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KernelsCompiler/Runtime Hardware Architectures Correctness Performance Metrics bilateralFilter (..) halfSampleRobust (..) renderVolume (..) integrate (..) : : Frame rate Accuracy Energy Computer Vision ICL-NUIM Dataset Fig. 1: The SLAMBench framework makes it possible for experts coming from the computer vision, compiler, run-time, and hardware communities to cooperate in a unified way to tackle algorithmic and implementation alternatives.Abstract-Real-time dense computer vision and SLAM offer great potential for a new level of scene modelling, tracking and real environmental interaction for many types of robot, but their high computational requirements mean that use on mass market embedded platforms is challenging. Meanwhile, trends in low-cost, low-power processing are towards massive parallelism and heterogeneity, making it difficult for robotics and vision researchers to implement their algorithms in a performance-portable way. In this paper we introduce SLAMBench, a publicly-available software framework which represents a starting point for quantitative, comparable and validatable experimental research to investigate trade-offs in performance, accuracy and energy consumption of a dense RGB-D SLAM system. SLAMBench provides a KinectFusion implementation in C++, OpenMP, OpenCL and CUDA, and harnesses the ICL-NUIM dataset of synthetic RGB-D sequences with trajectory and scene ground truth for reliable accuracy comparison of different implementation and algorithms. We present an analysis and breakdown of the constituent algorithmic elements of KinectFusion, and experimentally investigate their execution time on a variety of multicore and GPUaccelerated platforms. For a popular embedded platform, we also present an analysis of energy efficiency for different configuration alternatives.

Abstract. This paper presents a review of the software currently used in climate modelling in general and in CMIP5 in particular to couple the numerical codes representing the different components of the Earth System. The coupling technologies presented show common features, such as the ability to communicate and regrid data, and also offer different functions and implementations. Design characteristics of the different approaches are discussed as well as future challenges arising from the increasing complexity of scientific problems and computing platforms.

Over the past three years we have been developing a new approach for the modelling and simulation of complex fluids. This approach is based on a multiscale hybrid scheme, in which two or more contiguous subdomains are dynamically coupled together. One subdomain is described by molecular dynamics while the other is described by continuum fluid dynamics; such coupled models are of considerable importance for the study of fluid dynamics problems in which only a restricted aspect requires a fully molecular representation. Our model is representative of the generic set of coupled models whose algorithmic structure presents interesting opportunities for deployment on a range of architectures including computational grids. Here we describe the implementation of our HybridMD code within a coupling framework that facilitates flexible deployment on such architectures.

This paper describes the development and first results of the "Community Integrated Assessment System" (CIAS), a unique multi-institutional modular and flexible integrated assessment system for modelling climate change. Key to this development is the supporting software infrastructure, SoftIAM. Through it, CIAS is distributed between the communities of institutions which has each contributed modules to the CIAS system. At the heart of SoftIAM is the Bespoke Framework Generator (BFG) which enables flexibility in the assembly and composition of individual modules from a pool to form coupled models within CIAS, and flexibility in their deployment onto the available software and hardware resources. Such flexibility greatly enhances modellers' ability to re-configure the CIAS coupled models to answer different questions, thus tracking evolving policy needs. It also allows rigorous testing of the robustness of IA modelling results to the use of different component modules representing the same processes (for example, the economy). Such processes are often modelled in very different ways, using different paradigms, at the participating institutions. An illustrative application to the study of the relationship between the economy and the earth's climate system is provided