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Intertextuality is paramount in literary translation. This article studies the biblical composite permeating the second scene of the third act of Shakespeare's Richard II. It focuses on how Shakespeare's use of the scriptural concepts of kingship is transposed into Spanish by the most widely read translators of these plays. Special attention is paid to the way Shakespeare represents kingship in Richard II by quoting from the Bible. The methodology employed in this intertextual comparison involves a comparative analysis of the transposition component in both the source and the receiving intertext (the Spanish version of the text). The conclusions aim to encourage greater attention to be paid to intertextuality in the practice of translation, particularly after comparing the strengths and weaknesses of the five Spanish translations compared in this article.

In this work we present a scalable parallelization with PyCOMPSs (Tejedor et al., 2017; the Python binding of COMPSs) of the Python-based workflow BackTrackBB (Poiata et al., 2016) for the automatic detection and location of seismic sources using continuous waveform data recorded by regular to large seismic networks. PyCOMPSs is a task-based programming model for Python applications that relies in a powerful runtime able to extract dynamically the parallelism among tasks and execute them in distributed environments (e.g., HPC Clusters, Cloud infrastructures, etc.) transparently to the users. BackTrackBB with PyCOMPSs implementation allows to fully parallelize the seismic source detection and location process making it efficient and portable in terms of the use of available HPC resources. We provide details of the BackTrackBB workflow implementation with PyCOMPSs and discuss its performance by presenting the results of the scalability tests and memory usage analysis. All the tests have been performed on the MareNostrum4 High-Performance computer of the Barcelona Supercomputing Centre. The first version of the BackTrackBB with PyCOMPSs workflow was developed in the context of the European Centre Of Excellence (CoE) ChEESE for Exascale computing in solid earth sciences. The initial workflow developments and performance tests made use of a simplified synthetic dataset emulating a large-scale seismic network deployment in a seismically active area and corresponding to 100 vertical sensors recording a month of continuous waveforms at a sampling rate of 100 sps. In the following testing step, the workflow was applied to the real-case two-month long dataset from Vrancea seismic region in Romania (corresponding to the 60-190 km deep earthquakes activity). Real seismic data scenario proved to present some challenges in terms of the data-quality control, that often occurs in the case of continuous waveforms recorded by the seismic observatories. This issue have been resolved and corresponding modifications were included in the final version of BackTrackBB with PyCOMPSs. The real dataset tests showed that the workflow allows improved detection and location of seismic events through the efficient processing of the large continuous seismic data with important performance and scalability improvements. We show that BackTrackBB with PyCOMPSs workflow enables generation of fully reproducible, seismic catalogues (or seismic catalogues realizations) through the analysis of the continuous large (in terms of the number of seismic stations, data record length and covered area) seismic data-sets. Such implementations making use of advances full-waveform detection and location methods are currently highly-challenging or, some-times, impossible due to the amount of required main memory or unfeasible time to solution. PyCOMPSs has demonstrated to be able to deal with both issues successfully allowing to explore in greater depth the usage with BackTrackBB method. Workflows such as BackTrackBB with PyCOMPSs has the ability to significantly improve the detections and location process that is currently in place at seismological observatories or network operation centres, providing fully reproducing detailed catalogues in the seismically-active regions and allowing multiple input parameters testing (e.g., station configuration, velocity models).

