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

Prognostics and health management of light emitting diodes

Prognostics is an engineering process of diagnosing, predicting the remaining useful life and estimating the reliability of systems and products. Prognostics and Health Management (PHM) has emerged in the last decade as one of the most efficient approaches in failure prevention, reliability estimation and remaining useful life predictions of various engineering systems and products. Light Emitting Diodes (LEDs) are optoelectronic micro-devices that are now replacing traditional incandescent and fluorescent lighting, as they have many advantages including higher reliability, greater energy efficiency, long life time and faster switching speed. Even though LEDs have high reliability and long life time, manufacturers and lighting systems designers still need to assess the reliability of LED lighting systems and the failures in the LED. This research provides both experimental and theoretical results that demonstrate the use of prognostics and health monitoring techniques for high power LEDs subjected to harsh operating conditions. Data driven, model driven and fusion prognostics approaches are developed to monitor and identify LED failures, based on the requirement for the light output power. The approaches adopted in this work are validated and can be used to assess the life of an LED lighting system after their deployment based on the power of the light output emitted. The data driven techniques are only based on monitoring selected operational and performance indicators using sensors whereas the model driven technique is based on sensor data as well as on a developed empirical model. Fusion approach is also developed using the data driven and the model driven approaches to the LED. Real-time implementation of developed approaches are also investigated and discussed

Robust decision analysis for environmental management of groundwater contamination sites

In contrast to many other engineering fields, the uncertainties in subsurface processes (e.g., fluid flow and contaminant transport in aquifers) and their parameters are notoriously difficult to observe, measure, and characterize. This causes severe uncertainties that need to be addressed in any decision analysis related to optimal management and remediation of groundwater contamination sites. Furthermore, decision analyses typically rely heavily on complex data analyses and/or model predictions, which are often poorly constrained as well. Recently, we have developed a model-driven decision-support framework (called MADS; http://mads.lanl.gov) for the management and remediation of subsurface contamination sites in which severe uncertainties and complex physics-based models are coupled to perform scientifically defensible decision analyses. The decision analyses are based on Information Gap Decision Theory (IGDT). We demonstrate the MADS capabilities by solving a decision problem related to optimal monitoring network design.Comment: This paper has been withdrawn by the author due to a crucial sign error in equations 7 and

A Formal Architectural Description Language based on Symbolic Transition Systems and Modal Logic

International audienceComponent Based Software Engineering has now emerged as a discipline for system development. After years of battle between component platforms, the need for means to abstract away from specific implementation details is now recognized. This paves the way for model driven approaches (such as MDE) but also for the more older Architectural Description Language (ADL) paradigm. In this paper we present KADL, an ADL based on the Korrigan formal language which supports the following features: integration of fully formal behaviours and data types, expressive component composition mechanisms through the use of modal logic, specification readability through graphical notations, and dedicated architectural analysis techniques. Key Words: Architectural Description Language, Component Based Software Engineering, Mixed Formal Specifications, Symbolic Transition Systems, Abstract Data Types, Modal Logic Glue, Graphical Notations, Verification

Safurai 001: New Qualitative Approach for Code LLM Evaluation

This paper presents Safurai-001, a new Large Language Model (LLM) with significant potential in the domain of coding assistance. Driven by recent advancements in coding LLMs, Safurai-001 competes in performance with the latest models like WizardCoder [Xu et al., 2023], PanguCoder [Shen et al., 2023] and Phi-1 [Gunasekar et al., 2023] but aims to deliver a more conversational interaction. By capitalizing on the progress in data engineering (including latest techniques of data transformation and prompt engineering) and instruction tuning, this new model promises to stand toe-to-toe with recent closed and open source developments. Recognizing the need for an efficacious evaluation metric for coding LLMs, this paper also introduces GPT4-based MultiParameters, an evaluation benchmark that harnesses varied parameters to present a comprehensive insight into the models functioning and performance. Our assessment shows that Safurai-001 can outperform GPT-3.5 by 1.58% and WizardCoder by 18.78% in the Code Readability parameter and more.Comment: 22 pages, 1 figure, 3 table

Data Driven Prognosis: A Multi-Scale And Multi-Physics Approach

All current engineering prognostic practices require prior off-line tests. These are needed to: (1) Determine the exact conservative principle or utility function being satisfied, and (2) Determine associated material, geometric and process parameters. In addition, prediction of onset of instability or failure requires a failure criterion. The data driven prognosis (DDP) approach, developed here, obviates the need for such off-line testing and facilitates true predictive capability using only on-line data being sensed. To achieve this end, the DDP algorithm makes an assumption regarding polynomial order of the potential or utility function in the neighborhood of each observation points. Thus, an assumption regarding the local piecewise behavior replaces any global assumption. The needed system parameters in dimensionless forms are then estimated based on prior data or experience from the same experiment. A multi-physics model based on the concept of excess curvature is then developed to predict short-term and long term stability profiles of any system. The model is first validated against simple Balloon Burst experiment and later used for analyzing Gulf Stream and Economics systems. The proposed DDP algorithm may be used for general conservative systems provided the variables involved in the conservation principle are observable. The developed multi-physics model also provides an objective basis for data driven prediction of system stability and associated decision making in various mechanical, economic and societal systems

A Data-driven Model Development for Generalized Building Energy Predictions

Building energy predictions are in critical need in many fields. The conventional physic model-based approach (via EnergyPlus or similar tools) does decent work to predict energy consumptions. However, it is limited to single predefined building analysis and requires an extensive amount of time and labor to build models. Nevertheless, decision-makers usually need to quantify the energy savings of large building clusters within a short time. The thriving of big data and machine learning techniques enables predicting energy consumptions accurately for different applications within reasonable time frames. This study aims at developing data-driven models for generalized building energy predictions. The models can be used for establishing counter-factual baselines to validate the efficacy of energy-saving measures and energy production and usage planning. The former is usually for medium to long-term durations, while the latter is for short-term durations. We used real-world open data sets from ASHRAE, which covers energy consumptions of about 1,500 buildings for two years. We then preprocessed the data following the industry\u27s standard practice. Multiple approaches of missing values imputations, outlier detections, and feature engineering were explored, based on which the best methods are suggested for building energy predictions. Gradient boosting (GB) based model has been developed for medium to long-term predictions, while the long short-term memory (LSTM) based model has been developed for short term predictions. Hyperparameter tuning was performed on model structures and parameters. We used root mean squared error (RMSE) between the predicted and true energy consumptions to evaluate performances. The results show that the GB based model achieves RMSE of 0.49 for electricity, 1.10 for chilled water, 1.25 for steam, and 1.32 for hot water. The LSTM model performs better with shorter prediction days and longer input days. However, further increasing input days beyond a week does not increase the performance. The LSTM model has about 38% lower prediction errors than the baselines, which are averages of energy consumptions from similar historical days. The study demonstrates the development process of data-driven models for general purpose building energy predictions, from data preparations, model selections, development, and evaluations