A Modeling Approach based on UML/MARTE for GPU Architecture

Nowadays, the High Performance Computing is part of the context of embedded systems. Graphics Processing Units (GPUs) are more and more used in acceleration of the most part of algorithms and applications. Over the past years, not many efforts have been done to describe abstractions of applications in relation to their target architectures. Thus, when developers need to associate applications and GPUs, for example, they find difficulty and prefer using API for these architectures. This paper presents a metamodel extension for MARTE profile and a model for GPU architectures. The main goal is to specify the task and data allocation in the memory hierarchy of these architectures. The results show that this approach will help to generate code for GPUs based on model transformations using Model Driven Engineering (MDE).Comment: Symposium en Architectures nouvelles de machines (SympA'14) (2011

A New Approach for Quality Management in Pervasive Computing Environments

This paper provides an extension of MDA called Context-aware Quality Model Driven Architecture (CQ-MDA) which can be used for quality control in pervasive computing environments. The proposed CQ-MDA approach based on ContextualArchRQMM (Contextual ARCHitecture Quality Requirement MetaModel), being an extension to the MDA, allows for considering quality and resources-awareness while conducting the design process. The contributions of this paper are a meta-model for architecture quality control of context-aware applications and a model driven approach to separate architecture concerns from context and quality concerns and to configure reconfigurable software architectures of distributed systems. To demonstrate the utility of our approach, we use a videoconference system.Comment: 10 pages, 10 Figures, Oral Presentation in ECSA 201

Screening and metamodeling of computer experiments with functional outputs. Application to thermal-hydraulic computations

To perform uncertainty, sensitivity or optimization analysis on scalar variables calculated by a cpu time expensive computer code, a widely accepted methodology consists in first identifying the most influential uncertain inputs (by screening techniques), and then in replacing the cpu time expensive model by a cpu inexpensive mathematical function, called a metamodel. This paper extends this methodology to the functional output case, for instance when the model output variables are curves. The screening approach is based on the analysis of variance and principal component analysis of output curves. The functional metamodeling consists in a curve classification step, a dimension reduction step, then a classical metamodeling step. An industrial nuclear reactor application (dealing with uncertainties in the pressurized thermal shock analysis) illustrates all these steps

Aerodynamic shape optimization of a low drag fairing for small livestock trailers

Small livestock trailers are commonly used to transport animals from farms to market within the United Kingdom. Due to the bluff nature of these vehicles there is great potential for reducing drag with a simple add-on fairing. This paper explores the feasibility of combining high-fidelity aerodynamic analysis, accurate metamodeling, and efficient optimization techniques to find an optimum fairing geometry which reduces drag, without significantly impairing internal ventilation. Airflow simulations were carried out using Computational Fluid Dynamics (CFD) to assess the performance of each fairing based on three design variables. A Moving Least Squares (MLS) metamodel was built on a fifty-point Optimal Latin Hypercube (OLH) Design of Experiments (DoE), where each point represented a different geometry configuration. Traditional optimization techniques were employed on the metamodel until an optimum geometrical configuration was found. This optimum design was tested using CFD and it matched closely to the metamodel prediction. Further, the drag reduction was measured at 14.4% on the trailer and 6.6% for the combined truck and trailer

Model-Driven Methodology for Rapid Deployment of Smart Spaces based on Resource-Oriented Architectures

Advances in electronics nowadays facilitate the design of smart spaces based on physical mash-ups of sensor and actuator devices. At the same time, software paradigms such as Internet of Things (IoT) and Web of Things (WoT) are motivating the creation of technology to support the development and deployment of web-enabled embedded sensor and actuator devices with two major objectives: (i) to integrate sensing and actuating functionalities into everyday objects, and (ii) to easily allow a diversity of devices to plug into the Internet. Currently, developers who are applying this Internet-oriented approach need to have solid understanding about specific platforms and web technologies. In order to alleviate this development process, this research proposes a Resource-Oriented and Ontology-Driven Development (ROOD) methodology based on the Model Driven Architecture (MDA). This methodology aims at enabling the development of smart spaces through a set of modeling tools and semantic technologies that support the definition of the smart space and the automatic generation of code at hardware level. ROOD feasibility is demonstrated by building an adaptive health monitoring service for a Smart Gym

Knowledge Reuse for Customization: Metamodels in an Open Design Community for 3d Printing

Theories of knowledge reuse posit two distinct processes: reuse for replication and reuse for innovation. We identify another distinct process, reuse for customization. Reuse for customization is a process in which designers manipulate the parameters of metamodels to produce models that fulfill their personal needs. We test hypotheses about reuse for customization in Thingiverse, a community of designers that shares files for three-dimensional printing. 3D metamodels are reused more often than the 3D models they generate. The reuse of metamodels is amplified when the metamodels are created by designers with greater community experience. Metamodels make the community's design knowledge available for reuse for customization-or further extension of the metamodels, a kind of reuse for innovation

Meta-model Pruning

Large and complex meta-models such as those of Uml and its profiles are growing due to modelling and inter-operability needs of numerous\ud stakeholders. The complexity of such meta-models has led to coining\ud of the term meta-muddle. Individual users often exercise only a small\ud view of a meta-muddle for tasks ranging from model creation to construction\ud of model transformations. What is the effective meta-model that represents\ud this view? We present a flexible meta-model pruning algorithm and\ud tool to extract effective meta-models from a meta-muddle. We use\ud the notion of model typing for meta-models to verify that the algorithm\ud generates a super-type of the large meta-model representing the meta-muddle.\ud This implies that all programs written using the effective meta-model\ud will work for the meta-muddle hence preserving backward compatibility.\ud All instances of the effective meta-model are also instances of the\ud meta-muddle. We illustrate how pruning the original Uml metamodel\ud produces different effective meta-models