A new era in scientific computing: Domain decomposition methods in hybrid CPU–GPU architectures

Recent advances in graphics processing units (GPUs) technology open a new era in high performance computing. Applications of GPUs to scientific computations are attracting a lot of attention due to their low cost in conjunction with their inherently remarkable performance features and the recently enhanced computational precision and improved programming tools. Domain decomposition methods (DDM) constitute today an important category of methods for the solution of highly demanding problems in simulation-based applied science and engineering. Among them, dual domain decomposition methods have been successfully applied in a variety of problems in both sequential as well as in parallel/distributed processing systems. In this work, we demonstrate the implementation of the FETI method to a hybrid CPU–GPU computing environment. Parametric tests on implicit finite element structural mechanics benchmark problems revealed the tremendous potential of this type of hybrid computing environment as a result of the full exploitation of multi-core CPU hardware resources and the intrinsic software and hardware features of the GPUs as well as the numerical properties of the solution method.     

SOA adoption in business networks: do service-oriented architectures really advance inter-organizational integration?

The emergence of the Internet as a global communication infrastructure has dramatically reduced interaction costs within and across organizations, with significant impact on inter-organizational relationships, vertical industry structures, and markets. More recently, service-oriented architectures (SOA) and Web services have introduced the next paradigm shift and foster the idea of dynamic business networks with quick connect and disconnect relationships. However, little research has systematically analyzed how companies leverage SOA to improve their inter-organizational relationships and reshape their business networks. In addition, the mature research stream on inter-organizational information systems (IOS) has not yet sufficiently considered SOA. In order to close this gap, our research seeks to improve the fundamental understanding of how SOA is applied in business networks and how it differs from prior forms of IOS. Using an exploratory research approach, we investigate 33 SOA cases to identify focus areas and patterns of SOA adoption in business networks. Our case analysis builds on a multi-dimensional classification scheme which we derived from prior literature. While our empirical findings do not confirm all promising propositions related to SOA, they underline the specific contribution of SOA compared to prior forms of IOS. We conclude by suggesting five clusters of SOA adoption in the inter-organizational domain, each of those introducing new aspects in the coordination of distributed business networks.     

In-vehicle monitoring and management for military vehicles’ integrated vetronics architectures

Both the rapid uptake of electronics utilisation for military vehicles (vetronics) and the trend in designing vehicles based on open standards and architectures (such as the UK MoD Generic Vehicle Architecture – Def Stan 23-09 GVA) have resulted in modern and future vehicles becoming highly complex platforms. Although most modern vehicles provide advanced diagnostics and health monitoring, most of these systems focus on end systems and applications and there has been little work on monitoring the infrastructure on which these systems operate. Monitoring the operational integrity of the vetronics that enable integration of vehicle subsystems and providing feedback to the crew offers safety and reliability benefits as it allows the crew to assess in real-time, the vehicle’s ability to complete its mission. This paper presents a novel approach to achieve in-vehicle Vetronics Integrity Monitoring and Management (VIMM). The paper highlights the requirements for a generic modular system, and presents both an architecture and proof-of-concept implementation.     

Software architectures: Blueprint,Literature, Language or Decision?

This study questions the traditional view of software architecture as a specification that needs only be understood by software architects and engineers. Based on an intensive study of three software-producing organizations, we identify multiple metaphors (‘Blueprint,’ ‘Literature,’ ‘Language’ and ‘Decision’) that stakeholders use to understand the term software architecture, which in turn, allows them to effectively participate in its creation and use. Our results point to new research directions that may better encompass a broader view of software architecture.     

