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

Understanding Next-Generation VR: Classifying Commodity Clusters for Immersive Virtual Reality

Commodity clusters offer the ability to deliver higher performance computer graphics at lower prices than traditional graphics supercomputers. Immersive virtual reality systems demand notoriously high computational requirements to deliver adequate real-time graphics, leading to the emergence of commodity clusters for immersive virtual reality. Such clusters deliver the graphics power needed by leveraging the combined power of several computers to meet the demands of real-time interactive immersive computer graphics.However, the field of commodity cluster-based virtual reality is still in early stages of development and the field is currently adhoc in nature and lacks order. There is no accepted means for comparing approaches and implementers are left with instinctual or trial-and-error means for selecting an approach.This paper provides a classification system that facilitates understanding not only of the nature of different clustering systems but also the interrelations between them. The system is built from a new model for generalized computer graphics applications, which is based on the flow of data through a sequence of operations over the entire context of the application. Prior models and classification systems have been too focused in context and application whereas the system described here provides a unified means for comparison of works within the field

HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR

Fly-By-Wireless for Next Generation Aircraft: Challenges and Potential solutions

”Fly-By-Wireless” paradigm based on wireless connectivity in aircraft has the potential to improve efficiency and flexibility, while reducing weight, fuel consumption and maintenance costs. In this paper, first, the opportunities and challenges for wireless technologies in safety-critical avionics context are discussed. Then, the assessment of such technologies versus avionics requirements is provided in order to select the most appropriate one for a wireless aircraft application. As a result, the design of a Wireless Avionics Network based on Ultra WideBand technology is investigated, considering the issues of determinism, reliability and security

MOLNs: A cloud platform for interactive, reproducible and scalable spatial stochastic computational experiments in systems biology using PyURDME

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools, a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments

Coordinating views for data visualisation and algorithmic profiling

A number of researchers have designed visualisation systems that consist of multiple components, through which data and interaction commands flow. Such multistage (hybrid) models can be used to reduce algorithmic complexity, and to open up intermediate stages of algorithms for inspection and steering. In this paper, we present work on aiding the developer and the user of such algorithms through the application of interactive visualisation techniques. We present a set of tools designed to profile the performance of other visualisation components, and provide further functionality for the exploration of high dimensional data sets. Case studies are provided, illustrating the application of the profiling modules to a number of data sets. Through this work we are exploring ways in which techniques traditionally used to prepare for visualisation runs, and to retrospectively analyse them, can find new uses within the context of a multi-component visualisation system