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Complex coupled multi-physics simulations are playing increasingly important roles in scientific and engineering applications such as fusion plasma and climate modeling. At the same time, extreme scales, high levels of concurrency and the advent of multicore and many core technologies are making the high-end parallel computing systems on which these simulations run, hard to program. While the Partitioned Global Address Space (PGAS) languages is attempting to address the problem, the PGAS model does not easily support the coupling of multiple application codes, which is necessary for the coupled multi-physics simulations. Furthermore, existing frameworks that support coupled simulations have been developed for fragmented programming models such as message passing, and are conceptually mismatched with the shared memory address space abstraction in the PGAS programming model. This paper explores how multi-physics coupled simulations can be supported within the PGAS programming framework. Specifically, in this paper, we present the design and implementation of the XpressSpace programming system, which enables efficient and productive development of coupled simulations across multiple independent PGAS Unified Parallel C (UPC) executables. XpressSpace provides the global-view style programming interface that is consistent with the memory model in UPC, and provides an efficient runtime system that can dynamically capture the data decomposition of global-view arrays and enable fast exchange of parallel data structures between coupled codes. In addition, XpressSpace provides the flexibility to define the coupling process in specification file that is independent of the program source codes. We evaluate the performance and scalability of Xpress Space prototype implementation using different coupling patterns extracted from real world multi-physics simulation scenarios, on the Jaguar Cray XT5 system of Oak Ridge National Laboratory.

Widespread use of a comprehensive CI framework has the potential to revolutionize every science and engineering discipline as well as education. Computing power, data volumes, software, and network capacities are all on exponential growth paths. Highly diverse, multidisciplinary collaborations and partnerships are growing dramatically, greatly enabled by new and emerging technologies, spanning multiple agencies and international domains to address complex grand challenge problems. Scientific discovery is being advanced by linking computational facilities and instruments to build highly-capable simulation models, sophisticated algorithms, software, and other tools and services. CIF21 will enable new approaches to research and education - supporting new modalities such as distributed collaborative networks, allowing researchers to more easily adapt to changes in the research and education process, and providing an integrated framework for people, instruments, and tools to address complex problems and conduct multidisciplinary research. CIF21 will consist of secure, geographically distributed, and connected CI: advanced computing facilities, scientific instruments, software environments, advanced networks, data storage capabilities, and the critically important human capital and expertise.

Driven by input from its scientific community, the Cancer Imaging Program (CIP) finds itself at the junction of two powerful scientific requisites; the need for cross-disciplinary research and inter-institutional data-sharing to speed scientific discovery and reduce redundancy, and the need to provide imaging phenotype data to augment large scale genomic analysis.

Throughout history, humans have mainly relied on different forms of light to observe the universe. Today, we are on the edge of a new frontier in astronomy: gravitational wave astronomy. Gravitational waves carry information on the motions of objects in the universe. Since the universe was transparent to gravity moments after the Big Bang and long before light, gravitational waves will allow us to observe further back into the history of the universe than ever before. And since gravitational waves are not absorbed or reflected by the matter in the rest of the universe, we will be able to see them in the form in which they were created. Moreover, we will effectively be able to "see through" objects between Earth and the gravitational wave source. Most importantly, gravitational waves hold the potential of the unknown. Every time humans have opened new "eyes" on the universe, we have discovered something unexpected that revolutionized how we saw the universe and our place within it. Today, with the United States' gravitational wave detector (LIGO) and its international partners, we are preparing to see the universe with a new set of eyes that do not depend on light

In occasion of the Haiti earthquake an European Commission's Joint Research Center team used the damage reports mapped on the Ushahidi-Haiti platform to show that this crowdsourced data can help predict the spatial distribution of structural damage in Port-au-Prince

We've found that certain search terms are good indicators of flu activity. Google Flu Trends uses aggregated Google search data to estimate flu activity.