espanolEn este articulo, se revisa y examina la poesia de Roy Campbell inspirada en la Guerra Civil espanola: Flowering Rifle, Talking Bronco and“A Letter from theSan Mateo Front”. Los estudios llevados a cabo por Esteban Pujals (1959), Stephen Spender (1980) y Bernd Dietz (1985) reflejan la escasez investigadora que existe sobre los poemas belicos de Campbell. Este trabajo ofrece un entendimiento mas certero y fiel de las imagenes belicas y las referencias literarias de este conflicto, empleadas por Campbell, teniendo en cuenta la perspectiva ideologica del poeta. Campbell presenta un pean al liderazgo ‘Nacional’ y esta representacion exacerbada se opone al nivel de degradacion que sufren quiene estan a favor de la Republica. Algunas de las conclusiones que se extraen son que la mayor parte de sus versos se transforman en un panfleto cuasi-maniqueo semejante a una dramatizacion de tipo doctrinal o a una alegoria medieval. EnglishThis article revisits and re-examines Roy Campbell’s poems inspired by the Spanish Civil War: Flowering Rifle,Talking Bronco and“A Letter from the San Mateo Front”. The studies carried out by Esteban Pujals (1959), Stephen Spender (1980) and Bernd Dietz (1985) reflect thescarcityofresearch about Campbell’s warlike poems. This article aims to develop a better understanding of Campbell’s war images and literary references to the Spanish conflict, by analysing them in the light of the poet’s own political ideology. Campbell presents a paean to the ‘Nationalist’ leadership and this exaggerated idealising of the rebels and their deeds contrasts with the way he denigrates those in favour of the Republic. The article concludes that this exaggerated feat transforms most of these poetic works into quasi-Manichaean pamphlets resembling more a morality play than a work of modern literature.

With the advent of more powerful Quantum Computers, the need for larger Quantum Simulations has boosted. As the amount of resources grows exponentially with size of the target system Tensor Networks emerge as an optimal framework with which we represent Quantum States in tensor factorizations. As the extent of a tensor network increases, so does the size of intermediate tensors requiring HPC tools for their manipulation. Simulations of medium-sized circuits cannot fit on local memory, and solutions for distributed contraction of tensors are scarce. In this work we present RosneT, a library for distributed, out-of-core block tensor algebra. We use the PyCOMPSs programming model to transform tensor operations into a collection of tasks handled by the COMPSs runtime, targeting executions in existing and upcoming Exascale supercomputers. We report results validating our approach showing good scalability in simulations of Quantum circuits of up to 53 qubits., 8 pages, 13 figures

Although there appears to be no direct evidence that Shakespeare had access to the relectiones taught in the School of Salamanca during the sixteenth century, this study demonstrates that, forty years after their dissemination, the theories of Francisco Vitoria and his disciples were probably in circulation throughout England. The methodology in this article juxtaposes Shakespeare’s Richard II with one of Vitoria’s relectiones. This relectio modified the medieval idea of the divine origin of kingship, and generated a discussion about the origin of royal power which is central to the plot of Shakespeare’s play.

In the recent years, the improvement of software and hardware performance has made biomolecular simulations a mature tool for the study of biological processes. Simulation length and the size and complexity of the analyzed systems make simulations both complementary and compatible with other bioinformatics disciplines. However, the characteristics of the software packages used for simulation have prevented the adoption of the technologies accepted in other bioinformatics fields like automated deployment systems, workflow orchestration, or the use of software containers. We present here a comprehensive exercise to bring biomolecular simulations to the “bioinformatics way of working”. The exercise has led to the development of the BioExcel Building Blocks (BioBB) library. BioBB’s are built as Python wrappers to provide an interoperable architecture. BioBB’s have been integrated in a chain of usual software management tools to generate data ontologies, documentation, installation packages, software containers and ways of integration with workflow managers, that make them usable in most computational environments. We thank The Barcelona Supercomputing Center for providing all the HPC resources to launch the test executions presented in this manuscript. Te project was supported by the following grants: BioExcel Center of Excellence (Horizon 2020 Framework Programme. Grants 675728, and 823830). Elixir-Excelerate (Horizon 2020 Framework Programme. Grant 676559). Spanish National Institute for Bioinformatics (Institute of Health Carlos III. Grants PT13/0001/0019, PT13/0001/0028, PT17/0009/0001). Spanish Government, Severo Ochoa Grant SEV2015-0493, TIN2015-65316-P, Generalitat de Catalunya. 2014-SGR-1051, 2017-SGR-1110.

