Creating a programming language is a considerable undertaking, even for relatively small domain-specific languages (DSLs). Most approaches to ease this task either limit the flexibility of the DSL or consider entire languages as the unit of composition. This paper presents a new approach using syntactical constructs (also called syncons) for defining DSLs in much smaller units of composition while retaining flexibility. A syntactical construct defines a single language feature, such as an if statement or an anonymous function. Each syntactical construct is fully self-contained: it specifies its own concrete syntax, binding semantics, and runtime semantics, independently of the rest of the language. The runtime semantics are specified as a translation to a user defined target language, while the binding semantics allow name resolution before expansion. Additionally, we present a novel approach for dealing with syntactical ambiguity that arises when combining languages, even if the languages are individually unambiguous. The work is implemented and evaluated in a case study, where small subsets of OCaml and Lua have been defined and composed using syntactical constructs.

Modeling and simulation techniques are today extensively used both in industry and science. Parts of larger systems are, however, typically modeled and simulated by different techniques, tools, and algorithms. In addition, experts from different disciplines use various modeling and simulation techniques. Both these facts make it difficult to study coupled heterogeneous systems. Co-simulation is an emerging enabling technique, where global simulation of a coupled system can be achieved by composing the simulations of its parts. Due to its potential and interdisciplinary nature, cosimulation is being studied in different disciplines but with limited sharing of findings. In this survey, we study and survey the state-of-the-art techniques for co-simulation, with the goal of enhancing future research and highlighting the main challenges. To study this broad topic, we start by focusing on discrete-event-based co-simulation, followed by continuous-time-based co-simulation. Finally, we explore the interactions between these two paradigms, in hybrid co-simulation. To survey the current techniques, tools, and research challenges, we systematically classify recently published research literature on co-simulation, and summarize it into a taxonomy. As a result, we identify the need for finding generic approaches for modular, stable, and accurate coupling of simulation units, as well as expressing the adaptations required to ensure that the coupling is correct.

We introduce a dynamic mechanism for the solution of analytically-tractable substructure in probabilistic programs, using conjugate priors and affine transformations to reduce variance in Monte Carlo estimators. For inference with Sequential Monte Carlo, this automatically yields improvements such as locally-optimal proposals and Rao–Blackwellization. The mechanism maintains a directed graph alongside the running program that evolves dynamically as operations are triggered upon it. Nodes of the graph represent random variables, edges the analytically-tractable relationships between them. Random variables remain in the graph for as long as possible, to be sampled only when they are used by the program in a way that cannot be resolved analytically. In the meantime, they are conditioned on as many observations as possible. We demonstrate the mechanism with a few pedagogical examples, as well as a linear-nonlinear state-space model with simulated data, and an epidemiological model with real data of a dengue outbreak in Micronesia. In all cases one or more variables are automatically marginalized out to significantly reduce variance in estimates of the marginal likelihood, in the final case facilitating a random-weight or pseudo-marginal-type importance sampler for parameter estimation. We have implemented the approach in Anglican and a new probabilistic programming language called Birch.

Model-based design methodologies are commonly used in industry for the development of cyber-physical systems (CPSs). There are many different languages, tools, and formalisms for model-based design, each with its strengths and weaknesses. Instead of accepting the weaknesses of a particular tool, an alternative is to embrace heterogeneity and develop tool integration platforms and protocols to leverage the strengths from other environments. A fairly recent attempt in this direction is an open interface standard called Functional Mock-up Interface (FMI), which is focused on the convenient exchange and co-simulation of simulation models (Functional Mock-up Units, FMUs), primarily between component suppliers and OEMs. As it stands, FMI has reached acceptance in industry, but its specification (version 2.0) provides only limited support for hybrid co-simulation-simulating systems that mix continuous and discrete behaviors, which are commonly used to model CPSs. This paper identifies FMI's time representation based on floating-point numbers as a key problem, because it does not support well the discrete events that typically occur at the cyber-physical boundary; it is only suitable for modeling continuous dynamics without discrete behaviors. While time is a central concept in reasoning about the physical world, it is largely abstracted away when reasoning about the cyber world. As a result, the engineering methods for CPSs have misaligned abstractions between the physics domain, the mathematical domain used to model physics, the computational domain used to implement these mathematical abstractions for simulation, and the computational domain used on the cyber side of CPSs. The most common resolution for this conundrum is to adopt the naive Newtonian ideal model of time, where time is a real number known everywhere and advancing uniformly. But ironically, Newtonian time proves not so practical for hybrid co-simulation. The obvious reason is that digital computers do not work with real numbers. Whereas real numbers have infinite precision, their floating-point representation does not. This discrepancy leads to unpredictable quantization errors that may accumulate. Although real numbers can be compared for equality (e.g., to define “simultaneity”), it rarely makes sense to do so for floating-point numbers. We show that the approach taken in FMI (and many other modeling frameworks) that embraces a naive Newtonian physical model of time, and a cyber-approximation of this model using floating-point numbers, is inadequate for CPSs; it leads to models with unnecessarily inexplicable, nondeterministic, and complex behaviors. Our analysis concludes that a model of time that solely uses integers solves many of these problems. Specifically, we propose to use a 64-bit unsigned integer representation with arbitrary resolution, given as a power of ten, allowing model parameters specified in decimal to be represented exactly (granted ample resolution). Integer arithmetic is computationally efficient, and, for well-chosen resolutions, this representation will tolerate very long simulations without overflow. It is also easily converted to and from floating-point representations, albeit not losslessly. Given the vast range of time scales used across different simulation models, we believe that choosing a fixed universal time resolution does not make sense. Instead, we describe an algorithm that picks an adequate time resolution for a particular model and we provide procedures for time quantization needed to reconcile discrepacies between internal time representations of co-simulated FMUs. We propose concrete extensions to the FMI standard for the support of hybrid co-simulation that enable the use of integer time, automatic choice of time resolution, and the use of absent signals. We explain in detail how these extensions can be implemented mod-ularly within the frameworks of existing simulation environments and with support for legacy FMUs and superdense time.

