With increasing complexity and software content, modern embedded platforms employ a heterogeneous mix of multi-core processors along with hardware accelerators in order to provide high performance in limited power budgets. Due to complex interactions and highly dynamic behavior, static analysis of real-time performance and other constraints is challenging. As an alternative, full-system simulations have been widely accepted by designers. With traditional approaches being either slow or inaccurate, so-called host-compiled simulators have recently emerged as a solution for rapid evaluation of complete systems at early design stages. In such approaches, a faster simulation is achieved by natively executing application code at the source level, abstracting execution behavior of target platforms, and thus increasing simulation granularity. However, most existing host-compiled simulators often focus on application behavior only while neglecting effects of hardware/software interactions and associated speed and accuracy tradeoffs in platform modeling. In this dissertation, we focus on host-compiled operating system (OS) and processor modeling techniques, and we introduce novel dynamic timing model management approaches that efficiently improve both accuracy and speed of such models via automatically calibrating the simulation granularity. The contributions of this dissertation are twofold: We first establish an infrastructure for efficient host-compiled multi-core platform simulation by developing (a) abstract models of both real-time OSs and processors that replicate timing-accurate hardware/software interactions and enable full-system co-simulation, and (b) quantitative and analytical studies of host-compiled simulation principles to analyze error bounds and investigate possible improvements. Building on this infrastructure, we further propose specific techniques for improving accuracy and speed tradeoffs in host-compiled simulation by developing (c) an automatic timing granularity adjustment technique based on dynamically observing system state to control the simulation, (d) an out-of-order cache hierarchy modeling approach to efficiently reorder memory access behavior in the presence of temporal decoupling, and (e) a synchronized timing model to align platform threads to run efficiently in parallel simulation. Results as applied to industrial-strength platforms confirm that by providing careful abstractions and dynamic timing management, our models can achieve full-system simulations at equivalent speeds of more than a thousand MIPS with less than 3% timing error. Coupled with the capability to easily adjust simulation parameters and configurations, this demonstrates the benefits of our platform models for early application development and exploration. / text

This thesis describes work on a new high-level mathematical modeling language and framework called PDEModelica for modeling with partial differential equations. It is an extension to the current Modelica modeling language for object-oriented, equation-based modeling based on differential and algebraic equations. The language extensions and the framework presented in this thesis are consistent with the concepts of Modelica while adding support for partial differential equations and space-distributed variables called fields. The specification of a partial differential equation problem consists of three parts: 1) the description of the definition domain, i.e., the geometric region where the equations are defined, 2) the initial and boundary conditions, and 3) the actual equations. The known and unknown distributed variables in the equation are represented by field variables in PDEModelica. Domains are defined by a geometric description of their boundaries. Equations may use the Modelica derivative operator extended with support for partial derivatives, or vector differential operators such as divergence and gradient, which can be defined for general curvilinear coordinates based on coordinate system definitions. The PDEModelica system also allows the partial differential equation models to be defined using a coefficient-based approach, where PDE models from a library are instantiated with different parameter values. Such a library contains both continuous and discrete representations of the PDE model. The user can instantiate the continuous parts and define the parameters, and the discrete parts containing the equations are automatically instantiated and used to solve the PDE problem numerically. Compared to most earlier work in the area of mathematical modeling languages supporting PDEs, this work provides a modern object-oriented component-based approach to modeling with PDEs, including general support for hierarchical modeling, and for general, complex geometries. It is possible to separate the geometry definition from the model definition, which allows geometries to be defined separately, collected into libraries, and reused in new models. It is also possible to separate the analytical continuous model description from the chosen discretization and numerical solution methods. This allows the model description to be reused, independent of different numerical solution approaches. The PDEModelica field concept allows general declaration of spatially distributed variables. Compared to most other approaches, the field concept described in this work affords a clearer abstraction and defines a new type of variable. Arrays of such field variables can be defined in the same way as arrays of regular, scalar variables. The PDEModelica language supports a clear, mathematical syntax that can be used both for equations referring to fields and explicit domain specifications, used for example to specify boundary conditions. Hierarchical modeling and decomposition is integrated with a general connection concept, which allows connections between ODE/DAE and PDE based models. The implementation of a Modelica library needed for PDEModelica and a prototype implementation of field variables are also described in the thesis. The PDEModelica library contains internal and external solver implementations, and uses external software for mesh generation, requisite for numerical solution of the PDEs. Finally, some examples modeled with PDEModelica and solved using these implementations are presented.

Les systèmes sur puce sont de plus en plus complexes : ils intègrent des parties numériques, desparties analogiques et des capteurs ou actionneurs. SystemC et son extension SystemC AMSpermettent aujourd’hui de modéliser à haut niveau d’abstraction de tels systèmes. Ces outilsconstituent de véritables atouts dans une optique d’étude de faisabilité, d’exploration architecturale etde vérification du fonctionnement global des systèmes complexes hétérogènes et multiphysiques. Eneffet, les durées de simulation deviennent trop importantes pour envisager les simulations globales àbas niveau d’abstraction. De plus, les simulations basées sur l’utilisation conjointe de différents outilsprovoquent des problèmes de synchronisation. Les modèles de bas niveau, une fois crées par lesspécialistes des différents domaines peuvent toutefois être abstraits afin de générer des modèles dehaut niveau simulables sous SystemC/SystemC AMS en des temps de simulation réduits. Une analysedes modèles de calcul et des styles de modélisation possibles est d’abord présentée afin d’établir unlien avec les durées de simulation, ceci pour proposer un style de modélisation en fonction du niveaud’abstraction souhaité et de l’ampleur de la simulation à effectuer. Dans le cas des circuits analogiqueslinéaires, une méthode permettant de générer automatiquement des modèles de haut niveaud’abstraction à partir de modèles de bas niveau a été proposée. Afin d’évaluer très tôt dans le flot deconception la consommation d’un système, un moyen d’enrichir les modèles de haut niveaupréalablement générés est présenté. L’attention a ensuite été portée sur la modélisation à haut niveaudes systèmes multiphysiques. Deux méthodes y sont discutées : la méthode consistant à utiliser lecircuit équivalent électrique puis la méthode basée sur les bond graphs. En particulier, nous proposonsune méthode permettant de générer un modèle équivalent au bond graph à partir d’un modèle de basniveau. Enfin, la modélisation d’un système éolien est étudiée afin d’illustrer les différents conceptsprésentés dans cette thèse. / Systems on chip are more and more complex as they now embed not only digital and analog parts, butalso sensors and actuators. SystemC and its extension SystemC AMS allow the high level modeling ofsuch systems. These tools are efficient for feasibility study, architectural exploration and globalverification of heterogeneous and multiphysics systems. At low level of abstraction, the simulationdurations are too important. Moreover, synchronization problems appear when cosimulations areperformed. It is possible to abstract the low level models that are developed by the specialists of thedifferent domains to create high level models that can be simulated faster using SystemC/SystemCAMS. The models of computation and the modeling styles have been studied. A relation is shownbetween the modeling style, the model size and the simulation speed. A method that generatesautomatically the high level model of an analog linear circuit from its low level representation isproposed. Then, it is shown how to include in the high level model some information allowing thepower consumption estimation. After that, the multiphysics systems modeling is studied. Twomethods are discussed: firstly, the one that uses the electrical equivalent circuit, then the one based onthe bond graph approach. It is shown how to generate a bond graph equivalent model from a low levelrepresentation. Finally, the modeling of a wind turbine system is discussed in order to illustrate thedifferent concepts presented in this thesis.