{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:00:08Z","timestamp":1760241608412,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,6,1]],"date-time":"2018-06-01T00:00:00Z","timestamp":1527811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003329","name":"Ministerio de Econom\u00eda y Competitividad","doi-asserted-by":"publisher","award":["TEC2014-58036-C4-4-R","TEC2017-86722-C4-1-R","TEC2017-86722-C4-2-R"],"award-info":[{"award-number":["TEC2014-58036-C4-4-R","TEC2017-86722-C4-1-R","TEC2017-86722-C4-2-R"]}],"id":[{"id":"10.13039\/501100003329","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Dimensionality reduction represents a critical preprocessing step in order to increase the efficiency and the performance of many hyperspectral imaging algorithms. However, dimensionality reduction algorithms, such as the Principal Component Analysis (PCA), suffer from their computationally demanding nature, becoming advisable for their implementation onto high-performance computer architectures for applications under strict latency constraints. This work presents the implementation of the PCA algorithm onto two different high-performance devices, namely, an NVIDIA Graphics Processing Unit (GPU) and a Kalray manycore, uncovering a highly valuable set of tips and tricks in order to take full advantage of the inherent parallelism of these high-performance computing platforms, and hence, reducing the time that is required to process a given hyperspectral image. Moreover, the achieved results obtained with different hyperspectral images have been compared with the ones that were obtained with a field programmable gate array (FPGA)-based implementation of the PCA algorithm that has been recently published, providing, for the first time in the literature, a comprehensive analysis in order to highlight the pros and cons of each option.<\/jats:p>","DOI":"10.3390\/rs10060864","type":"journal-article","created":{"date-parts":[[2018,6,4]],"date-time":"2018-06-04T08:59:41Z","timestamp":1528102781000},"page":"864","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons"],"prefix":"10.3390","volume":"10","author":[{"given":"Ernestina","family":"Martel","sequence":"first","affiliation":[{"name":"Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35001 Las Palmas de Gran Canaria, Las Palmas, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2645-6749","authenticated-orcid":false,"given":"Raquel","family":"Lazcano","sequence":"additional","affiliation":[{"name":"Universidad Politecnica de Madrid, 28031 Madrid, Spain"}]},{"given":"Jos\u00e9","family":"L\u00f3pez","sequence":"additional","affiliation":[{"name":"Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35001 Las Palmas de Gran Canaria, Las Palmas, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5994-7440","authenticated-orcid":false,"given":"Daniel","family":"Madro\u00f1al","sequence":"additional","affiliation":[{"name":"Universidad Politecnica de Madrid, 28031 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0021-5808","authenticated-orcid":false,"given":"Rub\u00e9n","family":"Salvador","sequence":"additional","affiliation":[{"name":"Universidad Politecnica de Madrid, 28031 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2360-6721","authenticated-orcid":false,"given":"Sebasti\u00e1n","family":"L\u00f3pez","sequence":"additional","affiliation":[{"name":"Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35001 Las Palmas de Gran Canaria, Las Palmas, Spain"}]},{"given":"Eduardo","family":"Juarez","sequence":"additional","affiliation":[{"name":"Universidad Politecnica de Madrid, 28031 Madrid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4303-3051","authenticated-orcid":false,"given":"Ra\u00fal","family":"Guerra","sequence":"additional","affiliation":[{"name":"Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35001 Las Palmas de Gran Canaria, Las Palmas, Spain"}]},{"given":"C\u00e9sar","family":"Sanz","sequence":"additional","affiliation":[{"name":"Universidad Politecnica de Madrid, 28031 Madrid, Spain"}]},{"given":"Roberto","family":"Sarmiento","sequence":"additional","affiliation":[{"name":"Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35001 Las Palmas de Gran Canaria, Las Palmas, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,1]]},"reference":[{"key":"ref_1","unstructured":"Chang, C.-I. (2003). Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Kluwer Academic."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chang, C.-I. (2013). Hyperspectral Data Processing: Algorithm Design and Analysis, Wiley.","DOI":"10.1002\/9781118269787"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chang, C.-I. (2007). Hyperspectral Data Exploitation: Theory and Applications, Wiley.","DOI":"10.1002\/0470124628"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/MGRS.2017.2762087","article-title":"Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art","volume":"5","author":"Ghamisi","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2244672","article-title":"Hyperspectral Remote Sensing Data Analysis and Future Challenges","volume":"1","author":"Plaza","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1037\/h0070888","article-title":"Analysis of a complex of statistical variables into principal components","volume":"24","author":"Hotelling","year":"1933","journal-title":"J. Educ. Psychol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Plaza, A., and Chang, C.