The eXecutable Micro-Architectural Specification (xMAS) language developed in recent years finds an effective way to model on-chip communication fabrics and enables performance-bound analysis with network calculus at the micro-architectural level. For network-on-Chip (NoC) performance analysis, model validation is essential to ensure correctness and accuracy. In order to facilitate the xMAS modeling and corresponding analysis validation, this work presents a unified platform based on xMAS in Simulink. The platform provides a friendly graphical user interface for xMAS modeling and parameter setup by taking advantages of the Simulink modeling environment. The regulator and latency-rate sever are added to the xMAS primitive set to support typical flow and service behaviors. Hierarchical model build-up and Verilog-HDL code generation are essentially supported to manage complex models and to conduct cycle-accurate bit-accurate simulations. Based on the generated simulation models of xMAS, this tool is applied to evaluate the tightness of analytical delay bound results. We demonstrate the application as well as the work flow of the xMAS tool through a two-agent communication example and an all-to-one communication example with a tree topology.

The on-chip communication in current Chip-MultiProcessors (CMP) and MultiProcessor-SoC (MPSoC) is mainly based on the Network-on-Chip (NoC) design paradigm. Unfortunately, it is foreseen that conventional NoC architectures cannot sustain the performance, power, and reliability requirements demanded by the next generation of manycore architectures. Recently, emerging on-chip communication technologies, like wireless Networks-on-Chip (WiNoCs), have been proposed as candidate solutions for addressing the scalability limitations of conventional multi-hop NoC architectures. In a WiNoC, a subset of network nodes are equipped with a wireless interface which allows them long-range communication in a single hop. Assessing the performance and power figures of NoC and WiNoC architectures requires the availability of simulation tools that are often limited on modeling specific network configurations. This article presents Noxim, an open, configurable, extendible, cycle-accurate NoC simulator developed in SystemC, which allows to analyze the performance and power figures of both conventional wired NoC and emerging WiNoC architectures.

Discrete event models are widely used to replicate, analyze, and understand complex systems. DEVS (Discrete Event System Specification) formalism enables hierarchical modeling, so it provides an efficiency in the model development of complex models. However, the hierarchical modeling incurs prolonged simulation executions due to indirect event exchanges through the model hierarchy. Although direct event paths are applied to mitigate this overhead, the situation becomes even worse when a model changes its structures during simulation execution, called a dynamic structure model. This article suggests Coupling Relation Graph (CRG) and Strongly Coupled Component (SCC) concepts to improve hierarchical and dynamic structure DEVS simulation execution. CRG is a directed graph representing DEVS model structure, and SCC is a group of connected components in a CRG. Using CRG and SCC, this article presents (1) how to develop CRG from a DEVS model and (2) how to construct and update direct event paths with respect to dynamic structural changes. In particular, compared to the previous works, the proposed method focuses on the reduction of the updating costs for the direct event paths. Through theoretical and empirical analyses, this article shows that the proposed method significantly reduces the simulation execution time, especially when a simulation model contains lots of components and changes its model structures frequently. We expect that the proposed method would support the faster simulation executions of complex hierarchical and dynamic structure models.

Parallel and distributed simulations (or High-Level Architecture (HLA)-based simulations) employing optimistic synchronization allow federates to advance simulation time freely at the risk of overoptimistic executions and execution rollbacks. As a ...

Based on the theory of stochastic chemical kinetics, the inherent randomness of biochemical reaction networks can be described by discrete-state continuous-time Markov chains. However, the analysis of such processes is computationally expensive and sophisticated numerical methods are required. Here, we propose an analysis framework in which we integrate a number of moments of the process instead of the state probabilities. This results in a very efficient simulation of the time evolution of the process. To regain the state probabilities from the moment representation, we combine the fast moment-based simulation with a maximum entropy approach for the reconstruction of the underlying probability distribution. We investigate the usefulness of this combined approach in the setting of stochastic chemical kinetics and present numerical results for three reaction networks showing its efficiency and accuracy. Besides a simple dimerization system, we study a bistable switch system and a multiattractor network with complex dynamics.

Emerging drug resistance in parasitology and its impact on human and animal health are of serious concern. Attempts by the parasitology community to address this issue led to the introduction of so-called selective therapy where a proportion of the population is left untreated. This has led to re-emergence of parasites that have heretofore been controlled. Using stochastic simulations, this article explores the tradeoff between drug resistance and re-emergence. More importantly, the article identifies the importance of the parasite fitness parameter vector and its role in drug resistance. Suggestions for further biological work and statistical analyses are also provided.

We develop confidence intervals (CIs) for quantiles when applying variance-reduction techniques (VRTs) and sectioning. Similar to batching, sectioning partitions the independent and identically distributed (i.i.d.) outputs into nonoverlapping batches ...

We describe the design and prototype implementation of Indemics (Interactive EpidemicSimulation)-a modeling environment utilizing high-performance computing technologies for supporting complex epidemic simulations. Indemics can support policy analysts and epidemiologists interested in planning and control of pandemics. Indemics goes beyond traditional epidemic simulations by providing a simple and powerful way to represent and analyze policy-based as well as individual-based adaptive interventions. Users can also stop the simulation at any point, assess the state of the simulated system, and add additional interventions. Indemics is available to end-users via a web-based interface. Detailed performance analysis shows that Indemics greatly enhances the capability and productivity of simulating complex intervention strategies with a marginal decrease in performance. We also demonstrate how Indemics was applied in some real case studies where complex interventions were implemented.

Assimilating real-time sensor data into large-scale spatial-temporal simulations, such as simulations of wildfires, is a promising technique for improving simulation results. This asks for advanced data assimilation methods that can work with the complex structures and nonlinear behaviors associated with the simulation models. This article presents a data assimilation framework using Sequential Monte Carlo (SMC) methods for wildfire spread simulations. The models and algorithms of the framework are described, and experimental results are provided. This work demonstrates the feasibility of applying SMC methods to data assimilation of wildfire spread simulations. The developed framework can potentially be generalized to other application areas where sophisticated simulation models are used.