Modern big data workflows are characterized by computationally intensive kernels. The simulated results are often combined with knowledge extracted from AI models to ultimately support decision-making. These energy-hungry workflows are increasingly executed in data centers with energy-efficient hard-ware accelerators since FPG As are well-suited for this task due to their inherent parallelism. We present the H2020 project EVEREST, which has developed a system development kit (SDK) to simplify the creation of FPGA-accelerated kernels and manage the execution at runtime through a virtualization environment. This paper describes the main components of the EVEREST SDK and the benefits that can be achieved in our use cases.

Climate change and environmental degradation present significant threats globally. The various sources report on different climate records. The European Green Deal aims to counteract these challenges by achieving zero emissions by 2050, with a significant focus on citizen involvement. Citizen science initiatives play a critical role in engaging the public in climate action through data collection and monitoring of environmental impacts. Technological tools, such as dashboards, facilitate this involvement by visualizing complex climate data, raising awareness, and bridging the knowledge-action gap. This paper discusses a dashboard (developed within the Horizon 2020 project I-CHANGE) designed to engage citizens in environmental data collection and analysis. The Dashboard, co-designed with scientists and stakeholders, supports citizens in viewing, understanding, and interpreting data collected with low-cost sensors and through crowdsourcing activities. The paper gives an overview of the Dashboard's design, implementation, data integration, and its role in fostering public engagement and environmental consciousness.

Engaging the general public in science and providing easy access to scientific knowledge poses significant challenges. In the context of air pollution, we present an integrated approach that combines citizen science, serious games, and accessible technologies as a strategic solution to engage the general public in scientific endeavors. We propose the use of a Serious Game that employs real-time sensor data to raise awareness, disseminate information, and potentially encourage an attitude change. The integration of real-time sensors conjugated with the peculiarities of Serious Games and player motivation theories represent the core concept of providing real-time data in a playful and comprehensible manner. The paper discusses the design and development of the Pollution Runner, a 3D runner game implemented in Unity; the game features game mechanics, interfaces, and difficulty levels based on real-time data fetched from air quality sensors adhering to EPA standards and shared through The World Air Quality Index project. Such data are exported in HTML5 format to maximize its reach, compatibility, accessibility, and ease of use. Through the implementation of this integrated approach, we aim to facilitate public engagement in understanding and addressing sensor data related to air pollution while providing an enjoyable gaming experience, aimed to have a meaningful impact.

Summary A full hydrometeorological (HM) simulation, from rainfall to impact on urban areas, is a multidisciplinary job, which relies on the execution of a workflow composed of complex and heterogeneous model engines. Moreover, the accuracy of the simulation is strongly dependent on an extensive set of configuration parameters, which have to be selected in a consistent way among the models. Within the Distributed Research Infrastructure for Hydro‐Meteorology project, a Web‐based science gateway was developed with the aim to support HM researchers in designing, executing, and managing HM experiments. The core of this science gateway is the portal, which takes care of generating all the configuration files and handles the execution of simulation steps on a heterogeneous computing infrastructure composed of high‐performance computing, Grid resources, and Cloud resources. This paper presents technological insights about the implementation of the portal, with an analysis of the adopted technologies and infrastructures. Our experience highlights the need of coherent policies in the management of data, computational resources, and software components that represent the ecosystem to develop science gateways. Copyright © 2015 John Wiley & Sons, Ltd.

Predicting weather and climate and its impacts on the environment, including hazards such as floods and landslides, is a big challenge that can be efficiently supported by a distributed and heterogeneous infrastructure, exploiting several kinds of computational resources: HPC, Grids and Clouds. This can help researchers in speeding up experiments, improve resolution and accuracy, simulate with different numerical models and model chains. Such numerical models are complex with heavy computational requirements, huge numbers of parameters to tune, and not fully standardized interfaces. Hence, each research entity is usually focusing on a limited set of tools and hard-wired solutions to enable their interaction. The DRIHM approach is based on strong standardization, well defined interfaces, and an easy to use web interface for model configuration and experiment definition. A researcher can easily compare outputs from different hydrologic models forced by the same meteorological model, or compare different meteorological models to validate or improve her research. This paper presents the benefit of a web-based interface for hydro-meteorology research through a detailed analysis of the portal (based on liferay-gUse) developed by the DRIHM project.

Current workstations can offer really amazing raw computational power: up to 10 TFlops on a single machine equipped with multiple CPUs and accelerators as the Intel Xeon Phi or GPU devices. Such results can only be achieved with a massive parallelism of computational devices, thus the actual barrier posed by the exploitation of modern heterogeneous HPC resources is the difficulty in development and/or (performance) efficient porting of software on such architectures. In this paper, we present an experimental study about achievable performance of a widely used, computational intensive application the Fourier Transform, i.e. Discrete Fourier Transform (DFT) and Fast Fourier Transform. We propose an evaluation of the benefits obtained exploiting such resources in terms of performance and programming efforts in the development of the code with a emphasis on the programming approach adopted for code parallelization. With the exception of the interesting performance achieved exploiting GPU for the DFT algorithm, the use state-ofthe- art software libraries provide the best solution since they represent a good compromise to balance programming efforts and performance achievements.