-I. (2007). High Performance Computing in Remote Sensing, Chapman & Hall\/CRC Press.","DOI":"10.1201\/9781420011616"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1109\/JSTARS.2011.2162643","article-title":"Recent Developments in High Performance Computing for Remote Sensing: A Review","volume":"4","author":"Lee","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1109\/JSTARS.2010.2095495","article-title":"High Performance Computing for Hyperspectral Remote Sensing","volume":"4","author":"Plaza","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Fernandez, D., Gonzalez, C., Mozos, D., and Lopez, S. (2016). FPGA implementation of the Principal Component Analysis algorithm for dimensionality reduction of hyperspectral images. J. Real Time Image Process., 1\u201312.","DOI":"10.1007\/s11554-016-0650-7"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1007\/s11554-012-0269-2","article-title":"Real-Time Implementation of Remotely Sensed Hyperspectral Image Unmixing on GPUs","volume":"10","author":"Sanchez","year":"2015","journal-title":"J. Real Time Image Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2270","DOI":"10.1109\/JSTARS.2016.2542193","article-title":"Parallel and Distributed Dimensionality Reduction of Hyperspectral Data on Cloud Computing Architectures","volume":"9","author":"Wu","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.isprsjprs.2013.12.003","article-title":"UL-Isomap based nonlinear dimensionality reduction for hyperspectral imagery classification","volume":"89","author":"Sun","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1109\/JSTARS.2013.2238890","article-title":"Nonlinear Dimensionality Reduction via the ENH-LTSA Method for Hyperspectral Image Classification","volume":"7","author":"Sun","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Sun, W., and Du, Q. (2018). Graph-Regularized Fast and Robust Principal Component Analysis for Hyperspectral Band Selection. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2018.2794443"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4032","DOI":"10.1109\/TGRS.2017.2686842","article-title":"A Sparse and Low-Rank Near-Isometric Linear Embedding Method for Feature Extraction in Hyperspectral Imagery Classification","volume":"55","author":"Sun","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","first-page":"9","article-title":"Iterative methods for computing eigenvalues and eigenvectors","volume":"1","author":"Panju","year":"2011","journal-title":"Waterloo Math. Rev."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Golub, G.H., and van Loan, C.F. (2013). Matrix Computations, John Hopkins University Press. [4th ed.].","DOI":"10.56021\/9781421407944"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Quarteroni, A., Sacco, R., and Saleri, F. (2007). Numerical Mathematics, Springer. [2nd ed.].","DOI":"10.1007\/978-0-387-22750-4"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1090\/S0002-9947-1960-0109825-2","article-title":"The cyclic Jacobi method for computing the principal values of a complex matrix","volume":"94","author":"Forsythe","year":"1960","journal-title":"Trans. Am. Math. Soc."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"de Dinechin, B.D., Ayrignac, R., Beaucamps, Pi., Couvert, P., Ganne, B., de Massas, P.G., Jacquet, F., Jones, S., Chaisemartin, N.M., and Riss, F. (2013, January 10\u201312). A Clustered Manycore Processor Architecture for Embedded and Accelerated Applications. Proceedings of the IEEE High Performance Extreme Computing Conference (HPEC), Waltham, MA, USA.","DOI":"10.1109\/HPEC.2013.6670342"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Roux, B., Gautier, M., Sentieys, O., and Derrien, S. (2016, January 21\u201323). Communication-Based Power Modelling for Heterogeneous Multiprocessor Architectures. Proceedings of the IEEE 10th International Symposium on Embedded Multicore\/Many-core Systems-on-Chip (MCSoC), Lyon, France.","DOI":"10.1109\/MCSoC.2016.27"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.sysarc.2017.05.001","article-title":"Porting a PCA-based hyperspectral image dimensionality reduction algorithm for brain cancer detection on a manycore architecture","volume":"77","author":"Lazcano","year":"2017","journal-title":"J. Syst. Archit."},{"key":"ref_24","unstructured":"CUDA Toolkit Documentation (2018, April 06). NVIDIA Developer Documentation. Available online: http:\/\/docs.nvidia.com\/cuda\/."},{"key":"ref_25","unstructured":"(2018, April 06). THRUST: CUDA Toolkit Documentation. Available online: http:\/\/docs.nvidia.com\/cuda\/thrust\/index.html."},{"key":"ref_26","unstructured":"Lazcano, R., Madro\u00f1al, D., Desnos, K., Pelcat, M., Guerra, R., L\u00f3pez, S., Juarez, E., and Sanz, C. (2016, January 12\u201314). Parallelism Exploitation of a Dimensionality Reduction Algorithm Applied to Hyperspectral Images. Proceedings of the 2016 Conference on Design and Architectures for Signal and Image Processing (DASIP), Rennes, France."},{"key":"ref_27","unstructured":"Clark, R.N., Swayze, G.A., Wise, R., Livo, E., Hoefen, T., Kokaly, R., and Sutley, S.J. (2018, May 24). USGS Digital Spectral Library splib06a: U.S. Geological Survey, Digital Data Series 231, Available online: http:\/\/speclab.cr.usgs.gov\/spectral.lib06."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Saidi, S., Ernst, R., Uhrig, S., Theiling, H., and de Dinechin, B.D. (2015, January 4\u20139). The Shift to Multicores in Real-Time and Safety-Critical Systems. Proceedings of the 10th International Conference on Hardware\/Software Codesign and System Synthesis, CODES+ISSS 2015, Amsterdam, The Netherlands.","DOI":"10.1109\/CODESISSS.2015.7331385"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"De Dinechi, B.D., and Graillat, A. (2017, January 25). Network-on-Chip Service Guarantees on the Kalray MPPA-256 Bostan Processor. Proceedings of the 2nd International Workshop on Advanced Interconnect Solutions and Technologies for Emerging Computing Systems, Stockholm, Sweden.","DOI":"10.1145\/3073763.3073770"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"De Dinechin, B.D. (2015, January 22\u201325). Kalray MPPA: Massively Parallel Processor Array: Revisiting DSP Acceleration with the Kalray MPPA Manycore Processor. Proceedings of the 2015 IEEE Hot Chips 27 Symposium (HCS), Cupertino, CA, USA.","DOI":"10.1109\/HOTCHIPS.2015.7477332"},{"key":"ref_31","unstructured":"(2018, April 09). Xilinx Boards and Kits\u2014Power Supply Information. Available online: https:\/\/www.xilinx.com\/support\/answers\/67507.html."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/6\/864\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:06:57Z","timestamp":1760195217000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/6\/864"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,1]]},"references-count":31,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2018,6]]}},"alternative-id":["rs10060864"],"URL":"https:\/\/doi.org\/10.3390\/rs10060864","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2018,6,1]]}}}