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Overlapping Batch Confidence Intervals on Statistical Functionals Constructed from Time Series: Application to Quantiles, Optimization, and Estimation ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2024-03-14 Ziwei Su, Raghu Pasupathy, Yingchieh Yeh, Peter W. Glynn
We propose a general purpose confidence interval procedure (CIP) for statistical functionals constructed using data from a stationary time series. The procedures we propose are based on derived distribution-free analogues of the χ2 and Student’s t random variables for the statistical functional context, and hence apply in a wide variety of settings including quantile estimation, gradient estimation
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Performance Evaluation of Spintronic-Based Spiking Neural Networks Using Parallel Discrete-Event Simulation ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2024-03-05 Elkin Cruz-Camacho, Siyuan Qian, Ankit Shukla, Neil McGlohon, Shaloo Rakheja, Christopher D. Carothers
Spintronics devices that use the spin of electrons as the information state variable have the potential to emulate neuro-synaptic dynamics and can be realized within a compact form-factor, while operating at ultra-low energy-delay point. In this paper, we benchmark the performance of a spintronics hardware platform designed for handling neuromorphic tasks. To explore the benefits of spintronics-based
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Projected Gaussian Markov Improvement Algorithm for High-dimensional Discrete Optimization via Simulation ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2024-03-01 Xinru Li, Eunhye Song
This paper considers a discrete optimization via simulation (DOvS) problem defined on a graph embedded in the high-dimensional integer grid. Several DOvS algorithms that model the responses at the solutions as a realization of a Gaussian Markov random field (GMRF) have been proposed exploiting its inferential power and computational benefits. However, the computational cost of inference increases exponentially
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End-to-End Statistical Model Checking for Parameterization and Stability Analysis of ODE Models ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2024-02-24 David Julien, Gilles Ardourel, Guillaume Cantin, Benoît Delahaye
We propose a simulation-based technique for the parameterization and the stability analysis of parametric Ordinary Differential Equations. This technique is an adaptation of Statistical Model Checking, often used to verify the validity of biological models, to the setting of Ordinary Differential Equations systems. The aim of our technique is to estimate the probability of satisfying a given property
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Hyperparameter Tuning with Gaussian Processes for Optimal Abstraction Control in Simulation-based Optimization of Smart Semiconductor Manufacturing Systems ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2024-02-17 Moon Gi Seok, Wen Jun Tan, Boyi Su, Wentong Cai, Jisu Kwon, Seon Han Choi
Smart manufacturing utilizes digital twins that are virtual forms of their production plants for analyzing and optimizing decisions. Digital twins have been mainly developed as discrete-event models (DEMs) to represent the detailed and stochastic dynamics of productions in the plants. The optimum decision is achieved after simulating the DEM-based digital twins under various what-if decision candidates;
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Sufficient Conditions for Central Limit Theorems and Confidence Intervals for Randomized Quasi-Monte Carlo Methods ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2024-02-14 Marvin K. Nakayama, Bruno Tuffin
Randomized quasi-Monte Carlo methods have been introduced with the main purpose of yielding a computable measure of error for quasi-Monte Carlo approximations through the implicit application of a central limit theorem over independent randomizations. But to increase precision for a given computational budget, the number of independent randomizations is usually set to a small value so that a large
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Parallel Simulation of Quantum Networks with Distributed Quantum State Management ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2024-01-31 Xiaoliang Wu, Alexander Kolar, Joaquin Chung, Dong Jin, Martin Suchara, Rajkumar Kettimuthu
Quantum network simulators offer the opportunity to cost-efficiently investigate potential avenues for building networks that scale with the number of users, communication distance, and application demands by simulating alternative hardware designs and control protocols. Several quantum network simulators have been recently developed with these goals in mind. As the size of the simulated networks increases
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Exact and Approximate Moment Derivation for Probabilistic Loops With Non-Polynomial Assignments ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2024-01-23 Andrey Kofnov, Marcel Moosbrugger, Miroslav Stankovič, Ezio Bartocci, Efstathia Bura
Many stochastic continuous-state dynamical systems can be modeled as probabilistic programs with nonlinear non-polynomial updates in non-nested loops. We present two methods, one approximate and one exact, to automatically compute, without sampling, moment-based invariants for such probabilistic programs as closed-form solutions parameterized by the loop iteration. The exact method applies to probabilistic
