Debt compensation is a process via which various entities (companies, people, etc.) propose the payment of their mutual debts by compensating them according to the credits they may have in their favor. The aim is to establish the financial information of a company so that its debts can somehow be satisfied by its credits, breaking the vicious cycle of debts that cannot be paid. To this end, this financial information is retrieved from the business information system of a company, encompassing how much is owed and to which companies. From the retrieved information, this paper describes the implementation of an automatic system that allows calculating not only the debt cycles involving different companies, but also how these cycles can be effectively solved using blockchain technology thanks to the use of smart contracts. Blockchain technology can have a great impact for companies in this kind of application, guaranteeing the security and traceability of transactions among companies, which are two well-appreciated features from a financial auditing point of view.

The standard method used in the Weather Research and Forecasting (WRF) model for distributing MPI processes across the processors is not always optimal. This circumstance affects performance, i.e., execution times, but also energy consumption, especially if the application is to be extended to exascale. The authors found that the reason why the standard method for process distribution is not always optimal was an imbalance between the orthogonality of the communication and the proper cache usage, and this affects energy consumption. We present an improved MPI process distribution algorithm that increases the performance. Furthermore, scalability analyses for the new algorithm are presented and the energy use of the system is evaluated. A solution for balancing energy use with performance is also proposed for cases where the former is a concern.

Weather forecasting, especially snowfall prediction, was critical in the 2018 Winter Olympics, where the accuracy of the predictions was of key importance for the planning of the different Olympic events. It was a significant challenge for the authors to meet the requirements in time and forecast resolution, while doing their best to be as competitive as possible. All the forecasts were obtained using the Weather Research and Forecasting (WRF) model, executed on the GALGO supercomputer. In order to obtain the best performance and meet the required execution times, different combinations of compilers, Message Passing Interface (MPI) libraries and computing platforms were tested to seek the best combinations. This work proposes an empirical score of special interest to supercomputer maintainers, developers and scientists, which can be useful to obtain the best WRF configuration for their systems. Additionally, we found substantial performance differences when using different combinations of compilers, MPI libraries and hybrid shared memory paradigms, although these differences varied depending on the underlying platform. As conclusion, after all the tests we performed, we chose the combination with Intel compilers, Intel MPI library and OpenMP for the production system tasked to perform the weather forecasts for the Winter Olympic Games.

While it has been proved that multiple scattering in the microwave frequencies has to be accounted for in precipitation retrieval algorithms, the effects of the random arrangements of drops in space has seldom been investigated. The fact is, a single rain drop size distribution (RDSD) corresponds with many actual 3D distributions of those rain drops and each of those may a priori absorb and scatter radiation in a different way. Each spatial configuration is equivalent to any other in terms of the RDSD function, but not in terms of radiometric characteristics, both near and far from field, because of changes in the relative phases among the particles. Here, using the T-matrix formalism, we investigate the radiometric variability of two ensembles of 50 different 3D, stochastically-derived configurations from two consecutive measured RDSDs with 30 and 31 drops, respectively. The results show that the random distribution of drops in space has a measurable but apparently small effect in the scattering calculations with the exception of the asymmetry factor.

The authors have been aimed by the goal of reducing the computational cost of searching the Labeled Transition Systems, LTSs, generated by Process Algebras. In particular, we have been following the idea of moving the order of the computational cost required to find/reach a desired node/state from the exponential of the classical Breadth-First Search fashion to the polynomial produced either by the Depth-First Search or by the A* algorithm [6] . As usual, they both take as size of the problem the branching factor of the LTS. This paper, first, presents the Normal Formed ROSA processes required to, second, define a sound topological structure over this Process Algebra. The underlying notion of distance from this topology can be taken as the heuristics to guide the search for whatever node which reachability want to be studied, by means of an A* algorithm.

HidroMORE software was developed in the Remote Sensing and Geographic Information Systems (GIS) section from the University of Castilla-la Mancha to extend the Evapotranspiration assessment to a regional scale, implementing the FAO-56 methodology and the assimilation of the basal crop coefficient from Normalized Difference Vegetation Index (NDVI) images calculated from satellite images. However, when this software deals with high dimension images, the performance greatly decays. Currently, HidroMORE is being required for carring out calculations that result unapproachable in its current state. In this work HidroMORE 2 is presented where a High Performance Computing approach has been considered to manage the complexity of HidroMORE software. The work presented here takes into account two main aspects in order to improve the performance: improvements on input/output operations, that is, a better manage of hard disk operations; and on the other hand the use of Parallel Computing by exploiting current computer architectures, in particular, multicore architectures.

