Mapping of some soil properties at catchment scale

[Abstract] The spatial structure of gravel content and three textural fractions (sand, silt, clay) was investigated in a 19.8 ha mixed, agricultural and forest catchment through of geostatistical techniques. Three different depths (0-15 cm, 15-30 cm and 30-45 cm) were sampled in order to describe the spatial variability from 0 to about 300 m. It was shown a spatial structure for all the studied variables, which could be described by different types of semivariograms (sphericals,exponentials an gaussians) with nugget effect component and a spatial component ranging from 3,5 to 365 m. Maps were performed using the information contained in the semivariograms by block kriging, so that contour maps were drawn for the different texture fractions and also showing the kriging errors. It was found greater spatial dependence of the studied variables in the first 15 cm than in the other depths

Analysis and evaluation of MapReduce solutions on an HPC cluster

This is a post-peer-review, pre-copyedit version of an article published in Computers & Electrical Engineering. The final authenticated version is available online at: https://doi.org/10.1016/j.compeleceng.2015.11.021[Abstract] The ever growing needs of Big Data applications are demanding challenging capabilities which cannot be handled easily by traditional systems, and thus more and more organizations are adopting High Performance Computing (HPC) to improve scalability and efficiency. Moreover, Big Data frameworks like Hadoop need to be adapted to leverage the available resources in HPC environments. This situation has caused the emergence of several HPC-oriented MapReduce frameworks, which benefit from different technologies traditionally oriented to supercomputing, such as high-performance interconnects or the message-passing interface. This work aims to establish a taxonomy of these frameworks together with a thorough evaluation, which has been carried out in terms of performance and energy efficiency metrics. Furthermore, the adaptability to emerging disks technologies, such as solid state drives, has been assessed. The results have shown that new frameworks like DataMPI can outperform Hadoop, although using IP over InfiniBand also provides significant benefits without code modifications.Ministerio de Economía y Competitividad; TIN2013-42148-

Flame-MR: An event-driven architecture for MapReduce applications

[Abstract] Nowadays, many organizations analyze their data with the MapReduce paradigm, most of them using the popular Apache Hadoop framework. As the data size managed by MapReduce applications is steadily increasing, the need for improving the Hadoop performance also grows. Existing modifications of Hadoop (e.g., Mellanox Unstructured Data Accelerator) attempt to improve performance by changing some of its underlying subsystems. However, they are not always capable to cope with all its performance bottlenecks or they hinder its portability. Furthermore, new frameworks like Apache Spark or DataMPI can achieve good performance improvements, but they do not keep compatibility with existing MapReduce applications. This paper proposes Flame-MR, a new event-driven MapReduce architecture that increases Hadoop performance by avoiding memory copies and pipelining data movements, without modifying the source code of the applications. The performance evaluation on two representative systems (an HPC cluster and a public cloud platform) has shown experimental evidence of significant performance increases, reducing the execution time by up to 54% on the Amazon EC2 cloud.Ministerio de Economía y Competititvidad; TIN2013-42148-PMinisterio de Educación; FPU14/0280

Servet: A Benchmark Suite for Autotuning on Multicore Clusters

This is a post-peer-review, pre-copyedit version of an article published in 2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS). Proceedings. The final authenticated version is available online at: http://dx.doi.org/10.1109/IPDPS.2010.5470358.[Abstract] MapReduce is a powerful tool for processing large data sets used by many applications running in distributed environments. However, despite the increasing number of computationally intensive problems that require low-latency communications, the adoption of MapReduce in High Performance Computing (HPC) is still emerging. Here languages based on the Partitioned Global Address Space (PGAS) programming model have shown to be a good choice for implementing parallel applications, in order to take advantage of the increasing number of cores per node and the programmability benefits achieved by their global memory view, such as the transparent access to remote data. This paper presents the first PGAS-based MapReduce implementation that uses the Unified Parallel C (UPC) language, which (1) obtains programmability benefits in parallel programming, (2) offers advanced configuration options to define a customized load distribution for different codes, and (3) overcomes performance penalties and bottlenecks that have traditionally prevented the deployment of MapReduce applications in HPC. The performance evaluation of representative applications on shared and distributed memory environments assesses the scalability of the presented MapReduce framework, confirming its suitability.Xunta de Galicia; INCITE08PXIB105161PRMinisterio de Ciencia e Innovación; TIN2007-67537-C03-02Ministerio de Educación; FPU; AP2008-0157

Stress compensation by gap monolayers for stacked InAs/GaAs quantum dots solar cells

In this work we report the stacking of 10 and 50 InAs quantum dots layers using 2 monolayers of GaP for stress compensation and a stack period of 18 nm on GaAs (001) substrates. Very good structural and optical quality is found in both samples. Vertical alignment of the dots is observed by transmission electron microscopy suggesting the existence of residual stress around them. Photocurrent measurements show light absorption up to 1.2 μm in the nanostructures together with a reduction in the blue response of the device. As a result of the phosphorus incorporation in the barriers, a very high thermal activation energy (431 meV) has also been obtained for the quantum dot emission

Emission polarization control in semiconductor quantum dots coupled to a photonic crystal microcavity

8 páginas, 5 figuras.-- OCIS codes: (160.4760) Optical properties; (230.5298) Photonic crystals; (230.5590) Quantumwell, -wire and –dot devices.We study the optical emission of single semiconductor quantum dots weakly coupled to a photonic-crystal micro-cavity. The linearly polarized emission of a selected quantum dot changes continuously its polarization angle, from nearly perpendicular to the cavity mode polarization at large detuning, to parallel at zero detuning, and reversing sign for negative detuning. The linear polarization rotation is qualitatively interpreted in terms of the detuning dependent mixing of the quantum dot and cavity states. The present result is relevant to achieve continuous control of the linear polarization in single photon emitters.This work has been supported by research contracts of the Spanish Ministry of Education Grants MAT2008-01555/NAN, Consolider CSD 2006-19 and Naninpho-QD TEC2008-06756-C03- 01, and the Community of Madrid Grant Grant CAM (S2009/ESP-1503).Peer reviewe