Enhancing Energy Production with Exascale HPC Methods

High Performance Computing (HPC) resources have become the key actor for achieving more ambitious challenges in many disciplines. In this step beyond, an explosion on the available parallelism and the use of special purpose processors are crucial. With such a goal, the HPC4E project applies new exascale HPC techniques to energy industry simulations, customizing them if necessary, and going beyond the state-of-the-art in the required HPC exascale simulations for different energy sources. In this paper, a general overview of these methods is presented as well as some specific preliminary results.The research leading to these results has received funding from the European Union's Horizon 2020 Programme (2014-2020) under the HPC4E Project (www.hpc4e.eu), grant agreement n° 689772, the Spanish Ministry of Economy and Competitiveness under the CODEC2 project (TIN2015-63562-R), and from the Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP). Computer time on Endeavour cluster is provided by the Intel Corporation, which enabled us to obtain the presented experimental results in uncertainty quantification in seismic imagingPostprint (author's final draft

Reducing energy usage in resource-intensive Java-based scientific applications via micro-benchmark based code refactorings

In-silico research has grown considerably. Today's scientific code involves long-running computer simulations and hence powerful computing infrastructures are needed. Traditionally, research in high-performance computing has focused on executing code as fast as possible, while energy has been recently recognized as another goal to consider. Yet, energy-driven research has mostly focused on the hardware and middleware layers, but few efforts target the application level, where many energy-aware optimizations are possible. We revisit a catalog of Java primitives commonly used in OO scientific programming, or micro-benchmarks, to identify energy-friendly versions of the same primitive. We then apply the micro-benchmarks to classical scientific application kernels and machine learning algorithms for both single-thread and multi-thread implementations on a server. Energy usage reductions at the micro-benchmark level are substantial, while for applications obtained reductions range from 3.90% to 99.18%.Fil: Longo, Mathias. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina. University of Southern California; Estados UnidosFil: Rodriguez, Ana Virginia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

High-Performance Cloud Computing: A View of Scientific Applications

Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solutions and installed facilities such as clusters and super computers, which are difficult to setup, maintain, and operate. Cloud computing provides scientists with a completely new model of utilizing the computing infrastructure. Compute resources, storage resources, as well as applications, can be dynamically provisioned (and integrated within the existing infrastructure) on a pay per use basis. These resources can be released when they are no more needed. Such services are often offered within the context of a Service Level Agreement (SLA), which ensure the desired Quality of Service (QoS). Aneka, an enterprise Cloud computing solution, harnesses the power of compute resources by relying on private and public Clouds and delivers to users the desired QoS. Its flexible and service based infrastructure supports multiple programming paradigms that make Aneka address a variety of different scenarios: from finance applications to computational science. As examples of scientific computing in the Cloud, we present a preliminary case study on using Aneka for the classification of gene expression data and the execution of fMRI brain imaging workflow.Comment: 13 pages, 9 figures, conference pape

Many-Task Computing and Blue Waters

This report discusses many-task computing (MTC) generically and in the context of the proposed Blue Waters systems, which is planned to be the largest NSF-funded supercomputer when it begins production use in 2012. The aim of this report is to inform the BW project about MTC, including understanding aspects of MTC applications that can be used to characterize the domain and understanding the implications of these aspects to middleware and policies. Many MTC applications do not neatly fit the stereotypes of high-performance computing (HPC) or high-throughput computing (HTC) applications. Like HTC applications, by definition MTC applications are structured as graphs of discrete tasks, with explicit input and output dependencies forming the graph edges. However, MTC applications have significant features that distinguish them from typical HTC applications. In particular, different engineering constraints for hardware and software must be met in order to support these applications. HTC applications have traditionally run on platforms such as grids and clusters, through either workflow systems or parallel programming systems. MTC applications, in contrast, will often demand a short time to solution, may be communication intensive or data intensive, and may comprise very short tasks. Therefore, hardware and software for MTC must be engineered to support the additional communication and I/O and must minimize task dispatch overheads. The hardware of large-scale HPC systems, with its high degree of parallelism and support for intensive communication, is well suited for MTC applications. However, HPC systems often lack a dynamic resource-provisioning feature, are not ideal for task communication via the file system, and have an I/O system that is not optimized for MTC-style applications. Hence, additional software support is likely to be required to gain full benefit from the HPC hardware

Research in geosciences policy

Various topics related to cases of difficult adaptation to global change are discussed. Topics include patterns in the ratification of global environmental treaties, the effects of global climate change on Southeast Asia, and global change and biodiversity loss

An Open Source Based Data Warehouse Architecture to Support Decision Making in the Tourism Sector

In this paper an alternative Tourism oriented Data Warehousing architecture is proposed which makes use of the most recent free and open source technologies like Java, Postgresql and XML. Such architecture's aim will be to support the decision making process and giving an integrated view of the whole Tourism reality in an established context (local, regional, national, etc.) without requesting big investments for getting the necessary software.Tourism, Data warehousing architecture

Predicting Intermediate Storage Performance for Workflow Applications

Configuring a storage system to better serve an application is a challenging task complicated by a multidimensional, discrete configuration space and the high cost of space exploration (e.g., by running the application with different storage configurations). To enable selecting the best configuration in a reasonable time, we design an end-to-end performance prediction mechanism that estimates the turn-around time of an application using storage system under a given configuration. This approach focuses on a generic object-based storage system design, supports exploring the impact of optimizations targeting workflow applications (e.g., various data placement schemes) in addition to other, more traditional, configuration knobs (e.g., stripe size or replication level), and models the system operation at data-chunk and control message level. This paper presents our experience to date with designing and using this prediction mechanism. We evaluate this mechanism using micro- as well as synthetic benchmarks mimicking real workflow applications, and a real application.. A preliminary evaluation shows that we are on a good track to meet our objectives: it can scale to model a workflow application run on an entire cluster while offering an over 200x speedup factor (normalized by resource) compared to running the actual application, and can achieve, in the limited number of scenarios we study, a prediction accuracy that enables identifying the best storage system configuration

Automated tools and techniques for distributed Grid Software: Development of the testbed infrastructure

Grid technology is becoming more and more important as the new paradigm for sharing computational resources across different organizations in a secure way. The great powerfulness of this solution, requires the definition of a generic stack of services and protocols and this is the scope of the different Grid initiatives. As a result of international collaborations for its development, the Open Grid Forum created the Open Grid Services Architecture (OGSA) which aims to define the common set of services that will enable interoperability across the different implementations. This master thesis has been developed in this framework, as part of the two European-funded projects ETICS and OMII-Europe. The main objective is to contribute to the design and maintenance of large distributed development projects with the automated tool that enables to implement Software Engineering techniques oriented to achieve an acceptable level of quality at the release process. Specifically, this thesis develops the testbed concept as the virtual production-like scenario where to perform compliance tests. As proof of concept, the OGSA Basic Execution Service has been chosen in order to implement and execute conformance tests within the ETICS automated testbed framework