Graphics processing units have definitely consolidated a position of privilege in the acceleration of general purpose applications. Search algorithms in large databases are a clear example of applications that benefit from computing platforms based on these devices. To obtain an efficient implementation of a given code using these platforms, it is very important to take into account their features. However, the characteristics of the application and certain overheads still introduced by these platforms, make it not always advisable to use these devices to obtain significant time reductions. In this paper, we show how different properties of current graphics processing units are exploited for improving a version of the general metric structure similarity search algorithm introduced by the authors, and compare it with a multithreaded version of the same algorithm using conventional processors. The analysis of the results provides us relevant data to determine the most appropriate computing platform.

The recent appearance, evolution and massive expansion of social media-based technologies, in conjunction with what currently is known as Internet of Things, results in a vertiginous data production. One of the main contributions to address this matter has been the Hadoop framework (which implements the Map/Reduce paradigm), especially when used in conjunction with Cloud computing environments. In this paper, a comprehensive and rigourous study of the Map/Reduce framework using formal methods is presented. Specifically, the Timed Process Algebra BTC is used, and the resulting formal model is evaluated with a real social media data Hadoop-based application. Moreover, the formal model is validated by carrying out several experiments on a real private Cloud environment. Finally, the formal model outcomes are harnessed to determine the best performance–cost agreement in a real scenario. Results show that the proposed model enables to determine in advance both the performance of a Hadoop-based application within Cloud environments and the best performance–cost agreement.

Nowadays, the world around us is built up on more and more unstructured data every day. However, performing a longitudinal analysis of these data becomes a Big-Data problem that cannot be tackled with traditional tools, storage or processing infrastructures. One of the main contributions to address this matter has been the Hadoop framework (which implements the Map/Reduce paradigm), especially when used in conjunction with Cloud computing environments. This paper presents a formalization of the Map/Reduce paradigm which is used to evaluate performance parameters and make a trade-off analysis of the number of workers versus processing time and resource cost. We have used Prioritised -- Timed Coloured Petri Nets to obtain complete and unambiguous models of the system behaviour as well as CPNTools to evaluate the correctness of the system using state space exploration and for performance evaluation. The resulting formal model is evaluated with a real social media data Hadoop-based application and it is validated by carrying out experiments on a real private Cloud environment. Results show that the proposed model enables to determine in advance both the performance of a Map/Reduce-based application within Cloud environments and the best performance-cost agreement.

Nowadays, similarity search on metric spaces is becoming a research field of interest due to the fact of its application to different scientific areas. However, when these applications produce a huge amount of data, it is necessary to accelerate the searching process by means of parallel architectures such as clusters, multiprocessors (multicores) or clusters of multiprocessors (multicores). Currently, graphic accelerators have emerged as a technology that allows for good performance at a low cost. In order to exploit the underlined architecture formed by multicores and a graphic accelerator it is nedeed to carry out heterogeneous programming, where CPUs and GPU are working at the same time taking benefits of the complete architecture. In this paper, a suitable generic structure adapted to the multicore/GPU system is presented and the experimental results, obtained in terms of execution time and speed-up, show the advantages of using this heterogeneous system, as well as a study of power consumption.

HPG Aligner applies suffix arrays for DNA read mapping. This implementation produces a highly sensitive and extremely fast mapping of DNA reads that scales up almost linearly with read length. The approach presented here is faster (over 20× for long reads) and more sensitive (over 98% in a wide range of read lengths) than the current state-of-the-art mappers. HPG Aligner is not only an optimal alternative for current sequencers but also the only solution available to cope with longer reads and growing throughputs produced by forthcoming sequencing technologies. Availability and implementation: https://github.com/opencb/hpg-aligner. Contact: jdopazo@cipf.es or imedina@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Medium and large clusters incorporating hybrid CPU/graphics processing unit (GPU) nodes are present in many datacenters today. They can accelerate many different kinds of applications and appropriately manage applications dealing with a high volume of data. This is the case of the similarity problem because large databases are managed and very quick responses are required to hundreds or thousands of queries per second. However, the design and usage of heterogeneous computing platforms poses big challenges as system size, energy saving, task mapping, scheduling, among others, must be efficiently handled. In this paper we focus on the scheduling issue for distributing the incoming queries to all the processing components in the cluster nodes. Our algorithms exploit the computational resources, simultaneously processing queries on CPU cores and on the GPUs. Thus, we address the problem of how to distribute the queries over the whole system in order to obtain the best performance, under the assumption of defining a heuristic that automatically provides the best distribution. Experimental results show the benefits in terms of execution time and energy saving of using an appropriate scheduling scheme.

