Towards cloud-based parallel metaheuristics: A case study in computational biology with Differential Evolution and Spark

[Abstract] Many key problems in science and engineering can be formulated and solved using global optimization techniques. In the particular case of computational biology, the development of dynamic (kinetic) models is one of the current key issues. In this context, the problem of parameter estimation (model calibration) remains as a very challenging task. The complexity of the underlying models requires the use of efficient solvers to achieve adequate results in reasonable computation times. Metaheuristics have been the focus of great consideration as an efficient way of solving hard global optimization problems. Even so, in most realistic applications, metaheuristics require a very large computation time to obtain an acceptable result. Therefore, several parallel schemes have been proposed, most of them focused on traditional parallel programming interfaces and infrastructures. However, with the emergence of cloud computing, new programming models have been proposed to deal with large-scale data processing on clouds. In this paper we explore the applicability of these new models for global optimization problems using as a case study a set of challenging parameter estimation problems in systems biology. We have developed, using Spark, an island-based parallel version of Differential Evolution. Differential Evolution is a simple population-based metaheuristic that, at the same time, is very popular for being very efficient in real function global optimization. Several experiments were conducted both on a cluster and on the Microsoft Azure public cloud to evaluate the speedup and efficiency of the proposal, concluding that the Spark implementation achieves not only competitive speedup against the serial implementation, but also good scalability when the number of nodes grows. The results can be useful for those interested in using parallel metaheuristics for global optimization problems benefiting from the potential of new cloud programming models.Ministerio de Economía y Competitividad and FEDER; through the Project SYNBIOFACTORY; DPI2014-55276-C5-2-RMinisterio de Economía y Competitividad and FEDER; TIN2013-42148-PMinisterio de Economía y Competitividad and FEDER; TIN2016-75845-PXunta de Galicia; R2014/04

Optimizing parcel exchange among landowners: A soft alternative to land consolidation

© 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article [D. Teijeiro, E. Corbelle Rico, J. Porta, J. Parapar, y R. Doallo, «Optimizing parcel exchange among landowners: A soft alternative to land consolidation», Computers, Environment and Urban Systems, vol. 79, p. 101422, ene. 2020] has been accepted for publication in Computers, Environment and Urban Systems. The Version of Record is available online at: https://doi.org/10.1016/j.compenvurbsys.2019.101422.[Abstract]: For decades, public policy has favored the use of land consolidation to reduce the fragmentation of land ownership. Private actors, on the other hand, have focused on the purchase, rental and exchange of land plots. Plot exchange can be very useful in the restructuring of holdings, particularly when a large number of owners participate; however, the number of possible exchange combinations grows very quickly with the number of participating landowners and parcels. Finding an acceptable exchange solution can easily become challenging. In this paper we evaluate the practical use of a support system for land exchange processes. The system is based on the use of genetic algorithms, a particular kind of heuristics that loosely replicate the rules of evolution and natural selection. We assess the influence of the geometric distribution of parcels in the quality of the solution, as well as usefulness and performance of the system, via parallelization techniques. The proposed algorithm (GA-PE, Genetic Algorithm for Parcel Exchange) is tested with regards to several parameters, from several alternatives for certain steps of the algorithm to the resource distribution for the parallelizations implemented. We tested the algorithm in 6 different real and representative test cases, and provide results with different metrics. With the positive results obtained, we argue that land exchange is a process worth considering for private actors, and that genetic algorithms can be used to propose fair exchanges, even in complex scenarios, shortening in a meaningful way the time usually required to perform administrative procedures associated to land fragmentation problems.This research has been supported by the Government of Galicia (Xunta de Galicia) under the Consolidation Programme of Competitive Reference Groups, co-founded by ERDF funds from the EU [Ref. ED431C 2017/04]; under the Consolidation Programme of Competitive Research Units, co-founded by ERDF funds from the EU [Ref. R2016/037]; by Xunta de Galicia (Centro Singular de Investigación de Galicia accreditation 2016/2019) and the European Union (European Regional Development Fund, ERDF) under Grant Ref. ED431G/01.Xunta de Galicia; ED431C 2017/04Xunta de Galicia; R2016/037Xunta de Galicia; ED431G/0

Using the Cloud for Parameter Estimation Problems: Comparing Spark vs MPI with a Case-Study