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Bayesian Optimisation for Constrained Problems ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2024-01-22 Juan Ungredda, Juergen Branke
Many real-world optimisation problems such as hyperparameter tuning in machine learning or simulation-based optimisation can be formulated as expensive-to-evaluate black-box functions. A popular approach to tackle such problems is Bayesian optimisation, which builds a response surface model based on the data collected so far, and uses the mean and uncertainty predicted by the model to decide what information
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Knowledge Equivalence in Digital Twins of Intelligent Systems ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2024-01-14 Nan Zhang, Rami Bahsoon, Nikos Tziritas, Georgios Theodoropoulos
A digital twin contains up-to-date data-driven models of the physical world being studied and can use simulation to optimise the physical world. However, the analysis made by the digital twin is valid and reliable only when the model is equivalent to the physical world. Maintaining such an equivalent model is challenging, especially when the physical systems being modelled are intelligent and autonomous
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A Prescriptive Simulation Framework with Realistic Behavioural Modelling for Emergency Evacuations ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2024-01-14 Md. Shalihin Othman, Gary Tan
Emergency and crisis simulations play a pivotal role in equipping authorities worldwide with the necessary tools to minimize the impact of catastrophic events. Various studies have explored the integration of intelligence into Multi-Agent Systems (MAS) for crisis simulation. This involves incorporating psychological behaviours from the social sciences and utilizing data-driven machine learning models
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Divergence Reduction in Monte Carlo Neutron Transport with On-GPU Asynchronous Scheduling ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2024-01-14 Braxton Cuneo, Mike Bailey
While Monte Carlo Neutron Transport (MCNT) is near-embarrasingly parallel, the effectively unpredictable lifetime of neutrons can lead to divergence when MCNT is evaluated on GPUs. Divergence is the phenomenon of adjacent threads in a warp executing different control flow paths; on GPUS, it reduces performance because each work group may only execute one path at a time. The process of Thread Data Remapping
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Reproducibility Report for the Paper: Parallel Simulation of Quantum Networks with Distributed Quantum State Management ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2024-01-08 Andrea Piccione
The examined paper introduces a parallel version of SeQUeNCe, a Discrete Event Simulator for quantum networks. The authors have deposited their artifact on Zenodo, meeting the criteria for long-term preservation required by the Artifacts Available badge. The software within the artifact functions correctly with minor adjustments, aligning with the paper’s relevance and earning the Artifacts Evaluated—Functional
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Spatial/Temporal Locality-based Load-sharing in Speculative Discrete Event Simulation on Multi-core Machines ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2024-01-08 Federica Montesano, Romolo Marotta, Francesco Quaglia
Shared-memory multi-processor/multi-core machines have become a reference for many application contexts. In particular, the recent literature on speculative parallel discrete event simulation has reshuffled the architectural organization of simulation systems in order to deeply exploit the main features of this type of machines. A core aspect dealt with has been the full sharing of the workload at
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An Improved Model of Wavelet Leader Covariance for Estimating Multifractal Properties ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-12-16 Garry Jacyna, Damon Frezza, David M. Slater, James R. Thompson
Complex systems often produce multifractal signals defined by stationary increments that exhibit power-law scaling properties. The Legendre transform of the domain-dependent scaling function that defines the power law is known as the multifractal spectrum. The multifractal spectrum can also be defined by a power-series expansion of the scaling function and in practice the first two leading coefficients
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VT-IO: A Virtual Time System Enabling High-fidelity Container-based Network Emulation for I/O Intensive Applications ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-12-05 Gong Chen, Zheng Hu, Yanfeng Qu, Dong Jin
Network emulation allows unmodified code execution on lightweight containers to enable accurate and scalable networked application testing. However, such testbeds cannot guarantee fidelity under high workloads, especially when many processes concurrently request resources (e.g., CPU, disk I/O, GPU, and network bandwidth) that are more than the underlying physical machine can offer. A virtual time system