En la actualidad, la búsqueda por similitud en espacios métricos representa una línea de investigación de interés debido a sus múltiples campos de aplicación.Sin embargo, cuando en dichas aplicaciones aparecen grandes volúmenes de datos, se hace más que necesario el poder acelerar la búsqueda de las diferentes consultas en tales cantidades de datos.Una manera de llevar a cabo dicha aceleración pasa por el uso de clusters, multiprocesadores o clusters de multiprocesadores.En la actualidad, ha surgido con fuerza la posibilidad de utilizar aceleradores gráficos (GPU) como vehículo para acelerar aplicaciones a un muy bajo coste.En estos casos, la relación CPU/GPU no es de par a par y por tanto se denominan sistemas heterogéneos.Para explotar dichos sistemas heterogéneos se requiere una programación también heterogénea que emplee a la vez la CPU y la GPU.En este trabajo se realiza una verdadera programación heterogénea en el que tanto CPU como GPU están trabajando en forma simultánea y por tanto se aprovecha al máximo la arquitectura subyacente.Se presenta la implementación de una estructura genérica adaptada para un sistema multiprocesador con una GPU, mostrando los resultados experimentales en términos de tiempo y speed-up.Se muestra experimentalmente las ventajas comparativas al insertar GPU a una plataforma multicore, así como el análisis del consumo energético.

Similarity search is becoming a field of interest because it can be applied to different areas in science and engineering. In real applications, when large volumes of data are processing, query response time can be quite high. In this case, it is necessary to apply mechanisms to significantly reduce the average query response time. For that purpose, modern GPU/Multi-GPU systems offer a very impressive cost/performance ratio. In this paper, the authors make a comparative study of the most popular pivot selection methods in order to stablish a set of attractive features from the point of view of future GPU implementations.

This paper presents a performance analysis tool (BAL) for concurrent systems that involves resources and synchronization. It is based on timed process algebra (BTC) for specifying process systems and their resource requirements. The output of the tool is the minimum time and resources needed to reach a given state. BAL makes the syntactic analysis of the system specification, draws up its relevant transition graph by applying the rules of the operational semantics and solves a performance optimization problem relevant to the minimization of the maximum completion time. A case study is used to assess how BAL works, the degree of usefulness of the results and the time required to obtain them.

Nowadays, similarity search is becoming a field of increasing interest because these kinds of methods can be applied to different areas in science and engineering, for instance, pattern recognition, information retrieval, etc. This search is carried out over metric indexes decreasing the number of distance evaluations during the search process, improving the efficiency of this process. However, for real applications, when processing large volumes of data, query response time can be quite high. In this case, it is necessary to apply mechanisms in order to significantly reduce the average query response time. In this sense, the parallelization of the metric structures processing is an interesting field of research. Modern GPU/Multi-GPU systems offer a very impressive cost/performance ratio. In this paper, we show a simple and fast implementation of similarity search method on a Multi-GPU platform. The main contributions are mainly the definition of a generic metric structure more suitable for GPU platforms, the efficient usage of GPU memory system and the implementation of the method in a Multi-GPU platform.

Nowadays, similarity search is becoming a field of increasing interest because these kinds of methods can be applied to different areas in science and engineering, for instance, pattern recognition, information retrieval, etc. This search is carried out over metric indexes decreasing the number of distance evaluations during the search process, improving the efficiency of this process. However, for real applications, when processing large volumes of data, query response time can be quite high. In this case, it is necessary to apply mechanisms in order to significantly reduce the average query response time. In this sense, the parallelization of the metric structures processing is an interesting field of research. For that purpose, modern GPU/Multi-GPU systems offer a very impressive cost/performance ratio. In this paper, the authors make a comparative study of the most popular metric structures and pivot selection methods in order to stablish a set of attractive features from the point of view of future GPU implementations. Therefore, this work represents a state-ofthe-art paper setting up the starting point for future parallel implementations of similarity search techniques.

The analysis of medical image, in particular Magnetic Resonance Imaging (MRI), is a very useful tool to help the neurologists on the diagnosis. One of the stages on the analysis of MRI is given by a classification based on the Markov Random Fields (MRF) method. It is possible to find in the literature several packages to carry out this analysis, and of course, the classification tasks. One of them is the Insight Segmentation and Registration Toolkit (ITK). The analysis of MRI is an expensive computational task. In order to reduce the execution time spent on the analysis of MRI, parallelism techniques can be used. Currently, Graphics Processing Units (GPUs) are becoming a good choice to reduce the execution time of several applications at a low cost. In this paper, the authors present a GPU-based classification using MRF from the sequential implementation that appears in the ITK package. The experimental results show a spectacular execution time reduction being the GPU-based implementation up to 118 times faster than the sequential implementation included in the ITK package. Moreover, this result is also observed by reducing the total power consumption in a significant amount.