Hardware architectures become increasingly complex as the compute capabilities grow to exascale. We present the Analytical Memory Model with Pipelines (AMMP) of the Performance Prediction Toolkit (PPT). PPT-AMMP takes high-level source code and hardware architecture parameters as input and predicts runtime of that code on the target hardware platform, which is defined in the input parameters. PPT-AMMP

Spiking neural networks (SNN) are among the most computationally intensive types of simulation models, with node counts on the order of up to 1011. Currently, there is intensive research into hardware platforms suitable to support large-scale SNN simulations, whereas several of the most widely used simulators still rely purely on the execution on CPUs. Enabling the execution of these established simulators

Our world today increasingly relies on the orchestration of digital and physical systems to ensure the successful operations of many complex and critical infrastructures. Simulation-based testbeds are useful tools for engineering those cyber-physical systems and evaluating their efficiency, security, and resilience. In this article, we present a cyber-physical system testing platform combining distributed

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Ever since the conception of the statistical ranking-and-selection (R8S) problem, a predominant approach has been the indifference-zone (IZ) formulation. Under the IZ formulation, R8S procedures are designed to provide a guarantee on the probability of correct selection (PCS) whenever the performance of the best system exceeds that of the second-best system by a specified amount. We discuss the shortcomings

Fluid approximation results provide powerful methods for scalable analysis of models of population dynamics with large numbers of discrete states and have seen wide-ranging applications in modelling biological and computer-based systems and model checking. However, the applicability of these methods relies on assumptions that are not easily met in a number of modelling scenarios. This article focuses

In a setting in which experiments are performed repeatedly with the same simulation model, green simulation means reusing outputs from previous experiments to answer the question currently being asked of the model. In this article, we address the setting in which experiments are run to answer questions quickly, with a time limit providing a fixed computational budget, and then idle time is available

This article proposes two-stage Bayesian and frequentist procedures for determining whether a number of systems—each characterized by the same number of performance measures—belongs to a set Γ defined by a finite collection of linear inequalities. A system is “in (not in) Γ” if the vector of the means is in (not in) Γ, where the means must be estimated using Monte Carlo simulation. We develop algorithms

To integrate strategic, tactical, and operational decisions, stochastic programming has been widely used to guide dynamic decision-making. In this article, we consider complex systems and introduce the global-local metamodel-assisted stochastic programming via simulation that can efficiently employ the simulation resource to iteratively solve for the optimal first- and second-stage decisions. Specifically

The health system is intricate due to its dynamic nature and critical service requirements. The involvement of multiple layers of health service providers quadrupled this complexity and results in a complicated operating environment. Simulation is often considered an apt technique to model and study complex systems in the literature. The popularity of simulation in the healthcare domain had only accelerated

This article presents the reproducibility results associated with the article “Green Simulation with Database Monte Carlo,” by Mingbin Feng and Jeremy Staum. The authors have uploaded their artifact to Zenodo, which ensures a long-term retention of the artifact. The artifact, which is based on a set of R scripts, allows to easily regenerate data for the figures and the tables, it completes successfully

A variety of phenomena (such as the spread of diseases, pollution in rivers, etc.) can be studied as diffusion processes over networks (i.e., the diffusion of the phenomenon over a set of interconnected entities). This research introduces a method to study such diffusion processes in multiplex dynamic networks. We use a formal Modeling and Simulation methodology (in our case, DEVS, Discrete-Event System

We consider optimization problems with uncertain constraints that need to be satisfied probabilistically. When data are available, a common method to obtain feasible solutions for such problems is to impose sampled constraints following the so-called scenario optimization approach. However, when the data size is small, the sampled constraints may not statistically support a feasibility guarantee on

Data farming is a descriptive metaphor that captures the notion of generating data purposefully to maximize the information “yield” from simulation models. Large-scale designed experiments let us grow the simulation output efficiently and effectively. We can explore massive input spaces, uncover interesting features of complex simulation response surfaces, and explicitly identify cause-and-effect relationships

Simulations are often used for the design of complex systems as they allow one to explore the design space without the need to build several prototypes. Over the years, the simulation accuracy, as well as the associated computational cost, has increased significantly, limiting the overall number of simulations during the design process. Therefore, metamodeling aims to approximate the simulation response

This article provides a comprehensive and in-depth overview of our work on knowledge discovery in simulations. Application-wise, we focus on manufacturing simulations. Specifically, we propose and discuss a methodology for designing, executing, and analyzing large-scale simulation experiments with a broad coverage of possible system behavior targeted at generating knowledge about the system. Based

One limitation of current data-driven automatic crowd modeling methods is that the models generated have low interpretability, which limits the practical applications of the models. In this article, we propose a new data-driven crowd modeling approach that can generate universal behavior rules with better interpretability. Higher interpretability helps people better understand and analyze the rules

The combination of process modeling and simulation-based analysis provides a quantitative approach to analyze business processes, and to evaluate design alternatives before committing the required resources, to properly align operations with business strategies, improve operational efficiency, and gain competitive advantage. However, the use of simulation-based analysis is still limited in practice

Research in computer architecture is commonly done using software simulators. The simulation speed of such simulators is therefore critical to the rate of progress in research. One of the less commonly used ways to increase the simulation speed is to decompose the benchmark’s execution into contiguous chunks of instructions and simulate these chunks in parallel. Two issues arise from this approach

We develop a theory of superdense time that encompasses existing uses of superdense time in discrete event simulations and points to new forms that have not previously been explored. A central feature of our development is a set of axioms for superdense time. The sufficiency of these axioms is demonstrated by using them to prove that a general model of a discrete event simulation procedure, expressed

A truncated Lévy subordinator is a Lévy subordinator in R+ with Lévy measure restricted from above by a certain level b. In this article, we study the path and distribution properties of this type of process in detail and set up an exact simulation framework based on a marked renewal process. In particular, we focus on a typical specification of truncated Lévy subordinator, namely the truncated stable

Time-inhomogeneous queueing models play an important role in service systems modeling. Although the transient solutions of corresponding continuous-time Markov chains (CTMCs) are more precise than methods using stationary approximations, most authors consider their computational costs prohibitive for practical application. This article presents a new variant of the uniformization algorithm that utilizes

Cells exhibit stochastic behavior when the number of molecules is small. Hence a stochastic reaction-diffusion simulator capable of working at scale can provide a more accurate view of molecular dynamics within the cell. This paper describes a parallel discrete event simulator, Neuron Time Warp-Multi Thread (NTW-MT), developed for the simulation of reaction diffusion models of neurons. To the best