This paper describes the design of OpenFOAM, an object- oriented library for Computational Fluid Dynamics (CFD) and struc- tural analysis. Efficient and flexible implementation of complex physi- cal models in Continuum Mechanics is achieved by mimicking the form of partial differential equation in software. The library provides Fi- nite Volume and Finite Element discretisation in operator form and with polyhedral mesh support, with relevant auxiliary tools and sup- port for massively parallel computing. Functionality of OpenFOAM is illustrated on three levels: improvements in linear solver technology with CG-AMG solvers, LES data analysis using Proper Orthogonal Decom- position (POD) and a self-contained fluid-structure interaction solver.

A new program package, XEASY, was written for interactive computer support of the analysis of NMR spectra for three-dimensional structure determination of biological macromolecules. XEASY was developed for work with 2D, 3D and 4D NMR data sets. It includes all the functions performed by the precursor program EASY, which was designed for the analysis of 2D NMR spectra, i.e., peak picking and support of sequence-specific resonance assignments, cross-peak assignments, cross-peak integration and rate constant determination for dynamic processes. Since the program utilizes the X-window system and the Motif widget set, it is portable on a wide range of UNIX workstations. The design objective was to provide maximal computer support for the analysis of spectra, while providing the user with complete control over the final resonance assignments. Technically important features of XEASY are the use and flexible visual display of lsquostripsrsquo, i.e., two-dimensional spectral regions that contain the relevant parts of 3D or 4D NMR spectra, automated sorting routines to narrow down the selection of strips that need to be interactively considered in a particular assignment step, a protocol of resonance assignments that can be used for reliable bookkeeping, independent of the assignment strategy used, and capabilities for proper treatment of spectral folding and efficient transfer of resonance assignments between spectra of different types and different dimensionality, including projected, reduced-dimensionality triple-resonance experiments.

A new program package, XEASY, was written for interactive computer support of the analysis of NMR spectra for three-dimensional structure determination of biological macromolecules. XEASY was developed for work with 2D, 3D and 4D NMR data sets. It includes all the functions performed by the precursor program EASY, which was designed for the analysis of 2D NMR spectra, i.e., peak picking and support of sequence-specific resonance assignments, cross-peak assignments, cross-peak integration and rate constant determination for dynamic processes. Since the program utilizes the X-window system and the Motif widget set, it is portable on a wide range of UNIX workstations. The design objective was to provide maximal computer support for the analysis of spectra, while providing the user with complete control over the final resonance assignments. Technically important features of XEASY are the use and flexible visual display of lsquostripsrsquo, i.e., two-dimensional spectral regions that contain the relevant parts of 3D or 4D NMR spectra, automated sorting routines to narrow down the selection of strips that need to be interactively considered in a particular assignment step, a protocol of resonance assignments that can be used for reliable bookkeeping, independent of the assignment strategy used, and capabilities for proper treatment of spectral folding and efficient transfer of resonance assignments between spectra of different types and different dimensionality, including projected, reduced-dimensionality triple-resonance experiments.

The goal of EarthCube is to transform the conduct of research by supporting the development of community-guided cyberinfrastructure to integrate data and information for knowledge management across the Geosciences. This website has been set up to foster community collaboration, and will provide updated information, resource documents, and discussion forums so that community groups, consortia, researchers, and educators can share ideas, introduce concepts, and find and develop collaborative efforts.

The NIH Human Connectome Project is an ambitious effort to map the neural pathways that underlie human brain function. The overarching purpose of the Project is to acquire and share data about the structural and functional connectivity of the human brain. It will greatly advance the capabilities for imaging and analyzing brain connections, resulting in improved sensitivity, resolution, and utility, thereby accelerating progress in the emerging field of human connectomics. Altogether, the Human Connectome Project will lead to major advances in our understanding of what makes us uniquely human and will set the stage for future studies of abnormal brain circuits in many neurological and psychiatric disorders.

"All models are wrong, but some are useful." So proclaimed statistician George Box 30 years ago, and he was right. But what choice did we have? Only models, from cosmological equations to theories of human behavior, seemed to be able to consistently, if imperfectly, explain the world around us. Until now.