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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

Advances in computational Grid technologies are enabling the development of simulations of complex biological and physical systems. Such simulations can be assembled from separate components--separately deployable computation units of well-defined functionality. Such an assemblage can represent an application composed of interacting simulations or might comprise multiple instances of a simulation executing together, each running with different simulation parameters. However, such assemblages need the ability to cope with heterogeneous and dynamically changing execution environments, particularly where such changes can affect performance. This paper describes the design and implementation of a prototype performance control system (PerCo), which is capable of monitoring the progress of simulations and redeploying them so as to optimize performance. The ability to control performance by redeployment is demonstrated using an assemblage of lattice Boltzmann simulations running with and without control policies. The cost of using PerCo is evaluated and it is shown that PerCo is able to reduce overall execution time.

SUMMARYGENIE is a suite of modular Earth System Model components coupled in a variety of configurations used to investigate climate phenomena. As part of the GENIEfy project, there is a desire to make the activity of coupling GENIE configurations more flexible in order to ease the integration of new components, permit experimentation with alternative model orderings and connectivity, and execute GENIE components in distributed environments. The current coupling framework is inflexible because models are run in a fixed order by a prescriptive main code. This paper shows how the BFG2 (Bespoke Framework Generatorversion 2) coupling tool offers significantly more flexibility. Using BFG2, scientists describe GENIE configurations as metadata that can then be transformed automatically into the desired framework. It is demonstrated that BFG2 provides flexibility in composition and deployment, improvements that are brought without modification to the GENIE components, without loss of performance and in a such a manner that it is possible to produce exactly the same results as under the original framework. We also demonstrate how BFG2 may be used to improve the performance of future GENIE coupled models.

SUMMARYCoupled modelling is increasingly necessary to make progress in understanding the science of complex physical phenomena and a number of bespoke ('custom') coupled solutions to specific scientific challenges have emerged in recent years. These coupled models generally consist of some framework code in which individual models are embedded. The framework code promotes the required interoperation of the models to solve the larger problem being addressed. Bespoke solutions limit the ability of scientists to share models and to couple them together flexibly to produce (efficient) implementations to address new problems. This paper presents an approach, GCF, which addresses several of these limitations. Individual model sharing and flexibility in composition and deployment is achieved by imposing some lightweight development rules for single models and capturing information relating to the models themselves, to their composition into coupled models and to their deployment onto computational resources as machinereadable metadata. These metadata can be processed to support the generation of an implementation of the coupled model required by the developer. For example, lean and efficient framework code for the specific coupled model and deployment described by the developer can be generated. Alternatively, GCF-compliant models can be automatically adapted for use within other, existing frameworks. This paper presents the design and implementation of a bespoke framework generator to achieve the former, and the flexibility in the composition of GCF-compliant models is demonstrated.