Energy-aware performance analysis methodologies for HPC architectures—An exploratory study

Performance analysis is a crucial step in HPC architectures including clouds. Traditional performance analysis methodologies that were proposed, implemented, and enacted are functional with the objective of identifying bottlenecks or issues related to memory, programming languages, hardware, and virtualization aspects. However, the need for energy efficient architectures in highly scalable computing environments, such as, Grid or Cloud, has widened the research thrust on developing performance analysis methodologies that analyze the energy inefficiency of HPC applications or their associated hardware. This paper surveys the performance analysis methodologies that investigates into the available energy monitoring and energy awareness mechanisms for HPC architectures. In addition, the paper validates the existing tools in terms of overhead, portability, and user-friendly parameters by conducting experiments at HPCCLoud Research Laboratory at our premise. This research work will promote HPC application developers to select an apt monitoring mechanism and HPC tool developers to augment required energy monitoring mechanisms which fit well with their basic monitoring infrastructures.     

Underwater object detection: architectures and algorithms – a comprehensive review

Underwater object detection is an essential step in image processing and it plays a vital role in several applications such as the repair and maintenance of sub-aquatic structures and marine sciences. Many computer vision-based solutions have been proposed but an optimal solution for underwater object detection and species classification does not exist. This is mainly because of the challenges presented by the underwater environment which mainly include light scattering and light absorption. The advent of deep learning has enabled researchers to solve various problems like protection of the subaquatic ecological environment, emergency rescue, reducing chances of underwater disaster and its prevention, underwater target detection, spooring, and recognition. However, the advantages and shortcomings of these deep learning algorithms are still unclear. Thus, to give a clearer view of the underwater object detection algorithms and their pros and cons, we proffer a state-of-the-art review of different computer vision-based approaches that have been developed as yet. Besides, a comparison of various state-of-the-art schemes is made based on various objective indices and future research directions in the field of underwater object detection have also been proffered.

     

An octree-based,cartesian navier–stokes solver for modern cluster architectures

The Journal of Supercomputing - Adaptive Cartesian mesh approaches have proven useful for multi-scale applications where particular features can be finely resolved within a large solution domain....     

A Matrix–Matrix Multiplication methodology for single/multi-core architectures using SIMD

In this paper, a new methodology for speeding up Matrix–Matrix Multiplication using Single Instruction Multiple Data unit, at one and more cores having a shared cache, is presented. This methodology achieves higher execution speed than ATLAS state of the art library (speedup from 1.08 up to 3.5), by decreasing the number of instructions (load/store and arithmetic) and the data cache accesses and misses in the memory hierarchy. This is achieved by fully exploiting the software characteristics (e.g. data reuse) and hardware parameters (e.g. data caches sizes and associativities) as one problem and not separately, giving high quality solutions and a smaller search space.     

Accelerating solid–fluid interaction based on the immersed boundary method on multicore and GPU architectures

This work proposes several approaches to accelerate the solid–fluid interaction through the use of the Immersed Boundary method on multicore and GPU architectures. Different optimizations on both architectures have been proposed, focusing on memory management and workload mapping. We have chosen two different test scenarios which consist of single-solid and multiple-solid simulations. The performance analysis has been carried out on an intensive set of test cases to analyze the proposed optimizations using multiple CPUs (2) and GPUs (4). An effective performance is obtained for single-solid executions using one CPU (Intel Xeon E5520) achieving a speedup peak equal to 5.5. It is reached a higher benefit on multiple solids obtaining a top speedup of approximately 5.9 and 9 using one CPU (8 cores) and two CPUs (16 cores), respectively. On GPU (Kepler K20c) architecture, two different approaches are presented as the best alternative: one for single-solid executions and one for multiple-solid executions. The best approach obtained for one solid executions achieves a speedup of approximately 17 with respect the sequential counterpart. In contrast, for multiple-solid executions the benefit is much higher, being this type of problems much more suitable for GPU and reaching a peak speedup of 68, 115 and 162 using 1, 2 and 4 GPUs, respectively.     