The usage of workflows has led to progress in many fields of science, where the need to process large amounts of data is coupled with difficulty in accessing and efficiently using High Performance Computing platforms. On the one hand, scientists are focused on their problem and concerned with how to process their data. On top of that, the applications typically have different parts and use different tools for each part, thus complicating the distribution and the reproducibility of the simulations. On the other hand, computer scientists concentrate on how to develop frameworks for the deployment of workflows on HPC or HTC resources; often providing separate solutions for the computational aspects and the data analytic ones.In this paper we present an approach to support biomolecular researchers in the development of complex workflows that i) allow them to compose pipelines of individual simulations built from different tools and interconnected by data dependencies, ii) run them seamlessly on different computational platforms, and iii) scale them up to the large number of cores provided by modern supercomputing infrastructures. Our approach is based on the orchestration of computational building blocks for Molecular Dynamics simulations through an efficient workflow management system that has already been adopted in many scientific fields to run applications on multitudes of computing backends.Results demonstrate the validity of the proposed solution through the execution of massively parallel runs in a supercomputer facility.

Modern digital seismic networks record a wealth of high-quality continuous waveforms that contain a variety of signals associated to a wide range of seismic sources (e.g., earthquakes, volcanic, tectonic tremors, environmental sources) that probe transient energy release processes. Efficient and automatic detection, location and characterization of these different seismic sources is critical to understand slowly-driven evolution of active tectonic and volcanic systems toward catastrophic events. Developing a common analysis framework for systematic exploration of the increasing wealth of seismic observation streams is important for improving seismic monitoring systems and extracting large and accurately resolved seismic source catalogues.To this end, we present a scalable parallelization with PyCOMPSs (Tejedor et al., 2017) of the python-based BackTrackBB data-streaming workflow (Poiata et al., 2016; 2018) for automatic detection and location of seismic sources from continuous waveform streams recorded by large seismic networks. This allows achieving an efficient distribution and orchestration of BackTrackBB code on different architectures. PyCOMPSs is a task-based programming model for python applications that relies in a powerful runtime able to extract dynamically the parallelism among tasks and executing them in distributed environments (e.g. HPC Clusters, Cloud infrastructures, etc.) transparently to the users.We will provide details of the PyCOMPSs-based BackTrackBB workflow implementation. Results of scalability tests and memory usage analysis will be also discussed. Tests have been performed, in the context of the European Centre Of Excellence (CoE) ChEESE for Exascale computing in solid earth sciences, on the MareNostrum4 High-Performance computer of the Barcelona Supercomputing Centre, using large-scale datasets of synthetic and real-case seismological continuous waveform data sets.

Task-based programming has proven to be a suitable model for high-performance computing (HPC) applications. Different implementations have been good demonstrators of this fact and have promoted the acceptance of task-based programming in the OpenMP standard. Furthermore, in recent years, Apache Spark has gained wide popularity in business and research environments as a programming model for addressing emerging big data problems. COMP Superscalar (COMPSs) is a task-based environment that tackles distributed computing (including Clouds) and is a good alternative for a task-based programming model for big data applications. This article describes why we consider that task-based programming models are a good approach for big data applications. The article includes a comparison of Spark and COMPSs in terms of architecture, programming model, and performance. It focuses on the differences that both frameworks have in structural terms, on their programmability interface, and in terms of their efficiency by means of three widely known benchmarking kernels: Wordcount, Kmeans, and Terasort. These kernels enable the evaluation of the more important functionalities of both programming models and analyze different work flows and conditions. The main results achieved from this comparison are (1) COMPSs is able to extract the inherent parallelism from the user code with minimal coding effort as opposed to Spark, which requires the existing algorithms to be adapted and rewritten by explicitly using their predefined functions, (2) it is an improvement in terms of performance when compared with Spark, and (3) COMPSs has shown to scale better than Spark in most cases. Finally, we discuss the advantages and disadvantages of both frameworks, highlighting the differences that make them unique, thereby helping to choose the right framework for each particular objective., This work is supported by the Spanish Government (SEV2015-0493), by the Spanish Ministry of Science and Innovation (contract TIN2015-65316-P), and by Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272). Javier Conejero's postdoctoral contract is cofinanced by the Ministry of Economy and Competitiveness under the Juan de la Cierva Formación postdoctoral fellowship number FJCI-2015-24651. This work is also supported by the Intel-BSC Exascale Lab. The Human Brain Project receives funding from the EU's Seventh Framework Programme (FP7/ 2007-2013) under grant agreement no. 604102.