Date of Conference: 14-17 May 2017. Conference Location: Madrid[Abstract] Systems biology is an emerging approach focused in generating new knowledge about complex biological systems by combining experimental data with mathematical modeling and advanced computational techniques. Many problems in this field are extremely challenging and require substantial supercomputing resources to be solved. This is the case of parameter estimation in large-scale nonlinear dynamic systems biology models. Recently, Cloud Computing has emerged as a new paradigm for on-demand delivery of computing resources. However, scientific computing community has been quite hesitant in using the Cloud, simply because traditional programming models do not fit well with the new paradigm, and the earliest cloud programming models do not allow most scientific computations being efficiently run in the Cloud. In this paper we explore and compare two distributed computing models: the MPI (message-passing interface) model, that is high-performance oriented, and the Spark model, which is throughput oriented but outperforms other cloud programming solutions adding improved support for iterative algorithms through in-memory computing. The performance of a very well known metaheuristic, the Differential Evolution algorithm, has been thoroughly assessed using a challenging parameter estimation problem from the domain of computational systems biology. The experiments have been carried out both in a local cluster and in the Microsoft Azure public cloud, allowing performance and cost evaluation for both infrastructures.Gobierno de España; DPI2014-55276-C5-2-RFondos Feder; TIN2016-75845-PXunta de Galicia; R2016/045Xunta de Galicia; GRC2013/05

Small static radiosurgery field dosimetry with small volume ionization chambers

Purpose: To evaluate the response of the four smallest active volume thimble type ionization chambers commercially available (IBA-dosimetry RAZOR Nano Chamber, Standard Imaging Exradin A16, IBA-dosimetry CC01 and PTW T31022) when measuring SRS cone collimated Flattening Filter Free (FFF) fields. Methods: We employed Monte Carlo simulation for calculating correction factors as defined in IAEA TRS-483. Monte Carlo simulation beam model and ion chamber geometry definitions were supported by an extensive set of measurements. Type A and B uncertainty components were evaluated.Results: Commissioning of Monte Carlo 6 MV and 10 MV FFF beam models yielded relative differences between measured and simulated dose distributions lower than 1.5%. Monte Carlo simulated output factors for 5 mm SRS field agree with experimental values within 1% local relative difference for all chambers. Smallest active volume ion chamber (IBA-dosimetry RAZOR Nano Chamber) exhibits smallest correction, being compatible with unity. Correction factor combined uncertainties range between 0.7% and 0.9%. Smallest uncertainties were recorded for smallest and largest active volume ion chambers, although the latter exhibited largest correction factor. Highest contribution to combined uncertainty was type B component associated with beam model initial electron spatial Full Width Half Maximum (FWHM) uncertainty. Conclusions: Among the investigated chambers, the IBA RAZOR Nano Chamber was found to be an excellent choice for narrow beam output factor measurement since it requires minimum correction (in line with IAEA TRS-483 recommendations). This is caused by its tiny size and tissue equivalence materials which produce minimum volume averaging and fluence perturbationS

RBD-specific polyclonal F(ab´)2 fragments of equine antibodies in patients with moderate to severe COVID-19 disease: A randomized, multicenter, double-blind, placebo-controlled, adaptive phase 2/3 clinical trial