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LN: A Flexible Algorithmic Framework for Layered Queueing Network Analysis ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-11-21 Giuliano Casale, Yicheng Gao, Zifeng Niu, Lulai Zhu
Layered queueing networks (LQNs) are an extension of ordinary queueing networks useful to model simultaneous resource possession and stochastic call graphs in distributed systems. Existing computational algorithms for LQNs have primarily focused on mean-value analysis. However, other solution paradigms, such as normalizing constant analysis and mean-field approximation, can improve the computation
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Contextual Ranking and Selection with Gaussian Processes and OCBA ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-11-20 Sait Cakmak, Yuhao Wang, Siyang Gao, Enlu Zhou
In many real world problems, we are faced with the problem of selecting the best among a finite number of alternatives, where the best alternative is determined based on context specific information. In this work, we study the contextual Ranking and Selection problem under a finite-alternative-finite-context setting, where we aim to find the best alternative for each context. We use a separate Gaussian
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Stochastic Approximation for Estimating the Price of Stability in Stochastic Nash Games ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-11-11 Afrooz Jalilzadeh, Farzad Yousefian, Mohammadjavad Ebrahimi
The goal in this paper is to approximate the Price of Stability (PoS) in stochastic Nash games using stochastic approximation (SA) schemes. PoS is amongst the most popular metrics in game theory and provides an avenue for estimating the efficiency of Nash games. In particular, knowing the value of PoS can help with designing efficient networked systems, including transportation networks and power market
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Toward Data Center Digital Twins via Knowledge-based Model Calibration and Reduction ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-10-26 Ruihang Wang, Deneng Xia, Zhiwei Cao, Yonggang Wen, Rui Tan, Xin Zhou
Computational fluid dynamics (CFD) models have been widely used for prototyping data centers. Evolving them into high-fidelity and real-time digital twins is desirable for the online operations of data centers. However, CFD models often have unsatisfactory accuracy and high computation overhead. Manually calibrating the CFD model parameters is tedious and labor-intensive. Existing automatic calibration
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Using Cache or Credit for Parallel Ranking and Selection ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-10-26 Harun Avci, Barry L. Nelson, Eunhye Song, Andreas Wächter
In this article, we focus on ranking and selection procedures that sequentially allocate replications to systems by applying some acquisition function. We propose an acquisition function, called gCEI, which exploits the gradient of the complete expected improvement with respect to the number of replications. We prove that the gCEI procedure, which adopts gCEI as the acquisition function in a serial
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SEH: Size Estimate Hedging Scheduling of Queues ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-10-26 Maryam Akbari-Moghaddam, Douglas G. Down
For a single server system, Shortest Remaining Processing Time (SRPT) is an optimal size-based policy. In this article, we discuss scheduling a single-server system when exact information about the jobs’ processing times is not available. When the SRPT policy uses estimated processing times, the underestimation of large jobs can significantly degrade performance. We propose an index-based policy with
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Performance Analysis of Work Stealing Strategies in Large-Scale Multithreaded Computing ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-10-26 Grzegorz Kielanski, Benny Van Houdt
Distributed systems use randomized work stealing to improve performance and resource utilization. In most prior analytical studies of randomized work stealing, jobs are considered to be sequential and are executed as a whole on a single server. In this article, we consider a homogeneous system of servers where parent jobs spawn child jobs that can feasibly be executed in parallel. When an idle server
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Compositional Safe Approximation of Response Time Probability Density Function of Complex Workflows ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-10-26 Laura Carnevali, Marco Paolieri, Riccardo Reali, Enrico Vicario
We evaluate a stochastic upper bound on the response time Probability Density Function (PDF) of complex workflows through an efficient and accurate compositional approach. Workflows consist of activities having generally distributed stochastic durations with bounded supports, composed through sequence, choice/merge, and balanced/unbalanced split/join operators, possibly breaking the structure of well-formed
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DSMC Evaluation Stages: Fostering Robust and Safe Behavior in Deep Reinforcement Learning – Extended Version ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-10-26 Timo P. Gros, Joschka Groß, Daniel Höller, Jörg Hoffmann, Michaela Klauck, Hendrik Meerkamp, Nicola J. Müller, Lukas Schaller, Verena Wolf