Progress in science is deeply bound to the effective use of high-performance computing infrastructures and to the efficient extraction of knowledge from vast amounts of data. Such data comes from different sources that follow a cycle composed of pre-processing steps for data curation and preparation for subsequent computing steps, and later analysis and analytics steps applied to the results. However, scientific workflows are currently fragmented in multiple components, with different processes for computing and data management, and with gaps in the viewpoints of the user profiles involved. Our vision is that future workflow environments and tools for the development of scientific workflows should follow a holistic approach, where both data and computing are integrated in a single flow built on simple, high-level interfaces. The topics of research that we propose involve novel ways to express the workflows that integrate the different data and compute processes, dynamic runtimes to support the execution of the workflows in complex and heterogeneous computing infrastructures in an efficient way, both in terms of performance and energy. These infrastructures include highly distributed resources, from sensors and instruments, and devices in the edge, to High-Performance Computing and Cloud computing resources. This paper presents our vision to develop these workflow environments and also the steps we are currently following to achieve it., Comment: 10 pages, 6 figures, in proceedings of 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

Current scientific workflows are large and complex. They normally perform thousands of simulations whose results combined with searching and data analytics algorithms, in order to infer new knowledge, generate a very large amount of data. To this end, workflows comprise many tasks and some of them may fail. Most of the work done about failure management in workflow managers and runtimes focuses on recovering from failures caused by resources (retrying or resubmitting the failed computation in other resources, etc.) However, some of these failures can be caused by the application itself (corrupted data, algorithms which are not converging for certain conditions, etc.), and these fault tolerance mechanisms are not sufficient to perform a successful workflow execution. In these cases, developers have to add some code in their applications to prevent and manage the possible failures. In this paper, we propose a simple interface and a set of transparent runtime mechanisms to simplify how scientists deal with application-based failures in task-based parallel workflows. We have validated our proposal with use-cases from e-science and machine learning to show the benefits of the proposed interface and mechanisms in terms of programming productivity and performance. This work has been supported by the Spanish Government (contracts SEV2015-0493 and TIN2015-65316-P), by the Generalitat de Catalunya (contract 2014-SGR-1051), and by the European Commission’s Horizon 2020 Framework program through BioExcel Center of Excellence (contracts 823830, and 675728). The research leading to these results has received funding from the collaboration between Fujitsu and BSC (Script Language Platform).

Multiscale Genomics (MuG) Virtual Research Environment (MuGVRE) is a cloud-based computational infrastructure created to support the deployment of software tools addressing the various levels of analysis in 3D/4D genomics. Integrated tools tackle needs ranging from high computationally demanding applications (e.g. molecular dynamics simulations) to high-throughput data analysis applications (like the processing of next generation sequencing). The MuG Infrastructure is based on openNebula cloud systems implemented at the Institute for research in Biomedicine, and the Barcelona Supercomputing Center, and has specific interfaces for users and developers. Interoperability of the tools included in MuGVRE is maintained through a rich set of metadata allowing the system to associate tools and data in a transparent manner. Execution scheduling is based in a traditional queueing system to handle demand peaks in applications of fixed needs, and an elastic and multi-scale programming model (pyCOMPSs, controlled by the PMES scheduler), for complex workflows requiring distributed or multi-scale executions schemes. MuGVRE is available athttps://vre.multiscalegenomics.euand documentation and general information athttps://www.multiscalegenomics.eu. The infrastructure is open and freely accessible.