Background: passive immunotherapy is a therapeutic alternative for patients with COVID-19. Equine polyclonal antibodies (EpAbs) could represent a source of scalable neutralizing antibodies against SARS-CoV-2. Methods: we conducted a double-blind, randomized, placebo-controlled trial to assess efficacy and safety of EpAbs (INM005) in hospitalized adult patients with moderate and severe COVID-19 pneumonia in 19 hospitals of Argentina. Primary endpoint was improvement in at least two categories in WHO ordinal clinical scale at day 28 or hospital discharge (ClinicalTrials.gov number NCT04494984). Findings: between August 1st and October 26th, 2020, a total of 245 patients were enrolled. Enrolled patients were assigned to receive two blinded doses of INM005 (n = 118) or placebo (n = 123). Median age was 54 years old, 65 1% were male and 61% had moderate disease at baseline. Median time from symptoms onset to study treatment was 6 days (interquartile range 5 to 8). No statistically significant difference was noted between study groups on primary endpoint (risk difference [95% IC]: 5 28% [-3 95; 14 50]; p = 0 15). Rate of improvement in at least two categories was statistically significantly higher for INM005 at days 14 and 21 of follow-up. Time to improvement in two ordinal categories or hospital discharge was 14 2 (§ 0 7) days in the INM005 group and 16 3 (§ 0 7) days in the placebo group, hazard ratio 1 31 (95% CI 1 0 to 1 74). Subgroup analyses showed a beneficial effect of INM005 over severe patients and in those with negative baseline antibodies. Overall mortality was 6 9% the INM005 group and 11 4% in the placebo group (risk difference [95% IC]: 0 57 [0 24 to 1 37]). Adverse events of special interest were mild or moderate; no anaphylaxis was reported. Interpretation: Albeit not having reached the primary endpoint, we found clinical improvement of hospitalized patients with SARS-CoV-2 pneumonia, particularly those with severe disease.Fil: Lopardo, Gustavo. Municipalidad de Vicente Lopez (buenos Aires). Hospital Municipal Doctor Bernardo Houssay.; ArgentinaFil: Belloso, Waldo H.. Hospital Italiano; ArgentinaFil: Nannini, Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Colonna, Mariana. Inmunova; ArgentinaFil: Sanguineti, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Inmunova; ArgentinaFil: Zylberman, Vanesa. Inmunova; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Muñoz, Luciana. Inmunova; ArgentinaFil: Dobarro, Martín. Sanatorio Sagrado Corazón; ArgentinaFil: Lebersztein, Gabriel. Sanatorio Sagrado Corazón; ArgentinaFil: Farina, Javier. Gobierno de la Provincia de Buenos Aires. Hospital de Alta Complejidad Cuenca Alta Doctor Nestor Carlos Kirchner.; ArgentinaFil: Vidiella, Gabriela. Sanatorio Agote. Dr. Luis Agote; ArgentinaFil: Bertetti, Anselmo. Sanatorio Guemes Sociedad Anonima.; ArgentinaFil: Crudo, Favio. Universidad Nacional de San Antonio de Areco; ArgentinaFil: Alzogaray, Maria Fernanda. Instituto Medico Platense.; ArgentinaFil: Barcelona, Laura. Municipalidad de Vicente Lopez (buenos Aires). Hospital Municipal Doctor Bernardo Houssay.; ArgentinaFil: Teijeiro, Ricardo. Gobierno de la Ciudad Autónoma de Buenos Aires. Hospital General de Agudos Doctor Ignacio Pirovano; ArgentinaFil: Lambert, Sandra. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic; ArgentinaFil: Scublinsky, Darío. Clinica Zabala.; ArgentinaFil: Iacono, Marisa. Provincia del Neuquen. Hospital Provincial Neuquen "dr. E. Castro Rendon"; ArgentinaFil: Stanek, Vanina. Hospital Italiano; ArgentinaFil: Solari, Rubén. Gobierno de la Ciudad de Buenos Aires. Hospital de Infecciosas "Dr. Francisco Javier Muñiz"; ArgentinaFil: Cruz, Pablo. No especifíca;Fil: Casas, Marcelo Martín. Clinica Adventista Belgrano; ArgentinaFil: Abusamra, Lorena. Hospital Municipal Dr. Diego Thompson; ArgentinaFil: Luciardi, Héctor Lucas. Provincia de Tucuman. Ministerio de Salud. Sistema Provincial de Salud. Hosp. Centro de Salud "zenon Santillan"; ArgentinaFil: Cremona, Alberto. Hospital Italiano de La Plata; ArgentinaFil: Caruso, Diego. Hospital Español; ArgentinaFil: de Miguel, Bernardo. No especifíca;Fil: Perez Lloret, Santiago. Pontificia Universidad Católica Argentina "Santa María de los Buenos Aires"; Argentina. Universidad Abierta Interamericana. Secretaría de Investigación. Centro de Altos Estudios En Ciencias Humanas y de la Salud - Sede Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Millán, Susana. No especifíca;Fil: Kilstein, Yael. No especifíca;Fil: Pereiro, Ana. Fundación Mundo Sano; ArgentinaFil: Sued, Omar. Fundación Huésped; ArgentinaFil: Cahn, Pedro. Fundación Huésped; ArgentinaFil: Spatz, Linus. Inmunova; ArgentinaFil: Goldbaum, Fernando Alberto. Inmunova; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentina. Universidad Nacional de San Martin. Centro de Rediseño E Ingenieria de Proteinas.; Argentin

A cloud-based enhanced differential evolution algorithm for parameter estimation problems in computational systems biology