Neural networks (NN) are gaining importance in sequential decision-making. Deep reinforcement learning (DRL), in particular, is extremely successful in learning action policies in complex and dynamic environments. Despite this success, however, DRL technology is not without its failures, especially in safety-critical applications: (i) the training objective maximizes average rewards, which may disregard
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Optimizing Reachability Probabilities for a Restricted Class of Stochastic Hybrid Automata via Flowpipe Construction ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-10-26 Carina Da Silva, Stefan Schupp, Anne Remke
Stochastic hybrid automata (SHA) are a powerful tool to evaluate the dependability and safety of critical infrastructures. However, the resolution of nondeterminism, which is present in many purely hybrid models, is often only implicitly considered in SHA. This article instead proposes algorithms for computing maximum and minimum reachability probabilities for singular automata with urgent transitions
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Stochastic Approximation for Multi-period Simulation Optimization with Streaming Input Data ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-08-29 Linyun He, Uday V. Shanbhag, Eunhye Song
We consider a continuous-valued simulation optimization (SO) problem, where a simulator is built to optimize an expected performance measure of a real-world system while parameters of the simulator are estimated from streaming data collected periodically from the system. At each period, a new batch of data is combined with the cumulative data and the parameters are re-estimated with higher precision
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Learning to Simulate Sequentially Generated Data via Neural Networks and Wasserstein Training ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-08-10 Tingyu Zhu, Haoyu Liu, Zeyu Zheng
We propose a new framework of a neural network-assisted sequential structured simulator to model, estimate, and simulate a wide class of sequentially generated data. Neural networks are integrated into the sequentially structured simulators in order to capture potential nonlinear and complicated sequential structures. Given representative real data, the neural network parameters in the simulator are
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NIM: Generative Neural Networks for Automated Modeling and Generation of Simulation Inputs ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-08-10 Wang Cen, Peter J. Haas
Fitting stochastic input-process models to data and then sampling from them are key steps in a simulation study but highly challenging to non-experts. We present Neural Input Modeling (NIM), a Generative Neural Network (GNN) framework that exploits modern data-rich environments to automatically capture simulation input processes and then generate samples from them. The basic GNN that we develop, called
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DSMC Evaluation Stages: Fostering Robust and Safe Behavior in Deep Reinforcement Learning – Extended Version ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-07-12 Timo P. Gros, Joschka Groß, Daniel Höller, Jörg Hoffmann, Michaela Klauck, Hendrik Meerkamp, Nicola J. Müller, Lukas Schaller, Verena Wolf
Neural networks (NN) are gaining importance in sequential decision-making. Deep reinforcement learning (DRL), in particular, is extremely successful in learning action policies in complex and dynamic environments. Despite this success, however, DRL technology is not without its failures, especially in safety-critical applications: (i) the training objective maximizes average rewards, which may disregard
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Optimizing reachability probabilities for a restricted class of Stochastic Hybrid Automata via Flowpipe-Construction ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-07-11 Carina da Silva, Stefan Schupp, Anne Remke
Stochastic hybrid automata (SHA) are a powerful tool to evaluate the dependability and safety of critical infrastructures. However, the resolution of nondeterminism, which is present in many purely hybrid models, is often only implicitly considered in SHA. This paper instead proposes algorithms for computing maximum and minimum reachability probabilities for singular automata with urgent transitions
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Toward Data Center Digital Twins via Knowledge-based Model Calibration and Reduction ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-06-10 Ruihang Wang, Deneng Xia, Zhiwei Cao, Yonggang Wen, Rui Tan, Xin Zhou
Computational fluid dynamics (CFD) models have been widely used for prototyping data centers. Evolving them into high-fidelity and real-time digital twins is desirable for online operations of data centers. However, CFD models often have unsatisfactory accuracy and high computation overhead. Manually calibrating the CFD model parameters is tedious and labor-intensive. Existing automatic calibration
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Uncertainty-aware Simulation of Adaptive Systems ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-05-13 Jean-Marc Jézéquel, Antonio Vallecillo