Como es sabido, a pesar de su complejidad textual, las obras de William Shakespeare han generado un número considerable de traducciones a las principales lenguas del mundo. En este sentido, el presente estudio tiene un doble propósito: en primer lugar, trata de identificar y analizar las ambigüedades de algunos términos usados por Shakespeare en Hamlet 1.3.88-136 y, a continuación, realiza un cotejo de la traducción alemana de August Wilhelm Schlegel con el texto original, a fin de comprobar en qué medida se recrean los diversos sentidos y los múltiples significados de estos términos en la versión alemana. As is well known, in spite of their textual complexity, the works of William Shakespeare have given rise to a considerable number of translations into the major languages of the world. The aim of this article is twofold: firstly, to identify and analyze the ambiguities of some terms used by Shakespeare in Hamlet 1.3.88-136. Then, to contrast the German translation by August Wilhelm Schlegel with the original text, in order to point out to what extent the various senses and the multiple meanings of these terms have been preserved in this German version.

Energy efficiency is often identified as one of the key reasons for migrating to Cloud environments. It is stated that a data center hosting the Cloud environment is likely to achieve greater energy efficiency (at a reduced cost) compared to a local deployment. With increasing energy prices, it is also estimated that a large percentage of operational costs within a Cloud environment can be attributed to energy. In this work, we investigate and measure energy consumption of a number of virtual machines running the Hadoop system, over an OpenNebula Cloud. Our workload is based on sentiment analysis undertaken over Twitter messages. Our objective is to understand the tradeoff between energy efficiency and performance for such a workload. From our results we generalize and speculate on how such an analysis could be used as a basis to establish a Service Level Agreement (SLA) with a Cloud provider-especially where there is likely to be a high level of variability (both in performance and energy use) over multiple runs of the same application (at different times). Among the service level objectives that might be included in a SLA, Quality of Service (QoS) related metrics (i.e., latency) are one of the most challenging to support. This work provides some insight on the relationship between power consumption and QoS related metrics, describing how a combined consideration of these two metrics could be supported for a particular workload. Power consumption characterization of Hadoop Clouds (with a social media use case).Study of the QoS related to power consumption (in terms of processing time).Experimentation on two different Cloud infrastructures (single node-multi node).OpenNebula based private Cloud environments.

This article proves statistically the effectiveness of humour in the teaching of English and German to Spanish students of foreign languages. The referred lexicon is particularly troublemaking because of the difficulties that such vocabulary poses for Spanish learners of not-Latin-originated languages. The approach followed is a quantitative and contrastive analysis and was carried out with four groups of Spanish learners. The experimental groups (one for each language) were presented with the input they were supposed to learn in sentences and contexts in which the use of the English/German term, with the meaning of its Spanish ‘false friend’, resulted in nonsensical and highly humorous utterances. The presentation of the same lexical items to the control groups was more neutral and deliberately bereft of humour. After that, the participants were set a test to find out whether or not they remembered the proper sense of the lexicon taught and could also use it correctly. The results are conclusive and the outcome strongly supports the beneficial effects of pedagogical humour in the language classroom. Keywords: Humour, English as a foreign language, German as a foreign language, L2 resources, motivation, vocabulary development.

Task-based programming is becoming a tool of large interest for boosting High-Performance Computing (HPC) and Big Data applications. In particular, COMP Superscalar (COMPSs), is showing to be an effective task-based programming model for distributed computing of Big Data applications within HPC environments. Applications like NMMB-MONARCH, which is a dust forecast application composed by a set of steps (being some of them binaries with or without MPI), are perfect candidates for PyCOMPSs, the Python binding of COMPSs. This paper describes the success story of the adaptation of the NMMB-MONARCH online multi-scale atmospheric dust model to PyCOMPSs in order to exploit its inherent parallelism with the minimal developer effort. The paper also includes an evaluation of this implementation in the Nord3 supercomputer, a scalability analysis and an in-depth behaviour study. The main results presented in this paper are: (1) PyCOMPSs is able to extract the parallelism from the NMMB-MONARCH application; (2) it is able to improve the dust forecasting in terms of performance when compared with previous versions, and (3) PyCOMPSs is able to interact and share the resources with MPI applications when included in the workflow as tasks. Finally, we present the keys for exporting the knowledge of this experience to other applications in order to benefit from using PyCOMPSs.

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.

