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On Automated Memoization in the Field of Simulation Parameter Studies

Processes in computer simulations tend to be highly repetitive. In particular, parameter studies further exasperate the situation as the same model is... (more)

Combining Simulation and Emulation Systems for Smart Grid Planning and Evaluation

Software-defined networking (SDN) enables efficient network management. As the technology matures, utilities are looking to integrate those benefits... (more)

A Role-Dependent Data-Driven Approach for High-Density Crowd Behavior Modeling

In this article, we propose a role-dependent (RD) data-driven modeling approach to simulate pedestrians’ motion in high-density scenes. It is... (more)

Modeling Large-Scale Slim Fly Networks Using Parallel Discrete-Event Simulation

As supercomputers approach exascale performance, the increased number of processors translates to an increased demand on the underlying network... (more)

NeMo: A Massively Parallel Discrete-Event Simulation Model for Neuromorphic Architectures

Neuromorphic computing is a broad category of non–von Neumann architectures that mimic biological nervous systems using hardware. Current research shows that this class of computing can execute data classification algorithms using only a tiny fraction of the power conventional CPUs require. This raises the larger research question: How... (more)

NEWS

Reproducibility Board

June 2018

TOMACS establishes a reproducibility board. Its members will take an active role in reproducing computational results and in evaluating modeling and simulation artifacts, as well as in fine tuning the overall process. 

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MS:STAROC A Survey of Statistical Model Checking published

February 2018

In the TOMACS series  "State of the Art and Open Challenges" (MS:STAROC) the paper "A Survey of Statistical Model Checking", by Gul Agha and Karl Palmskog has been published.

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Special Issue: Toward an Ecosystem of Models and Data

January 2018

A special issue based on the 2017 Informs Simulation Society Research Workshop (I-Sim 2017) will appear in 2019, guest edited by Peter J. Haas and Georgios Theodoropoulos.

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Forthcoming Articles

Replicated Computations Results (RCR) Report for "Fast Random Integer Generation in an Interval"

Fast Random Integer Generation in an Interval

In simulations, probabilistic algorithms and statistical tests, we often generate random integers in an interval (e.g., [0,s)). For example, random integers in an interval are essential to the Fisher-Yates random shuffle. Consequently, popular languages like Java, Python, C++, Swift and Go include ranged random integer generation functions as part of their runtime libraries. Pseudo-random values are usually generated in words of a fixed number of bits (e.g., 32~bits, 64~bits) using algorithms such as a linear congruential generator. We need functions to convert such random words to random integers in an interval ($[0,s)$) without introducing statistical biases. The standard functions in programming languages such as Java or Go involve integer divisions. Unfortunately, division instructions are relatively expensive. We review an unbiased function to generate ranged integers from a source of random words that avoids integer divisions with high probability. To establish the practical usefulness of the approach, we show that this algorithm can multiply the speed of unbiased random shuffling on x64 processors. Our proposed approach has been adopted by the Go language for its implementation of the shuffle function.

Efficient Parallel Simulation over Large-scale Social Contact Networks

Social contact network (SCN) models the daily contacts between people in real life. It consists of agents and locations. When agents visit a location at the same time, the social interactions can be established among them. Simulations over SCN have been employed to study social dynamics such as disease spread among population. Because of the scale of SCN and the execution time requirement, the simulations are usually run in parallel. However, a challenge to the parallel simulation is that the structure of SCN is naturally skewed with a few hub locations that have far more visitors than others. These hub locations can cause load imbalance and heavy communication between partitions, which therefore impact the simulation performance. This paper proposes a comprehensive solution to address this challenge. Firstly, the hub locations are decomposed into small locations, so that SCN can be divided into partitions with better balanced workloads. Secondly, the agents are decomposed to exploit data locality, so that the overall communication across partitions can be greatly reduced. Thirdly, two enhanced execution mechanisms are designed for locations and agents respectively to improve simulation parallelism. To evaluate the efficiency of the proposed solution, an epidemic simulation was developed and extensive experiments were conducted on two computer clusters using three SCN data sets with different scales. The results demonstrate that our approach can significantly improve the execution performance of the simulation.

