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This article aims to present a web-based interactive visualization and analytical platform for weather data in Armenia by integrating the three existing infrastructures for observational data, numerical weather prediction, and satellite image processing. The weather data used in the platform consists of near-surface atmospheric elements including air temperature, pressure, relative humidity, wind and precipitation. The visualization and analytical platform has been implemented for 2-m surface temperature. The platform gives Armenian State Hydrometeorological and Monitoring Service analytical capabilities to analyze the in-situ observations, model and satellite image data per station and region for a given period.

The article aims to introduce an integrated web-based interactive data platform for molecular dynamic simulations using the datasets generated by different life science communities from Armenia. The suggested platform, consisting of data repository and workflow management services, is vital for current and future scientific discoveries in the life science domain. We focus on interactive data visualization workflow service as a key to perform more in-depth analyzes of research data outputs, helping to understand the problems efficiently and to consolidate the data into one collective illustration platform. The functionalities of the integrated data platform is presented as an advanced integrated environment to capture, analyze, process and visualize the scientific data.

International audience; The environmental protection is a dominant concern for all types of industries, organizations, and governments. In this regard, the reduction of the energy consumption is substantial in bringing down the CO2 gas emission, which is considered as an important factor causing global warming. The e-infrastructure service providers, such as National Research and Education Networks or National Grid Initiatives have crucial role in the context of energy awareness because the energy consumption of the networking, data, and computational infrastructures keeps increasing exponentially over the time. In addition to this, scientific gateways and cloud services are becoming more significant to tackle scientific and societal challenges. Therefore, there is a need to provide robust and reliable services taking into account energy consumption aspect of e-infrastructures. The aim of the article is to introduce an energy optimization methodology for the beneficiaries of the e-infrastructures to explore, optimize, and report the energy consumption and CO2 emission of data, computing, and networking facilities. The suggested methodology has been implemented within the Armenian e-infrastructure aiming at the reduction of the energy consumption and thereby the CO2 emission.

Proceedings of: Third International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2016). Sofia (Bulgaria), October, 6-7, 2016. The power consumption of data centers is becoming a crucial challenge in the context of the steadily increasing demand for computation. In this regard finding a way to improve energy efficiency of running applications in data centers is becoming a crucial trend. One method to improve the processor utilization is the consolidation of applications on physical servers. It is possible to run multiple jobs in parallel on the same machine, especially when their requirements regarding computation are smaller than the maximum processor performance. It reduces the number of servers in the data center required to handle multiple requests and therefore leads to energy usage reductions. In this paper, we introduce a realistic model of applications with deadlines executed in parallel on a server and competing for the shared resources and present an energy-aware algorithm which may be used to minimize the overall energy consumption of the servers. European Community's Seventh Framework Program This work is partially supported by EU under the COST Program Action 1305: Network for Sustainable Ultrascale Computing (NESUS). The research presented in this paper is partially funded by a grant from Polish National Science Center under award number 2013/08/A/ST6/00296. This research was supported by the EU Seventh Framework Programme FP7/2007–2013 under grant agreement no. FP7-ICT-2013-10 (609757).

The environmental protection is a dominant concern for all types of industries, organizations, and governments. In this regard, the reduction of the energy consumption is substantial in bringing down the CO2 gas emission, which is considered as an important factor causing global warming. The e‐infrastructure service providers, such as National Research and Education Networks or National Grid Initiatives have crucial role in the context of energy awareness because the energy consumption of the networking, data, and computational infrastructures keeps increasing exponentially over the time. In addition to this, scientific gateways and cloud services are becoming more significant to tackle scientific and societal challenges. Therefore, there is a need to provide robust and reliable services taking into account energy consumption aspect of e‐infrastructures. The aim of the article is to introduce an energy optimization methodology for the beneficiaries of the e‐infrastructures to explore, optimize, and report the energy consumption and CO2 emission of data, computing, and networking facilities. The suggested methodology has been implemented within the Armenian e‐infrastructure aiming at the reduction of the energy consumption and thereby the CO2 emission.