This is a post-peer-review, pre-copyedit version of an article published in Cluster Computing. The final authenticated version is available online at: https://doi.org/10.1007/s10586-017-0860-1[Abstract] Metaheuristics are gaining increasing recognition in many research areas, computational systems biology among them. Recent advances in metaheuristics can be helpful in locating the vicinity of the global solution in reasonable computation times, with Differential Evolution (DE) being one of the most popular methods. However, for most realistic applications, DE still requires excessive computation times. With the advent of Cloud Computing effortless access to large number of distributed resources has become more feasible, and new distributed frameworks, like Spark, have been developed to deal with large scale computations on commodity clusters and cloud resources. In this paper we propose a parallel implementation of an enhanced DE using Spark. The proposal drastically reduces the execution time, by means of including a selected local search and exploiting the available distributed resources. The performance of the proposal has been thoroughly assessed using challenging parameter estimation problems from the domain of computational systems biology. Two different platforms have been used for the evaluation, a local cluster and the Microsoft Azure public cloud. Additionally, it has been also compared with other parallel approaches, another cloud-based solution (a MapReduce implementation) and a traditional HPC solution (a MPI implementation)Ministerio de Economía y Competitividad; DPI2014-55276-C5-2-RMinisterio de Economía y Competitividad; TIN2013-42148-PMinisterio de Economía y Competitividad; TIN2016-75845-PXunta de Galicia ; R2016/045Xunta de Galicia; GRC2013/05

Global Retinoblastoma Presentation and Analysis by National Income Level.

Importance: Early diagnosis of retinoblastoma, the most common intraocular cancer, can save both a child's life and vision. However, anecdotal evidence suggests that many children across the world are diagnosed late. To our knowledge, the clinical presentation of retinoblastoma has never been assessed on a global scale. Objectives: To report the retinoblastoma stage at diagnosis in patients across the world during a single year, to investigate associations between clinical variables and national income level, and to investigate risk factors for advanced disease at diagnosis. Design, Setting, and Participants: A total of 278 retinoblastoma treatment centers were recruited from June 2017 through December 2018 to participate in a cross-sectional analysis of treatment-naive patients with retinoblastoma who were diagnosed in 2017. Main Outcomes and Measures: Age at presentation, proportion of familial history of retinoblastoma, and tumor stage and metastasis. Results: The cohort included 4351 new patients from 153 countries; the median age at diagnosis was 30.5 (interquartile range, 18.3-45.9) months, and 1976 patients (45.4%) were female. Most patients (n = 3685 [84.7%]) were from low- and middle-income countries (LMICs). Globally, the most common indication for referral was leukocoria (n = 2638 [62.8%]), followed by strabismus (n = 429 [10.2%]) and proptosis (n = 309 [7.4%]). Patients from high-income countries (HICs) were diagnosed at a median age of 14.1 months, with 656 of 666 (98.5%) patients having intraocular retinoblastoma and 2 (0.3%) having metastasis. Patients from low-income countries were diagnosed at a median age of 30.5 months, with 256 of 521 (49.1%) having extraocular retinoblastoma and 94 of 498 (18.9%) having metastasis. Lower national income level was associated with older presentation age, higher proportion of locally advanced disease and distant metastasis, and smaller proportion of familial history of retinoblastoma. Advanced disease at diagnosis was more common in LMICs even after adjusting for age (odds ratio for low-income countries vs upper-middle-income countries and HICs, 17.92 [95% CI, 12.94-24.80], and for lower-middle-income countries vs upper-middle-income countries and HICs, 5.74 [95% CI, 4.30-7.68]). Conclusions and Relevance: This study is estimated to have included more than half of all new retinoblastoma cases worldwide in 2017. Children from LMICs, where the main global retinoblastoma burden lies, presented at an older age with more advanced disease and demonstrated a smaller proportion of familial history of retinoblastoma, likely because many do not reach a childbearing age. Given that retinoblastoma is curable, these data are concerning and mandate intervention at national and international levels. Further studies are needed to investigate factors, other than age at presentation, that may be associated with advanced disease in LMICs

Post-Franco Theatre

In the multiple realms and layers that comprise the contemporary Spanish theatrical landscape, “crisis” would seem to be the word that most often lingers in the air, as though it were a common mantra, ready to roll off the tongue of so many theatre professionals with such enormous ease, and even enthusiasm, that one is prompted to wonder whether it might indeed be a miracle that the contemporary technological revolution – coupled with perpetual quandaries concerning public and private funding for the arts – had not by now brought an end to the evolution of the oldest of live arts, or, at the very least, an end to drama as we know it

The global retinoblastoma outcome study : a prospective, cluster-based analysis of 4064 patients from 149 countries