The exploitation of service oriented technologies, such as Grid computing, is being boosted by the current service oriented economy trend, leading to a growing need of Quality of Service (QoS) mechanisms. However, Grid computing was created to provide vast amounts of computational power but in a best effort way. Providing QoS guarantees is therefore a very difficult and complex task due to the distributed and heterogeneous nature of their resources, specially the volunteer computing resources (e.g., desktop resources). The scope of this paper is to empower an integrated multi QoS support suitable for Grid Computing environments made of either dedicated and volunteer resources, even taking advantage of that fact. The QoS is provided through SLAs by exploiting different available scheduling mechanisms in a coordinated way, and applying appropriate resource usage optimization techniques. It is based on the differentiated use of reservations and scheduling in advance techniques, enhanced with the integration of rescheduling techniques that improve the allocation decisions already made, achieving a higher resource utilization and still ensuring the agreed QoS. As a result, our proposal enhances best-effort Grid environments by providing QoS aware scheduling capabilities. This proposal has been validated by means of a set of experiments performed in a real Grid testbed. Results show how the proposed framework effectively harnesses the specific capabilities of the underlying resources to provide every user with the desired QoS level, while, at the same time, optimizing the resources' usage. (C) 2013 Elsevier B.V. All rights reserved.

The purpose of this essay is to examine and identify the functions of the East Midlands dialect in 5011s and Lovers in order to explore the possibilities of its translation into Spanish. The fact that this regional variety of English also expresses a deep conflict of the working and the u pper classes in this novel, makes it possible for the linguistic contrast, if not conflict, between Gertrude and Walter Morel to become patent in the target text, too. lndeed, by replacing the East Midlands dialect with a nonstandard variety of Spanish, not identified with the world of crime and marginalisation, one may find a way to partially translate a local dialect that, per se, is always an untranslatable one.

In this article, Roy Campbell’s poems inspired by the Spanish Civil War are revisited and re-examined. Their war images and literary references to this conflict are analysed in the light of the poet’s own political ideology. The conclusion is that the contrast between the exaggerated idealisation to which the rebels and their deeds are subjected, and the degree of degradation that those in favour of the Republic reach in their poetic representation is such that it transforms most of its lines into a quasi-Manichean or moralityplaylike pamphlet.

COMPSs is a programming framework that aims to facilitate the parallelization of existing applications written in Java, C/C++ and Python scripts. For that purpose, it offers a simple programming model based on sequential development in which the user is mainly responsible for identifying the functions to be executed as asynchronous parallel tasks and annotating them with annotations or standard Python decorators. A runtime system is in charge of exploiting the inherent concurrency of the code, automatically detecting and enforcing the data dependencies between tasks and spawning these tasks to the available resources, which can be nodes in a cluster, clouds or grids. In cloud environments, COMPSs provides scalability and elasticity features allowing the dynamic provision of resources. This work has been supported by the following institutions: the Spanish Government with grant SEV-2011-00067 of the Severo Ochoa Program and contract Computacion de Altas Prestaciones VI (TIN2012-34557); by the SGR programme (2014-SGR-1051) of the Catalan Government; by the project The Human Brain Project, funded by the European Commission under contract 604102; by the ASCETiC project funded by the European Commission under contract 610874; by the EUBrazilCloudConnect project funded by the European Commission under contract 614048; and by the Intel-BSC Exascale Lab collaboration.

The growing number of people using social media to publish their opinions, share expertise, make social connections and promote their ideas to an international audience is creating data on an epic scale. This enables social scientists to conduct research into ethnography, discourse analysis and analysis of social interactions, providing insight into today's society, which is largely augmented by social computing. The tools available for such analysis are often proprietary and expensive, and often non-interoperable, meaning the rapid marshalling of large data-sets through a range of analyses is arduous and difficult to scale. The collaborative online social media observatory (COSMOS), an integrated social media analysis tool is presented, developed for open access within academia. COSMOS is underpinned by a scalable Hadoop infrastructure and can support the rapid analysis of large data-sets and the orchestration of workflows between tools with limited human effort. We describe an architecture and scalability results for the computational analysis of social media data, and comment on the storage, search and retrieval issues associated with massive social media data-sets. We also provide an insight into the impact of such an integrated on-demand service in the social science academic community.