Գծային հանրահաշվի LDLT ֆակտորիզացիաները շատ կարևոր են և մեծ կիրառություն ունեն գիտական և ինժեներական հաշվարկներում: Այդ ֆակտորիզացիաների դասին են պատկանում Բանչ-Կաուֆմանի և Աասենի ալգորիթմներով ֆակտորիզացիաները, ինչպես նաև առանց պտույտի LDLT ֆակտոիզացիան: Այս աշխատանքում ներկայացված են նշված երեք ֆակտորիզացիաների իրականացումները հիբրիդային ճարտարապետությունում, և տրված են արտադրողականություններ NVIDIA Tesla K40c գրաֆիկական պրոցեսորի վրա MAGMA գրադարանի կիրառմամբ կոմպլեքս Հերմիտյան մատրիցների համար: LDLT факторизации линейной алгебры очень важны и широко используются в научных и инженерных расчетах. К этому классу относятся факторизации с алгоритмами Банча-Кауфмана и Аасена, а также факторизация LDLT без поворота. В этой работе представлены реализации упомянутых трех факторизаций в гибридной архитектуре, а также представлены производительности на графическом процессоре NVIDIA Tesla K40c с использованием библиотеки MAGMA для комплексных Эрмитовых матриц. Linear algebra LDLT factorizations are very important and widely used in scientific and engineering calculations. The factorizations with Bunch-Kaufmann and Aasen’s algorithms, as well as the LDLT factorization without pivoting also belong to this class of factorizations.The implementation of the mentioned three factorizations in hybrid architecture are presented in this work, and also performances are given on the NVIDIA Tesla K40c graphic processor using the MAGMA library for complex Hermitian matrices. LDLT факторизации линейной алгебры очень важны и широко используются в научных и инженерных расчетах. К этому классу относятся факторизации с алгоритмами Банча-Кауфмана и Аасена, а также факторизация LDLT без поворота. В этой работе представлены реализации упомянутых трех факторизаций в гибридной архитектуре, а также представлены производительности на графическом процессоре NVIDIA Tesla K40c с использованием библиотеки MAGMA для комплексных Эрмитовых матриц.

The most important factor of High performance computing (HPC) systems nowadays is to limit or decrease the power consumption while preserving a high utilization. And with the availability of alternative energy, which powers such systems, there is a need to maximize the usage of alternative energy over brown power. For now, the usage of alternative energy is varying in time due to different factors such as sunny days, the wind, etc. and it is crucial to have an energy-aware algorithm to maximize the usage of this energy. In this paper a SaaS service is presented to optimize a usage of alternative energy, to reduce the power consumption and to preserve a best possible percentage of resource utilization.

Earth Science community depends on the exploration, analysis and reprocessing of high volumes of data as well as the modeling and simulation of complex coupled systems on multiple scales. The main aim of this article is to introduce a new hydrological modeling service based on the Soil and Water Assessment Tool (SWAT) (Arnold et al. J American Water Resour Assoc 34(1), 73–89, 1998 ; Arnold and Fohrer Hydrol Process 19(3), 563–572, 2005) model using high efficiency, resource sharing and low cost cloud computing resources (Astsatryan et al. International Journal of Scientific & Engineering Research 1(1), 1130–1133, 2014). Such a Desktop as a Service (DaaS) approach allowing users to work from anywhere, and gives centralized desktop management and great performance. Within the Spatial Data Infrastructure (SDI) and cloud platform, the DaaS service gives secure access to the model and a centralized data storage to get a SWAT model input. The article illustrates the analyses of the implementation of the SWAT model for the Sotk watershed of Lake Sevan in Armenia (Sargsyan 2007).

Armenia is a small country geographically located in the South Caucasus region. Once the most industrialized republic of the Soviet Union, the country inherited a dramatic ecological situation from the Soviet era. After the mid‐1990s, the economic policy of Armenia shifted towards a strong support for industrial development greatly ignoring ecological interests. As a result, mining‐related industries such as dressing and metallurgical plants were permitted to operate without environmental regulations, and geo‐exploration and exploitation works in deposits were conducted disregarding nature protection norms. As the key national environmental academic entity, the Center for Ecological‐Noosphere Studies of the National Academy of Sciences of the Republic of Armenia (CENS) has a strong national role in delivering authoritative environmental information and data sets. Aware of the need for enhanced and more efficient delivery of information and data sharing, CENS engaged in recent years in several international capacity building projects directed to the setting up of an environmental Spatial Data Infrastructure (SDI). The goal of this SDI was both to provide enhanced capacities for intra muro maintenance and exchange of data among research groups and\ to allow various external stakeholders to access more efficiently geospatial data sets and services. This first step was successful in showing the potential of data sharing for internal institutional organization, to gain visibility towards key stakeholders in the country and the South Caucasus region, and to start engaging in international voluntary partnerships such as the Group on Earth Observations (GEO). CENS now envisions to scale up its SDI infrastructure to a national SDI (nSDI) in order to support a wider range of geospatial services towards the Armenian environmental community. This paper will discuss several aspects and challenges of the envisioned strategy for an Armenian nSDI. First, we present how the current components of the implemented SDI benefit the scientific and environmental communities in Armenia, with an emphasis on the promising link between high performance computing and geospatial services. Second, we examine how the EGIDA methodology – a GEO capacity building suites of activities developed in the FP7 project of the same name – can be applied to support the process of scaling up the infrastructure to become a nSDI, which has been selected as a pilot study in the FP7 EOPOWER project. Finally, we discuss the potential of future full‐scale provision of geospatial services in Armenia and how these could benefit the various stakeholders involved in Armenia and in the South Caucasus region.