DATA SHARING : The study data will become available online once all analyses are complete.BACKGROUND : Retinoblastoma is the most common intraocular cancer worldwide. There is some evidence to suggest that major differences exist in treatment outcomes for children with retinoblastoma from different regions, but these differences have not been assessed on a global scale. We aimed to report 3-year outcomes for children with retinoblastoma globally and to investigate factors associated with survival. METHODS : We did a prospective cluster-based analysis of treatment-naive patients with retinoblastoma who were diagnosed between Jan 1, 2017, and Dec 31, 2017, then treated and followed up for 3 years. Patients were recruited from 260 specialised treatment centres worldwide. Data were obtained from participating centres on primary and additional treatments, duration of follow-up, metastasis, eye globe salvage, and survival outcome. We analysed time to death and time to enucleation with Cox regression models. FINDINGS : The cohort included 4064 children from 149 countries. The median age at diagnosis was 23·2 months (IQR 11·0–36·5). Extraocular tumour spread (cT4 of the cTNMH classification) at diagnosis was reported in five (0·8%) of 636 children from high-income countries, 55 (5·4%) of 1027 children from upper-middle-income countries, 342 (19·7%) of 1738 children from lower-middle-income countries, and 196 (42·9%) of 457 children from low-income countries. Enucleation surgery was available for all children and intravenous chemotherapy was available for 4014 (98·8%) of 4064 children. The 3-year survival rate was 99·5% (95% CI 98·8–100·0) for children from high-income countries, 91·2% (89·5–93·0) for children from upper-middle-income countries, 80·3% (78·3–82·3) for children from lower-middle-income countries, and 57·3% (52·1-63·0) for children from low-income countries. On analysis, independent factors for worse survival were residence in low-income countries compared to high-income countries (hazard ratio 16·67; 95% CI 4·76–50·00), cT4 advanced tumour compared to cT1 (8·98; 4·44–18·18), and older age at diagnosis in children up to 3 years (1·38 per year; 1·23–1·56). For children aged 3–7 years, the mortality risk decreased slightly (p=0·0104 for the change in slope). INTERPRETATION : This study, estimated to include approximately half of all new retinoblastoma cases worldwide in 2017, shows profound inequity in survival of children depending on the national income level of their country of residence. In high-income countries, death from retinoblastoma is rare, whereas in low-income countries estimated 3-year survival is just over 50%. Although essential treatments are available in nearly all countries, early diagnosis and treatment in low-income countries are key to improving survival outcomes.The Queen Elizabeth Diamond Jubilee Trust and the Wellcome Trust.https://www.thelancet.com/journals/langlo/homeam2023Paediatrics and Child Healt