Adaptive systems manage and regulate the behavior of devices or other systems using control loops to automatically adjust the value of some measured variables to equal the value of a desired set-point. These systems normally interact with physical parts or operate in physical environments, where uncertainty is unavoidable. Traditional approaches to manage that uncertainty use either robust control
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NIM: Generative Neural Networks for Automated Modeling and Generation of Simulation Inputs ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-04-19 Wang Cen, Peter J. Haas
Fitting stochastic input-process models to data and then sampling from them are key steps in a simulation study, but highly challenging to non-experts. We present Neural Input Modeling (NIM), a generative-neural-network (GNN) framework that exploits modern data-rich environments to automatically capture simulation input processes and then generate samples from them. The basic GNN that we develop, called
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Compositional safe approximation of response time probability density function of complex workflows ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-04-05 Laura Carnevali, Marco Paolieri, Riccardo Reali, Enrico Vicario
We evaluate a stochastic upper bound on the response time Probability Density Function (PDF) of complex workflows through an efficient and accurate compositional approach. Workflows consist of activities having generally distributed stochastic durations with bounded supports, composed through sequence, choice/merge, and balanced/unbalanced split/join operators, possibly breaking the structure of well-formed
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Learning to Simulate Sequentially Generated Data via Neural Networks and Wasserstein Training ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-04-03 Tingyu Zhu, Haoyu Liu, Zeyu Zheng
We propose a new framework of a neural network-assisted sequential structured simulator to model, estimate, and simulate a wide class of sequentially generated data. Neural networks are integrated into the sequentially structured simulators in order to capture potential nonlinear and complicated sequential structures. Given representative real data, the neural network parameters in the simulator are
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Estimating Multiclass Service Demand Distributions Using Markovian Arrival Processes ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-02-28 Runan Wang, Giuliano Casale, Antonio Filieri
Building performance models for software services in DevOps is costly and error-prone. Accurate service demand distribution estimation is critical to precisely modeling queueing behaviors and performance prediction. However, current estimation methods focus on capturing the mean service demand, disregarding higher-order moments of the distribution that still can largely affect prediction accuracy.
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Efficient Simulation of Sparse Graphs of Point Processes ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-02-28 Cyrille Mascart, David Hill, Alexandre Muzy, Patricia Reynaud-Bouret
We derive new discrete event simulation algorithms for marked time point processes. The main idea is to couple a special structure, namely the associated local independence graph, as defined by Didelez, with the activity tracking algorithm of Muzy for achieving high-performance asynchronous simulations. With respect to classical algorithms, this allows us to drastically reduce the computational complexity
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Batching Adaptive Variance Reduction ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-02-28 Chenxiao Song, Reiichiro Kawai
Adaptive Monte Carlo variance reduction is an effective framework for running a Monte Carlo simulation along with a parameter search algorithm for variance reduction, whereas an initialization step is required for preparing problem parameters in some instances. In spite of the effectiveness of adaptive variance reduction in various fields of application, the length of the preliminary phase has often
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Automatic Reuse, Adaption, and Execution of Simulation Experiments via Provenance Patterns ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-02-28 Pia Wilsdorf, Anja Wolpers, Jason Hilton, Fiete Haack, Adelinde Uhrmacher
Simulation experiments are typically conducted repeatedly during the model development process, for example, to revalidate if a behavioral property still holds after several model changes. Approaches for automatically reusing and generating simulation experiments can support modelers in conducting simulation studies in a more systematic and effective manner. They rely on explicit experiment specifications
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Replication of Computational Results Report for “Automatic Reuse, Adaption, and Execution of Simulation Experiments via Provenance Patterns” ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-02-28 Pierangelo Di Sanzo
In this article, a reproducibility study is presented, with reference to the computational results reported in the article “Automatic Reuse, Adaption, and Execution of Simulation Experiments via Provenance Patterns,” by P. Wilsdorf, A. Wolpers, J. Hilton, F. Haack, and A. M. Uhrmacher. Based on the achieved results, the Artifacts Available badge is assigned.