Visual Analytics to Identify Temporal Patterns and Variability in Simulations from Cellular Automata

Cellular Automata (CA) models are discrete simulation models, in which a collection of cells placed in a regular spatial configuration change state over a number of time steps. Running an experiment produces spatio-temporal data and multi-run data (as multiple runs are employed for stochastic models). Visually navigating such data proves arduous, as the commonly employed slider-based visualizations offer little support to identify temporal trends or assess where a model's uncertainty may be excessive. In this paper, we designed and implemented a new visualization environment dealing with the spatio-temporal, multi-run data produced by CA. This new environment uses several linked visualizations, and focuses on the identifying of temporal trends and uncertainty. We conducted an empirical evaluation of this new environment to (i) assess whether important tasks for modelers can be performed efficiently with this environment, (ii) examine how performances are influenced by key simulation factors, and (iii) identify whether modelers can use the familiar slider-based visualization together with our new environment. Our results shows that participants were confident on results obtained using our new environment. They were also able to accomplish tasks without taking longer than they would with current solutions. Our qualitative analysis found that some participants saw value switching between our proposed visualization and the commonly used slider-based version. In addition, we noted that errors were affected not only by the type of visualizations by also by specific features of the simulations. Future work should explore combining and adapting these visualizations depending on salient parameters from the simulations.

Managing Pending Events in Sequential & Parallel Simulations using 3-Tier Heap & 2-Tier-Ladder Queue

Performance of sequential and parallel Discrete Event Simulation (DES) is strongly influenced by the data structure used for managing and processing pending events. Accordingly, we propose and evaluate the effectiveness of our multi-tiered (2 and 3 tier) data structures and our 2-tier Ladder Queue, for both sequential and optimistic parallel simulations on distributed memory platforms. Our experiments compare the performance of our data structures against a performance-tuned version of the Ladder Queue, which has shown to outperform many other data structures for DES. The core simulation-based empirical assessments are in C++ and are based on 2,500 configurations of well-established PHOLD and PCS benchmarks. We have conducted analyses on two computing clusters with different hardware to ensure our results are reproducible. Moreover, to fully establish the robustness of our analysis and data structures, we have also implemented pertinent queues in Java and verified consistent, reproducible performance characteristics. Collectively, our analyses show that our 3-tier heap and 2-tier ladder queue outperform the Ladder Queue by 60× in some simulations, particularly those with higher concurrency per Logical Process (LP), in both sequential and Time Warp synchronized parallel simulations.

Ranking and Selection: A New Sequential Bayesian Procedure for Use with Common Random Numbers

We introduce a new concept for selecting the best alternative out of a given set of systems which are evaluated with respect to their expected performances. We assume that the systems are simulated on a computer and that a joint observation of all systems has a multivariate normal distribution with \emph{unknown mean} and \emph{unknown covariance} matrix. In particular, the observations of the systems may be stochastically dependent as it is the case if common random numbers are used for the simulation. The main application we have in mind is heuristic stochastic optimization where systems are different solutions to an optimization problem with random inputs. We repeatedly allocate a fixed budget of simulation runs to the alternatives. We use a Bayesian setup with an uninformative prior and allocate the simulation budget based on the posterior distribution of the observations until the ranking and selection decision is correct with a given high probability. We introduce a new simple allocation strategy that is directly connected to the error probabilities calculated before. The necessary posterior distributions can only be approximated, but we give extensive empirical evidence that the error made is well below the given bounds. Our extensive test results show that our procedure \BayesRS uses less simulations than comparable procedures from the literature in different correlation scenarios. At the same time \BayesRS needs no additional prior parameters and can cope with different types of ranking and selection tasks.

Guest Editorial for the TOMACS Special Issue on the Principles of Advanced Discrete Simulation (PADS) 2016

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