A multiphysics and multiscale software environment for modeling astrophysical systems

We present MUSE, a software framework for combining existing computational tools for different astrophysical domains into a single multiphysics, multiscale application. MUSE facilitates the coupling of existing codes written in different languages by providing inter-language tools and by specifying an interface between each module and the framework that represents a balance between generality and computational efficiency. This approach allows scientists to use combinations of codes to solve highly-coupled problems without the need to write new codes for other domains or significantly alter their existing codes. MUSE currently incorporates the domains of stellar dynamics, stellar evolution and stellar hydrodynamics for studying generalized stellar systems. We have now reached a "Noah's Ark" milestone, with (at least) two available numerical solvers for each domain. MUSE can treat multi-scale and multi-physics systems in which the time- and size-scales are well separated, like simulating the evolution of planetary systems, small stellar associations, dense stellar clusters, galaxies and galactic nuclei. In this paper we describe three examples calculated using MUSE: the merger of two galaxies, the merger of two evolving stars, and a hybrid N-body simulation. In addition, we demonstrate an implementation of MUSE on a distributed computer which may also include special-purpose hardware, such as GRAPEs or GPUs, to accelerate computations. The current MUSE code base is publicly available as open source at http://muse.liComment: 24 pages, To appear in New Astronomy Source code available at http://muse.l

The Astrophysical Multipurpose Software Environment

We present the open source Astrophysical Multi-purpose Software Environment (AMUSE, www.amusecode.org), a component library for performing astrophysical simulations involving different physical domains and scales. It couples existing codes within a Python framework based on a communication layer using MPI. The interfaces are standardized for each domain and their implementation based on MPI guarantees that the whole framework is well-suited for distributed computation. It includes facilities for unit handling and data storage. Currently it includes codes for gravitational dynamics, stellar evolution, hydrodynamics and radiative transfer. Within each domain the interfaces to the codes are as similar as possible. We describe the design and implementation of AMUSE, as well as the main components and community codes currently supported and we discuss the code interactions facilitated by the framework. Additionally, we demonstrate how AMUSE can be used to resolve complex astrophysical problems by presenting example applications.Comment: 23 pages, 25 figures, accepted for A&

A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing

Data Grids have been adopted as the platform for scientific communities that need to share, access, transport, process and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this paper, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration. Through this taxonomy, we aim to categorise existing systems to better understand their goals and their methodology. This would help evaluate their applicability for solving similar problems. This taxonomy also provides a "gap analysis" of this area through which researchers can potentially identify new issues for investigation. Finally, we hope that the proposed taxonomy and mapping also helps to provide an easy way for new practitioners to understand this complex area of research.Comment: 46 pages, 16 figures, Technical Repor

Defining bacterial species in the genomic era : insights from the genus Acinetobacter

Background: Microbial taxonomy remains a conservative discipline, relying on phenotypic information derived from growth in pure culture and techniques that are time-consuming and difficult to standardize, particularly when compared to the ease of modern high-throughput genome sequencing. Here, drawing on the genus Acinetobacter as a test case, we examine whether bacterial taxonomy could abandon phenotypic approaches and DNA-DNA hybridization and, instead, rely exclusively on analyses of genome sequence data. Results: In pursuit of this goal, we generated a set of thirteen new draft genome sequences, representing ten species, combined them with other publically available genome sequences and analyzed these 38 strains belonging to the genus. We found that analyses based on 16S rRNA gene sequences were not capable of delineating accepted species. However, a core genome phylogenetic tree proved consistent with the currently accepted taxonomy of the genus, while also identifying three misclassifications of strains in collections or databases. Among rapid distance-based methods, we found average-nucleotide identity (ANI) analyses delivered results consistent with traditional and phylogenetic classifications, whereas gene content based approaches appear to be too strongly influenced by the effects of horizontal gene transfer to agree with previously accepted species. Conclusion: We believe a combination of core genome phylogenetic analysis and ANI provides an appropriate method for bacterial species delineation, whereby bacterial species are defined as monophyletic groups of isolates with genomes that exhibit at least 95% pair-wise ANI. The proposed method is backwards compatible; it provides a scalable and uniform approach that works for both culturable and non-culturable species; is faster and cheaper than traditional taxonomic methods; is easily replicable and transferable among research institutions; and lastly, falls in line with Darwin’s vision of classification becoming, as far as is possible, genealogical

Deceit: A flexible distributed file system

Deceit, a distributed file system (DFS) being developed at Cornell, focuses on flexible file semantics in relation to efficiency, scalability, and reliability. Deceit servers are interchangeable and collectively provide the illusion of a single, large server machine to any clients of the Deceit service. Non-volatile replicas of each file are stored on a subset of the file servers. The user is able to set parameters on a file to achieve different levels of availability, performance, and one-copy serializability. Deceit also supports a file version control mechanism. In contrast with many recent DFS efforts, Deceit can behave like a plain Sun Network File System (NFS) server and can be used by any NFS client without modifying any client software. The current Deceit prototype uses the ISIS Distributed Programming Environment for all communication and process group management, an approach that reduces system complexity and increases system robustness