is a tool that uses the Big Data engine Apache Spark to boost the performance of the alignment phase of PASTA (Practical Alignments using SATé and TrAnsitivity). PASTASpark guarantees scalability and fault tolerance, and allows to obtain MSAs from very large datasets in reasonable time.

Citation:
José M. Abuín, Tomás F. Pena and Juan C. Pichel. PASTASpark: multiple sequence alignment meets Big Data. Bioinformatics, Vol. 33, Issue 18, pp. 2948-2950, 2017.

SparkBWA

  

SparkBWA is a new tool that exploits the capabilities of a Big Data technology as Apache Spark to boost the performance of one of the most widely adopted DNA sequence aligner, the Burrows-Wheeler Aligner (BWA).

Citation:
José M. Abuín, Juan C. Pichel, Tomás F. Pena and Jorge Amigo. SparkBWA: Speeding Up the Alignment of High-Throughput DNA Sequencing Data. PLoS ONE, Vol. 11, Issue 5, pp. 1-21, 2016.

BigBWA

  

BigBWA allows to execute the Burrows-Wheeler Aligner (BWA) on an Apache Hadoop cluster.

Citation:
José M. Abuín, Juan C. Pichel, Tomás F. Pena and Jorge Amigo. BigBWA: Approaching the Burrows-Wheeler Aligner to Big Data Technologies. Bioinformatics, Vol. 31, Issue 24, pp. 4003-4005, 2015.

Projects

Here you can find a list of some of the most relevant research projects I am/was involved with:

eRISK: Technologies for the early prediction of signs related with psychological disorders
Funded by Ministerio de Economía, Industria y Competitividad (RTI2018-093336-B-C21)
Period: Jan 2019 - Dec 2021
BigNLP: Approaching High Performance Computing to Big Data Technologies: Natural Language Processing as Case Study
Funded by Ministerio de Economía y Competitividad (TIN2014-54565-JIN)
Period: Sep 2015 - Sep 2018
HPCNLP: High Performance Computing for Natural Language Processing   
Funded by Xunta de Galicia (EM2013/041)
Period: Aug 2013 - Dec 2014
CELTIC: Strategic Knowledge led by technologies for the Competitive Intelligence
Funded by FEDER-INNTERCONECTA, CDTI, Imaxin Software
Period: Jan 2012 - Dec 2014
Evaluation and Optimization of the Locality of Irregular Applications on the SCC (Single-Chip Cloud Computer) Architecture
Funded by Intel Corporation  
Period: Oct 2009 - Oct 2012
OPTIMIZA: Optimization of Irregular Applications on High Performance CPU/GPU Emerging Architectures
Funded by Xunta de Galicia (PGIDIT09TIC002CT)
Period: Oct 2009 - Oct 2012
Using Hardware Counters to Improve Memory Performance: Irregular Codes and Page Migration
Funded by HP Labs  
Period: May 2008 - Apr 2011
Improving UPC Usability and Performance in Constellation Systems
Funded by HP Labs  
Period: May 2008 - Apr 2011

Contact

Juan Carlos Pichel
CITIUS (Universidade de Santiago de Compostela)
Rúa de Jenaro de la Fuente
15782 Santiago de Compostela (Spain)
juancarlos.pichel@usc.es
   +34 881816437