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A Personality-based Model of Emotional Contagion and Control in Crowd Queuing Simulations ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-02-28 Junxiao Xue, Mingchuang Zhang, Hui Yin
Queuing is a frequent daily activity. However, long waiting lines equate to frustration and potential safety hazards. We present a novel, personality-based model of emotional contagion and control for simulating crowd queuing. Our model integrates the influence of individual personalities and interpersonal relationships. Through the interaction between the agents and the external environment parameters
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Simulating the Impact of Dynamic Rerouting on Metropolitan-scale Traffic Systems ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-02-28 Cy Chan, Anu Kuncheria, Jane Macfarlane
The rapid introduction of mobile navigation aides that use real-time road network information to suggest alternate routes to drivers is making it more difficult for researchers and government transportation agencies to understand and predict the dynamics of congested transportation systems. Computer simulation is a key capability for these organizations to analyze hypothetical scenarios; however, the
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Introduction to the Special Section on PADS 2021 ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-02-16 Saikou Y. Diallo, Andreas Tolk
No abstract available.
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Performance analysis of work stealing strategies in large scale multi-threaded computing ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-02-16 Grzegorz Kielanski, Benny Van Houdt
Distributed systems use randomized work stealing to improve performance and resource utilization. In most prior analytical studies of randomized work stealing, jobs are considered to be sequential and are executed as a whole on a single server. In this paper we consider a homogeneous system of servers where parent jobs spawn child jobs that can feasibly be executed in parallel. When an idle server
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SEH: Size Estimate Hedging Scheduling of Queues ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-01-17 Maryam Akbari-Moghaddam, Douglas G. Down
For a single server system, Shortest Remaining Processing Time (SRPT) is an optimal size-based policy. In this paper, we discuss scheduling a single-server system when exact information about the jobs’ processing times is not available. When the SRPT policy uses estimated processing times, the underestimation of large jobs can significantly degrade performance. We propose an index-based policy with
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Virtual Time III, Part 1: Unified Virtual Time Synchronization for Parallel Discrete Event Simulation ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-01-11 David R. Jefferson, Peter Barnes
Algorithms for synchronization of parallel discrete event simulation have historically been divided between conservative methods that require lookahead but not rollback, and optimistic methods that require rollback but not lookahead. In this paper we present a new approach in the form of a framework called Unified Virtual Time (UVT) that unifies the two approaches, combining the advantages of both
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Virtual Time III, Part 2: Combining Conservative and Optimistic Synchronization ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-01-11 David R. Jefferson, Peter D. Barnes
This is Part 2 of a trio of works intended to provide a unifying framework in which conservative and optimistic synchronization for parallel discrete event simulations can be freely and transparently combined in the same logical process on an event-by-event basis. In this article, we continue the outline of an approach called Unified Virtual Time (UVT) that was introduced in Part 1, showing in detail
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Towards Differentiable Agent-Based Simulation ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2023-01-11 Philipp Andelfinger
Simulation-based optimization using agent-based models is typically carried out under the assumption that the gradient describing the sensitivity of the simulation output to the input cannot be evaluated directly. To still apply gradient-based optimization methods, which efficiently steer the optimization towards a local optimum, gradient estimation methods can be employed. However, many simulation
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A General Framework to Simulate Diffusions with Discontinuous Coefficients and Local Times ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2022-11-05 Kailin Ding, Zhenyu Cui
In this article, we propose an efficient general simulation method for diffusions that are solutions to stochastic differential equations with discontinuous coefficients and local time terms. The proposed method is based on sampling from the corresponding continuous-time Markov chain approximation. In contrast to existing time discretization schemes, the Markov chain approximation method corresponds
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Performance Analysis of Speculative Parallel Adaptive Local Timestepping for Conservation Laws ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2022-11-05 Maximilian Bremer, John Bachan, Cy Chan, Clint Dawson
Stable simulation of conservation laws, such as those used to model fluid dynamics and plasma physics applications, requires the satisfaction of the so-called Courant-Friedrichs-Lewy condition. By allowing regions of the mesh to advance with different timesteps that locally satisfy this stability constraint, significant work reduction can be attained when compared to a time integration scheme using
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Dynamic Data-driven Microscopic Traffic Simulation using Jointly Trained Physics-guided Long Short-Term Memory ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2022-11-05 Htet Naing, Wentong Cai, Hu Nan, Wu Tiantian, Yu Liang
Symbiotic simulation systems that incorporate data-driven methods (such as machine/deep learning) are effective and efficient tools for just-in-time (JIT) operational decision making. With the growing interest on Digital Twin City, such systems are ideal for real-time microscopic traffic simulation. However, learning-based models are heavily biased towards the training data and could produce physically
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Bayesian Optimisation vs. Input Uncertainty Reduction ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2022-07-25 Juan Ungredda, Michael Pearce, Juergen Branke
Simulators often require calibration inputs estimated from real-world data, and the estimate can significantly affect simulation output. Particularly when performing simulation optimisation to find an optimal solution, the uncertainty in the inputs significantly affects the quality of the found solution. One remedy is to search for the solution that has the best performance on average over the uncertain
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A New Test for Hamming-Weight Dependencies ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2022-07-25 David Blackman, Sebastiano Vigna
We describe a new statistical test for pseudorandom number generators (PRNGs). Our test can find bias induced by dependencies among the Hamming weights of the outputs of a PRNG, even for PRNGs that pass state-of-the-art tests of the same kind from the literature, and particularly for generators based on F2-linear transformations such as the dSFMT [22], xoroshiro1024+ [1], and WELL512 [19].