High performance techniques applied to geoprocesses

Programa Oficial de Doutoramento en Investigación en Tecnoloxías da Información. 5023V01[Resumo] As aplicacións modernas teñen unhas demandas de potencia de cómputo en constante crecemento. Os recentes avances en hardware necesitan do uso de técnicas procedentes do campo da Computación de Altas Prestacións (High Performance Computing, HPC) para poder sacar proveito ás características e potencia que brindan. Esta tese céntrase na aplicación destas técnicas a varios xeoprocesos co obxectivo de maximizar o rendemento. O primeiro xeoproceso que abordamos nesta tese é a concentración parcelaria a través do intercambio de parcelas. Presentamos un algoritmo xenético capaz de atopar boas solucións ao problema combinatorio do intercambio de parcelas. Esta proposta é capaz de empregar paralelismo de memoria compartida e memoria distribuída para reducir de forma drástica o tempo de execución do algoritmo e aumentar a calidade das solucións ao mesmo tempo, cando se empregan varios computadores para distribuír a carga de traballo. O segundo xeoproceso tratado é a visualización de nubes de puntos masivas de datos LiDAR (Light Detection and Ranging ). O traballo realizado neste campo céntrase nunha estratexia de autoaxuste da estrutura de datos multiresolución empregada para maximizar o rendemento dunha ferramenta web de visualización para nubes de puntos masivas. Esta proposta é capaz de reducir os tempo de carga de comparada coas mellores alternativas actuais, mantendo taxas de refresco altas e baixo consumo de memoria. O último xeoproceso no que se traballa nesta Tese é o filtrado de puntos terreo en nubes de puntos LiDAR masivas a nivel territorial. A escala destes dataset presenta difcultades pola presenza de distintos tipos de entornas que precisan de axustes diferentes para un clasificador ou clasificadores diferentes para obter bos resultados. Esta Tese presenta unha nova estratexia multietapa para o filtrado de puntos terreo, capaz de detectar automaticamente o tipo de entorna presente en cada rexión e aplicar o mellor algoritmo de filtrado para a entorna detectada. Grazas a unha implementación que fai uso de Spark, é capaz de realizar a clasificación máis de 8 veces máis rápido que a versión secuencial cando se fai uso de varios nodos.[Resumo] Las aplicaciones modernas tienen unas demandas de potencia de cómputo en constante aumento. Las recientes avances en hardware necesitan del uso de técnicas procedentes del campo de Computación de Altas Prestaciones (High Performance Computing, HPC) para poder sacar provecho a las características y potencia que brindan. Esta Tesis se centra en la aplicación de estas técnicas a múltiples geoprocesos con el objetivo de maximizar el rendimiento. El primer geoproceso que abordamos en esta Tesis es la concentración parcelaria mediante intercambio de parcelas. Se presenta un algoritmo genético capaz de encontrar buenas soluciones a problema combinatorio que es el intercambio de parcelas. Esta propuesta es capaz de usar paralelismo de memoria compartida y distribuida para reducir drásticamente el tiempo de ejecución del algoritmo y aumentar la cali- dad de las soluciones al mismo tiempo, cuando se emplean varios ordenadores para distribuir la carga de trabajo. El segundo geoproceso tratado es la visualización de nubes de puntos masivas de datos LiDAR (Light Detect on and Rang ng). El trabajo realizado es este campo se centra en una estrategia de autoajuste de la estructura de datos multiresolución usada para maximizar el rendimiento de una herramienta web de visualización para nubes de puntos masivas. Esta propuesta es capaz de reducir los tiempo de carga comparada a las mejores alternativas actuales, manteniendo altas tasas de refresco y bajo consumo de memoria. El último geoproceso en el que se ha trabajado en esta Tesis es el filtrado de puntos terreno en nubes de puntos LiDAR masivas a nivel territorial. La escala de estos dataset presenta desafíos causados por la presencia de distintos tipos de entorno que necesitan ajustes diferentes para un clasificador o clasificadores diferentes para obtener buenos resultados. En esta Tesis se presenta una nueva estrategia multietapa para el filtrado de puntos terreno, capaz de detectar automáticamente el tipo de entorno presente en cada área y aplicar el mejor algoritmo de filtrado para el entorno detectado. Gracias a una implementación usando Spark, es capaz de realizar la clasificación más de 8 veces más rápido que la versión secuencial cuando se emplean varios nodos.[Abstract] Modern applications dernand cornputational power in ever increasing arnounts. Current hardware advancernents require of techniques frorn the dornain of High Per- forrnance Cornputing (HPC) in order to leverage all of the features and perforrnance they provide. This Thesis focuses on the application of these techniques to rnultiple geoprocesses with the goal of rnaxirnizing perforrnance. The first geoprocess covered in this Thesis is land consolidation through par- cel exchange. Ve present a genetic algorithrn that can find good solutions to the cornbinatorial problern of parcel exchange. This proposal is capable of using shared and distributed rnernory parallelisrn to drastically reduce the execution tirne of the algorithrn and increase the quality of the solutions found at the sarne tirne, when using rnultiple cornputers to distribute the workload. The second geoprocess covered is rnassive LiDAR (Light Detection and Ranging) point clouds visualization. The work on this topic focuses on an autotuning strat- egy to adjust the rnultiresolution data structure used to rnaxirnize the perforrnance of a web-based visualization tool for rnassive point clouds. This strategy provides reduced loading tirnes when cornpared against the best current alternatives, rnain- taining high frarne rates and low rnernory consurnption. The last geoprocess worked on is ground point filtering on territory-level rnassive LiDAR point clouds. The scale of these dataset present challenges due to the presence of different environrnents with different needs for correct classification. In this Thesis a nev rnultistage strategy for ground filtering is presented, capable of autornatically identifying different types of environrnents and applying the best classifier in each one. It is capable of rnatching the best classifiers in each of the environrnents and, thanks to an irnplernentation using Spark, is capable of perforrning the classifi- cation several tirnes faster than the sequential version vhen using rnultiple cornpute nodes.Xunta de Galicia; ED431C2017/04Xunta de Galicia; ED431C 2021/30Xunta de Galicia; ED481A-2019/231Xunta de Galicia; ED431G 2019/0