Physical systems can be naturally modeled by combining continuous and discrete models. Such hybrid models may simplify the modeling task of complex system, as well as increase simulation performance. Moreover, modern simulation engines can often efficiently generate simulation traces, but how do we know that the simulation results are correct? If we detect an error, is the error in the model or in the simulation itself? This paper discusses the problem of simulation safety, with the focus on hybrid modeling and simulation. In particular, two key aspects are studied: safe zero-crossing detection and deterministic hybrid event handling. The problems and solutions are discussed and partially implemented in Modelica and Ptolemy II.

Topic models, and more specifically the class of latent Dirichlet allocation (LDA), are widely used for probabilistic modeling of text. Markov chain Monte Carlo (MCMC) sampling from the posterior distribution is typically performed using a collapsed Gibbs sampler. We propose a parallel sparse partially collapsed Gibbs sampler and compare its speed and efficiency to state-of-the-art samplers for topic models on five well-known text corpora of differing sizes and properties. In particular, we propose and compare two different strategies for sampling the parameter block with latent topic indicators. The experiments show that the increase in statistical inefficiency from only partial collapsing is smaller than commonly assumed, and can be more than compensated by the speedup from parallelization and sparsity on larger corpora. We also prove that the partially collapsed samplers scale well with the size of the corpus. The proposed algorithm is fast, efficient, exact, and can be used in more modeling situations than the ordinary collapsed sampler. Supplementary materials for this article are available online.

The design and implementation of real-time systems require that both the logical and the temporal behavior are correct. There exist several specialized languages and tools that use the notion of logical time, as well as industrial strength languages such as Ada and RTJS that incorporate direct handling of real time. Although these languages and tools have shown to be good alternatives for safety-critical systems, most commodity real-time and embedded systems are today implemented in the standard C programming language. Such systems are typically targeting proprietary bare-metal platforms, standard POSIX compliant platforms, or open-source operating systems. It is, however, error prone to develop large, reliable, and portable systems based on these APIs. In this paper, we present an extension to the C programming language, called Timed C, with a minimal set of language primitives, and show how a retargetable source-to-source compiler can be used to compile and execute simple, expressive, and portable programs. To evaluate our approach, we conduct a case study of a CubeSat satellite. We implement the core timing aspects in Timed C, and show portability by compiling on-board software to both flight hardware, and to low-cost experimental platforms.

Availability of authentication and authorization services is critical for the safety of the Internet of Things (IoT). By leveraging an emerging network architecture based on edge computers, IoT’s availability can be protected even under situations such as network failures or denial-of-service (DoS) attacks. However, little has been explored for the issue of sustaining availability even when edge computers fail. In this paper, we propose an architectural mechanism for enhancing the availability of the authorization infrastructure for the IoT. The proposed approach leverages a technique called secure migration, which allows IoT devices to migrate to other local authorization entities served in trusted edge computers when their authorization entity becomes unavailable. Specifically, we point out necessary considerations for planning secure migration and present automated migration policy construction and protocols for preparing and executing the migration. The effectiveness of our approach is illustrated using a concrete application of smart buildings and network simulation, where our proposed solution achieves significantly higher availability in case of failures in some of the authorization entities.

Embedding a domain-specific language (DSL) in a general purpose host language is an efficient way to develop a new DSL. Various kinds of languages and paradigms can be used as host languages, including object-oriented, functional, statically typed, and dynamically typed variants, all having their pros and cons. For deep embedding, statically typed languages enable early checking and potentially good DSL error messages, instead of reporting runtime errors. Dynamically typed languages, on the other hand, enable flexible transformations, thus avoiding extensive boilerplate code. In this paper, we introduce the concept of gradually typed symbolic expressions that mix static and dynamic typing for symbolic data. The key idea is to combine the strengths of dynamic and static typing in the context of deep embedding of DSLs. We define a gradually typed calculus <*>, formalize its type system and dynamic semantics, and prove type safety. We introduce a host language called Modelyze that is based on <*>, and evaluate the approach by embedding a series of equation-based domain-specific modeling languages, all within the domain of physical modeling and simulation.

Scratchpad memory (SPM) is a promising on-chip memory choice in real-time and cyber-physical systems where timing is of the utmost importance. SPM has time-predictable characteristics since its data movement between the SPM and the main memory is entirely managed by software. One way of such management is dynamic management. In dynamic management of instruction SPMs, code blocks are dynamically copied from the main memory to the SPM at runtime by executing direct memory access (DMA) instructions. Code management techniques try to minimize the overhead of DMA operations by finding an allocation scheme that leads to efficient utilization. In this article, we present three function-level code management techniques. These techniques perform allocation at the granularity of functions, with the objective of minimizing the impact of DMA overhead to the worst-case execution time (WCET) of a given program. The first technique finds an optimal mapping of each function to a region using integer linear programming (ILP), whereas the second technique is a polynomial-time heuristic that is suboptimal. The third technique maps functions directly to SPM addresses, not using regions, which can further reduce the WCET. Based on ILP, it can also find an optimal mapping. We evaluate our techniques using the Mälardalen WCET suite, MiBench suite, and proprietary automotive applications from industry. The results show that our techniques can significantly reduce the WCET estimates compared to caches with the state-of-the-art cache analysis.