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.\ud 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\ud 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\ud under contract 604102; by the ASCETiC project funded by the European Commission under contract 610874; by the\ud EUBrazilCloudConnect project funded by the European Commission under contract 614048; and by the Intel-BSC Exascale\ud Lab collaboration.Peer ReviewedPostprint (published version

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.

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 noninteroperable, 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 ondemand service in the social science academic community.

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Abstract-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 paper describes why we consider that task-based programming models are a good approach for Big Data applications. The paper 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 analyse different workflows 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 pre-defined 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.