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The DEVStone Metric: Performance Analysis of DEVS Simulation Engines ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2022-07-25 Román Cárdenas, Kevin Henares, Patricia Arroba, José L. Risco-Martín, Gabriel A. Wainer
The DEVStone benchmark allows us to evaluate the performance of discrete-event simulators based on the Discrete Event System (DEVS) formalism. It provides model sets with different characteristics, enabling the analysis of specific issues of simulation engines. However, this heterogeneity hinders the comparison of the results among studies, as the results obtained on each research work depend on the
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Rare-event Simulation for Neural Network and Random Forest Predictors ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2022-07-06 Yuanlu Bai, Zhiyuan Huang, Henry Lam, Ding Zhao
We study rare-event simulation for a class of problems where the target hitting sets of interest are defined via modern machine learning tools such as neural networks and random forests. This problem is motivated from fast emerging studies on the safety evaluation of intelligent systems, robustness quantification of learning models, and other potential applications to large-scale simulation in which
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Replicated Computational Results (RCR) Report for “A New Test for Hamming-Weight Dependencies” ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2022-07-06 Xiaoliang Wu, Dong Jin
In the paper “A New Test for Hamming-Weight Dependencies”, Blackman and Vigna propose a new statistical test for pseudorandom number generators (PRNG). Compared with the state-of-the-art tests, the proposed test could find statistical bias in the Hamming weights of the output of the generator. The proposed test is evaluated by using generators in TestU01 [2]. The authors provide the C code used in
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The DEVStone Metric: Performance Analysis of DEVS Simulation Engines ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2022-06-11 Román Cárdenas, Kevin Henares, Patricia Arroba, José L. Risco-Martín, Gabriel A. Wainer
The DEVStone benchmark allows us to evaluate the performance of discrete-event simulators based on the DEVS formalism. It provides model sets with different characteristics, enabling the analysis of specific issues of simulation engines. However, this heterogeneity hinders the comparison of the results among studies, as the results obtained on each research work depend on the chosen subset of DEVStone
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Drawing Random Floating-point Numbers from an Interval ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2022-04-11 Frédéric Goualard
Drawing a floating-point number uniformly at random from an interval [a, b) is usually performed by a location-scale transformation of some floating-point number drawn uniformly from [0, 1). Due to the weak properties of floating-point arithmetic, such a transformation cannot ensure respect of the bounds, uniformity or spatial equidistributivity. We investigate and quantify precisely these shortcomings
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A New Test for Hamming–Weight Dependencies ACM Trans. Model. Comput. Simul. (IF 0.9) Pub Date : 2022-03-28 David Blackman, Sebastiano Vigna
We describe a new statistical test for pseudorandom number generators (PRNGs). Our test can find bias induced by dependencies among the Hamming weights of the outputs of a PRNG, even for PRNGs that pass state-of-the-art tests of the same kind from the literature, and in particular for generators based on F2-linear transformations such as the dSFMT [22], xoroshiro1024+ [1], and WELL512 [19].