The GPU Virtualization Service (gVirtuS) presented in this work tries to fill the gap between in-house hosted computing clusters, equipped with GPGPUs devices, and pay-for-use high performance virtual clusters deployed via public or private computing clouds. gVirtuS allows an instanced virtual machine to access GPGPUs in a transparent and hypervisor independent way, with an overhead slightly greater than a real machine/GPGPU setup. The performance of the components of gVirtuS is assessed through a suite of tests in different deployment scenarios, such as providing GPGPU power to cloud computing based HPC clusters and sharing remotely hosted GPGPUs among HPC nodes

The astonishing development of diverse and different hardware platforms is twofold: on one side, the challenge for the exascale performance for big data processing and management; on the other side, the mobile and embedded devices for data collection and human machine interaction. This drove to a highly hierarchical evolution of programming models. GVirtuS is the general virtualization system developed in 2009 and firstly introduced in 2010 enabling a completely transparent layer among GPUs and VMs. This paper shows the latest achievements and developments of GVirtuS, now supporting CUDA 6.5, memory management and scheduling. Thanks to the new and improved remoting capabilities, GVirtus now enables GPU sharing among physical and virtual machines based on x86 and ARM CPUs on local workstations, computing clusters and distributed cloud appliances.

This paper presents a Coastal Vulnerability Assessment (CVA) of a microtidal beach located on the Ionian Sea in Calabria region (southern Italy) in order to examine the influence of the different run-up equations on CVA score and propose mitigation measures for the most vulnerable parts of the beach. The coastal area has been severely eroded by extreme wave storms, which have also damaged important archaeological structures located on a nearby cliff. A typical 1 year return period (T r ) storm, associated with the recent criticalities, was chosen to test the different run-up formulas (Holman (1986), Mase (1989) Stockdon et al. (2006) and Poate et al. (2016)) on a number of beach profiles in order to check the sensitivity of the CVA calculation with regard to the different run-up equations. The obtained results provide evidence that different run-up levels often give rise to different CVA scores. Based on vulnerability results, some mitigation measures have been proposed for the beach in front of the archaeological area, based on submerged detached breakwater and an adherent gabion wall for the cliff defence.

Abstract. We introduce Science Object Linking and Embedding (SOLE), a tool for linking research papers with associated science objects, such as source codes, datasets, annotations, workflows, packages, and virtual machine images. The objective of SOLE is to reduce the cost to an author of linking research papers with such science objects for the purpose of reproducible research. To this end, SOLE allows an author to use simple tags to delimit a science object to be associated with a research paper. It creates an adequate representation of the science object and manages a bibliography-like specification of science objects. Authors and readers can reference elements of this bibliography and associate them with phrases in the text of the research paper through a Web interface, in a similar manner to a traditional bibliography tool.

Abstract. Extreme weather events bear a significant impact on coastal human activities and on the related economy. Forecasting and hindcasting the action of sea storms on piers, coastal structures and beaches is an important tool to mitigate their effects. To this end, with particular regard to low coasts and beaches, we have developed a computational model chain based partly on open-access models and partly on an ad-hoc-developed numerical calculator to evaluate beach wave run-up levels and flooding. The offshore wave simulations are carried out with a version of the WaveWatch III model, implemented by CCMMMA (Campania Centre for Marine and Atmospheric Monitoring and Modelling – University of Naples Parthenope), validated with remote-sensing data. The waves thus computed are in turn used as initial conditions for the run-up calculations, carried out with various empirical formulations; the results were finally validated by a set of specially conceived video-camera-based experiments on a micro-tidal beach located on the Ligurian Sea. Statistical parameters are provided on the agreement between the computed and observed values. It appears that, while the system is a useful tool to properly simulate beach flooding during a storm, empirical run-up formulas, when used in a coastal vulnerability context, have to be carefully chosen, applied and managed, particularly on gravel beaches.

Abstract. The prediction of the formation, spacing and location of rip currents is a scientific challenge that can be achieved by means of different complementary methods. In this paper the analysis of numerical and experimental data, including RPAS (remotely piloted aircraft systems) observations, allowed us to detect the presence of rip currents and rip channels at the mouth of Sele River, in the Gulf of Salerno, southern Italy. The dataset used to analyze these phenomena consisted of two different bathymetric surveys, a detailed sediment analysis and a set of high-resolution wave numerical simulations, completed with Google Earth ™ images and RPAS observations. The grain size trend analysis and the numerical simulations allowed us to identify the rip current occurrence, forced by topographically constrained channels incised on the seabed, which were compared with observations.

Summary Low‐power devices are usually highly constrained in terms of CPU computing power, memory, and GPGPU resources for real‐time applications to run. In this paper, we describe RAPID, a complete framework suite for computation offloading to help low‐powered devices overcome these limitations. RAPID supports CPU and GPGPU computation offloading on Linux and Android devices. Moreover, the framework implements lightweight secure data transmission of the offloading operations. We present the architecture of the framework, showing the integration of the CPU and GPGPU offloading modules. We show by extensive experiments that the overhead introduced by the security layer is negligible. We present the first benchmark results showing that Java/Android GPGPU code offloading is possible. Finally, we show the adoption of the GPGPU offloading into BioSurveillance, a commercial real‐time face recognition application. The results show that, thanks to RAPID, BioSurveillance is being successfully adapted to run on low‐power devices. The proposed framework is highly modular and exposes a rich application programming interface to developers, making it highly versatile while hiding the complexity of the underlying networking layer.

Summary Progress in sustainability science is hindered by challenges in creating and managing complex data acquisition, processing, simulation, post‐processing, and intercomparison pipelines. To address these challenges, we developed the Framework to Advance Climate, Economic, and Impact Investigations with Information Technology (FACE‐IT) for crop and climate impact assessments. This integrated data processing and simulation framework enables data ingest from geospatial archives; data regridding, aggregation, and other processing prior to simulation; large‐scale climate impact simulations with agricultural and other models, leveraging high‐performance and cloud computing; and post‐processing to produce aggregated yields and ensemble variables needed for statistics, for model intercomparison, and to connect biophysical models to global and regional economic models. FACE‐IT leverages the capabilities of the Globus Galaxies platform to enable the capture of workflows and outputs in well‐defined, reusable, and comparable forms. We describe FACE‐IT and applications within the Agricultural Model Intercomparison and Improvement Project and the Center for Robust Decision‐making on Climate and Energy Policy. Copyright © 2015 John Wiley & Sons, Ltd.