An image-based approach to the reconstruction of ancient architectures by extracting and arranging 3D spatial components

The objective of this research is the rapid reconstruction of ancient buildings of historical importance using a single image. The key idea of our approach is to reduce the infinite solutions that might otherwise arise when recovering a 3D geometry from 2D photographs. The main outcome of our research shows that the proposed methodology can be used to reconstruct ancient monuments for use as proxies for digital effects in applications such as tourism, games, and entertainment, which do not require very accurate modeling. In this article, we consider the reconstruction of ancient Mughal architecture including the Taj Mahal. We propose a modeling pipeline that makes an easy reconstruction possible using a single photograph taken from a single view, without the need to create complex point clouds from multiple images or the use of laser scanners. First, an initial model is automatically reconstructed using locally fitted planar primitives along with their boundary polygons and the adjacency relation among parts of the polygons. This approach is faster and more accurate than creating a model from scratch because the initial reconstruction phase provides a set of structural information together with the adjacency relation, which makes it possible to estimate the approximate depth of the entire structural monument. Next, we use manual extrapolation and editing techniques with modeling software to assemble and adjust different 3D components of the model. Thus, this research opens up the opportunity for the present generation to experience remote sites of architectural and cultural importance through virtual worlds and real-time mobile applications. Variations of a recreated 3D monument to represent an amalgam of various cultures are targeted for future work.     

PyFR: An open source framework for solving advection–diffusion type problems on streaming architectures using the flux reconstruction approach

High-order numerical methods for unstructured grids combine the superior accuracy of high-order spectral or finite difference methods with the geometric flexibility of low-order finite volume or finite element schemes. The Flux Reconstruction (FR) approach unifies various high-order schemes for unstructured grids within a single framework. Additionally, the FR approach exhibits a significant degree of element locality, and is thus able to run efficiently on modern streaming architectures, such as Graphical Processing Units (GPUs). The aforementioned properties of FR mean it offers a promising route to performing affordable, and hence industrially relevant, scale-resolving simulations of hitherto intractable unsteady flows within the vicinity of real-world engineering geometries. In this paper we present PyFR, an open-source Python based framework for solving advection–diffusion type problems on streaming architectures using the FR approach. The framework is designed to solve a range of governing systems on mixed unstructured grids containing various element types. It is also designed to target a range of hardware platforms via use of an in-built domain specific language based on the Mako templating engine. The current release of PyFR is able to solve the compressible Euler and Navier–Stokes equations on grids of quadrilateral and triangular elements in two dimensions, and hexahedral elements in three dimensions, targeting clusters of CPUs, and NVIDIA GPUs. Results are presented for various benchmark flow problems, single-node performance is discussed, and scalability of the code is demonstrated on up to 104 NVIDIA M2090 GPUs. The software is freely available under a 3-Clause New Style BSD license (see www.pyfr.org).     

Made in China

人们都说":外来的和尚好念经",然而,当软件这一可塑性极强的产品在中国市场上遇到SaaS这一"外来和尚"的时候,似乎这本经并不好念。据了解,尽管被国外引为经典的SaaS模式在国内已经发展了近3年,可是在国外赢利效果相当不错的Salesforce.com     

bodied in sever

d jointly by the national laws,folk customs and morality,though the ways of regulation can vary in different countries or historical periods.laws about sex, sex laws, Foucault0贵州大学学报(社会科学版)Journal of Guizhou Uni     

Distance decay in spectral space in analysing ecosystem β‐diversity

The use of spectral distance for explaining the phenomenon of distance decay in species similarity between two sites (based on the niche difference model) is presented here. Distance decay is based on the first law of geography: ‘the similarity between two sites decays with increasing the distance between them’. From an ecological point of view, this could be expressed as: ‘the β‐diversity between two sites should increase with an increase in spatial distance’. Beta‐diversity is defined as the amount of turnover in species composition from one site to another; and it plays a key role in biodiversity management and conservation, as it allows the detection of spatial gradients that act functionally in determining the spatial variation in species composition. This work demonstrates how the celebrated distance decay pattern achieved by means of spatial distance can be attained even with spectral distance, measured on Landsat near‐infrared images. It is argued that spectral heterogeneity represents a good proxy of β‐diversity of an area, becoming a valuable tool in biodiversity characterization at regional and global scales.