The distributed research infrastructure for hydrometeorology (DRIHM) project focuses on the development of an e-Science infrastructure to provide end-to-end hydro meteorological research (HMR) services (models, data, and post processing tools) by exploiting HPC, Grid and Cloud facilities. In particular, the DRIHM infrastructure supports the execution and analysis of high-resolution simulations through the definition of workflows composed by heterogeneous HMR models in a scalable and interoperable way, while hiding all the low level complexities. This contribution gives insights into best practices adopted to satisfy the requirements of an emerging multidisciplinary scientific community composed of earth and atmospheric scientists. To this end, DRIHM supplies innovative services leveraging high performance and distributed computing resources. Hydro meteorological requirements shape this IT infrastructure through an iterative "learning-by-doing" approach that permits tight interactions between the application community and computer scientists, leading to the development of a flexible, extensible, and interoperable framework.

The allocation of jobs to a set of heterogeneous resources, as in common e-Science environments, is a key issue for their efficient use. To reach a better coupling and improve execution times of applications, a consolidated though challenging opportunity is the benchmarking of resources. In this direction, we propose to exploit two complementary approaches, the micro and application-driven benchmarks, which allow to grasp the behavior of computational contexts with respect to more realistic workloads and to lead to a more efficient assignment of the submitted jobs to resources. To evaluate the appropriateness of our methodology an experimentation on a test bed was carried-out. A first comparison of benchmarking results highlighted differences in performance amongst the two levels of benchmarks. Moreover the appropriateness of adopting benchmarking information to rank resources was also confirmed by examining different allocation strategies through a queuing network simulator.

Tissue MicroArray technology aims to perforin inimunohistocheniical staining on hundreds of different tissue samples simultaneously, allowing faster analysis and considerably reducing costs incurred in staining. The presented work supports the pre-array phase of this technique, i.e. the automatic discrimination between normal and pathological regions within the analyzed tissues, and it works in the specific context of tubular breast cancer. The diagnosis is performed by automatically analyzing specific morphological features of the breast samples, in order to define if tissues present a normal behavior either they show pathological characteristics, in particular the absence of a double layer of cells around the lumen or the decay of a regular glands-and-lobules structure. Tissue structure is investigated through a parallel image processing algorithm, which performs the extraction of morphological parameters from the acquired images and compares them to experimentally validated threshold values. The input image is divided into a number of independent sub-images, to be singularly analyzed and labeled according to the result of the computed diagnosis. The time spent to actually analyze each sub-image generally varies depending on the pathological characteristics of each part of the tissue. In order to properly manage and exploit this feature of the algorithm, the analysis of the sub-images is dynamically dispatched among parallel processes. Experimental results, carried on by exploiting the parallel paradigm, certify the actual improvement in the execution time, leading to almost linear speed-up values on the actual tissue elaboration.

The geometric shape of molecular surfaces strongly influences the docking processes where, although electrostatic, hydrophobic and van der Waals interactions affect greatly the binding affinity of the molecules, shape complementarity is a necessary condition. The vast majority of molecular docking algorithms uses a brute force enumeration of the transformation space, which requires extremely long running times. Few other methods use local shape feature matching to reduce the search to those relative positions which satisfy geometric constraints. Based on a shape analysis tool developed in Computer Graphics, in this paper we introduce ProTailor, a parallel algorithm for efficient multi-scale detection of morphological features on molecular surfaces. Thanks to an almost linear speed-up, we show how ProTailor is well suited to efficiently identify salient features like cavities and depressions, saddle areas or bridges. Feature identification may serve as a powerful tool to automatically locate potential binding sites or as a pre-processing step for efficient shape complementarity assessment in docking prediction.

The merging between molecular biology and computer science is becoming a key point in knowledge discovery, in particular when huge amount of data coming from different laboratories need to be integrated and analyzed. For example Tissue MicroArray (TMA) is a high throughput technology that allows researchers to analyze several tissue samples in a parallel way. TMA is a good validation method of researcher's hypothesis about pathological tissue morphology and biomarkers evaluation. In this context, the Grid technology is a good opportunity to share data and solve intensive storage and computing problems. However, the exploitation of the Grid is not a trivial pursuit for many users. Nowadays, an important issue is to provide a simplified use of Grid resources, enabling the use of tools that scientists usually employ. In this paper, we present TMAinspect, a Web based system for Tissue Microarray experiment analysis that provides to not expert users a graphical interface to search for TMA images and to efficiently process them in EGEE. For the image elaboration we use PIMA(GE)2 Lib, the Parallel IMAGE processing GEnoa Library.

The grid represents a great opportunity in the scientific community to solve data/compute intensive problems and to share data. Using the grid, different application domains could achieve great advantages; however, the exploitation of the grid is not a trivial pursuit for many users. The key issue is to enable a simplified use of grid resources and tools that scientists usually employ. In this paper, we investigate these topics in bioinformatics community to allow the efficient elaboration on the grid of images obtained through the tissue microarray technique. We present a grid framework for tissue microarray analysis, GF4TMA, that allows the selection of TMA images and their efficient analysis by the exploitation of the grid architecture. In particular, we exploited PIMA(GE)2Lib, the Parallel IMAGE processing GEnoa Library. A critical point was to enable parallel computations on the grid without compromising the efficiency and the user-friendliness of the native library. We tackled this issue by encapsulating the library in a grid service.