Modelo de estimación de rendimiento para arquitecturas paralelas heterogéneas

[ES] Un modelo de estimación de rendimiento predice el coste de un algoritmo a partir de una serie de parámetros. En el campo de la computación paralela en CPU se dispone de múltiples modelos para realizar esta estimación de manera teórica, pero si el algoritmo se ejecuta sobre una GPU solo existen unos pocos que presentan importantes carencias. El objetivo de este trabajo es cubrir de alguna manera este vacío, proporcionando un modelo de alto nivel que permita estimar costes de una manera sencilla y que dé soporte para la evaluación de algoritmos ejecutados sobre arquitecturas heterogéneas. Para ello, se comparan varios modelos de GPU ya existentes, y el que exhibe un mejor rendimiento es tomado como base y se le añaden nuevas consideraciones para mejorar las estimaciones. Finalmente, se implementan varios algoritmos para validar las estimaciones del nuevo modelo, comparándolo con los resultados experimentales y con los obtenidos con el modelo de referencia. Dichos algoritmos son la reducción, el producto matriz-vector y la factorización de Cholesky.[EN] A performance model predicts the cost of an algorithm, based on a set of parameters. In the field of parallel CPU computing there are many models which allow to perform a theoretical estimation, but just a few of them exist for algorithms executed in GPU, which present important limitations. This work aims to fulfill this void, providing a high-level model which allows to estimate cost easily and supports evaluating algorithms executed in heterogeneous architectures. In order achieve this goal, previous GPU models are compared using some simple algorithms, and the model which exhibits the best performance is taken as a basis and new considerations are included in order to improve its estimations. Finally, some algorithms are implemented in order to validate the new model estimations, compared both with experimental results and the ones got with the base model. These algorithms are reduction, matrix-vector product and Cholesky's decomposition.González García, CY. (2012). Modelo de estimación de rendimiento para arquitecturas paralelas heterogéneas. http://hdl.handle.net/10251/27244Archivo delegad

The ELIXIR Human Copy Number Variations Community:building bioinformatics infrastructure for research

Copy number variations (CNVs) are major causative contributors both in the genesis of genetic diseases and human neoplasias. While 'High-Throughput' sequencing technologies are increasingly becoming the primary choice for genomic screening analysis, their ability to efficiently detect CNVs is still heterogeneous and remains to be developed. The aim of this white paper is to provide a guiding framework for the future contributions of ELIXIR's recently established h uman CNV Community, with implications beyond human disease diagnostics and population genomics. This white paper is the direct result of a strategy meeting that took place in September 2018 in Hinxton (UK) and involved representatives of 11 ELIXIR Nodes. The meeting led to the definition of priority objectives and tasks, to address a wide range of CNV-related challenges ranging from detection and interpretation to sharing and training. Here, we provide suggestions on how to align these tasks within the ELIXIR Platforms strategy, and on how to frame the activities of this new ELIXIR Community in the international context