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Urban traffic simulation of extensive areas with complex driver models poses a significant computational challenge. Developing highly scalable parallel simulation algorithms is the only feasible way to provide useful results in this case. In this paper, we present extensions of the SMARTS system, a traffic simulation tool, which provides efficient scalability with a large number of parallel processes. The presented extensions enabled its scalability for HPC-grade systems. The extended version has been thoroughly tested in strong and weak scalability scenarios for up to 2400 computing cores of a supercomputer. The satisfactory scalability has been achieved by introducing several significant improvements, which have been discussed in details.

Detailed models of numerous groups of social beings, which find applications in broad range of applications, require efficient methods of parallel simulation. Detailed features of particular models strongly influence the complexity of the parallelization problem. In this paper we identify and analyze existing classes of models and possible approaches to their simulation parallelization. We propose a new method for efficient scalability of the most challenging class of models: stochastic, with beings mobility and mutual exclusion of actions. The method is based on a concept of two-stage application of plans, which ensures equivalence of parallel and sequential execution. The method is analyzed in terms of distribution transparency and scalability at HPC-grade hardware. Both weak and strong scalability tests show speedup close to linear with more than 3000 parallel workers.

The IEEE SystemC language is widely used in industry and academia to model and simulate system-level designs. Despite the availability of multi- and many-core host processors, however, the Accellera reference simulator is still based on sequential discrete event simulation, utilizing only a single core at any time. While many advanced parallel simulation approaches have been proposed, most require modification of the SystemC source code so that the model is free from parallel access conflicts and rely on the designer to manually perform this difficult transformation.

The methods known collectively as “ranking & selection” have been a theoretical and practical success story for the optimization of simulated stochastic systems: they are widely used in practice, have been implemented in commercial simulation software, and research has made them more and more statistically efficient. However, “statistically efficient” has meant minimizing the number of simulation-generated observations required to make a selection, or maximizing the strength of the inference given a budget of observations. Exploiting high-performance computing, and specifically the capability to simulate many feasible solutions in parallel, has challenged the ranking & selection paradigm. In this paper we review the challenge and suggest an entirely different approach.

This paper presents Spike - a command line tool for continuous, stochastic & hybrid simulation of biochemical reaction networks represented as (coloured) Petri nets. It supports import from and export to various Petri net data formats, and also imports SBML models. Spike’s abilities include the configuration of models by changing stoichiometries (arc weights), initial conditions (markings) and kinetic parameters. It also unfolds coloured stochastic/continuous/hybrid Petri nets. To comply with the demand for reproducible simulation experiments, Spike builds on a script language in a human-readable format. Its core features permit the design of a set of simulation experiments by a single configuration file. These simulation experiments can be executed in parallel on a multi-core machine; distributed execution is in preparation.

Models of information spread in online social networks (OSNs) are in high demand these days. Most of them consider peer-to-peer interaction on a predefined topology of friend network. However, in particular types of OSNs the largest information cascades are observed during the community-user interaction when communities play the role of superspreaders for their audience. In the paper, we consider the problem of the parallel simulation of community-wide information spreading in large-scale (up to dozens of millions of nodes) networks. The efficiency of parallel algorithm is studied for synthetic and real-world social networks from VK.com using the Lomonosov supercomputer (Moscow State University, Russian Federation).

To enable the systematic evaluation of complex technical systems by engineers from various disciplines advanced 3D simulation environments that model all relevant aspects are used. In these Virtual Testbeds real-time capabilities are very hard to achieve without applying multi-threading strategies, due to the high complexity of the simulation. We present a novel simulation architecture that facilitates a modular approach to perform parallel simulations of arbitrary environments, where no explicit knowledge of the underlying simulation algorithms or model partitioning is needed by the engineer. Simulation components such as rigid-body dynamics, kinematics, renderer, controllers etc. can be distributed among different threads without concern about the specific technical realization. We achieve this by managing self-contained independent copies of the simulation model, each bound to one thread.

Monte-Carlo rendering algorithms are known for producing highly realistic images, but at a significant computational cost, because they rely on tracing up to trillions of light paths through a scene to simulate physically based light transport. For this reason, a large body of research exists on various techniques for accelerating these costly algorithms. As one of the Monte-Carlo rendering algorithms, PSSMLT (Primary Sample Space Metropolis Light Transport) is widely used nowadays for photorealistic rendering. Unfortunately, the computational cost of PSSMLT is still very high since the space of light paths in high-dimension and up to trillions of paths are typically required in such path space. Recent research on PSSMLT has proposed a variety of optimized methods for single node rendering, however, multi-node rendering for PSSMLT is rarely mentioned due in large part to the complicated mathematical model, complicated physical processes and the irregular memory access patterns, and the imbalanced workload of light-carrying paths.

Agent-based models (ABMs) need to populate a mega number of agents over a scalable simulation space in order to handle practical problems, (e.g., metropolitan traffic simulation and nationwide epidemic prediction). Although parallel and distributed simulation have steadily addressed their computational needs, non-computing scientists still tend to use GUI-rich, easy-to-use ABM interpretive platforms. This paper intends to identify the difficulty in using the current parallel ABM simulators and to propose their future improvements. For this purpose, we surveyed different ABM applications, modeled them as seven benchmark test cases, used them to analyze the agent descriptivity of parallel ABM simulators, and evaluated their execution performance affected by the current implementations.

This paper reports about a software package which has been developed to automatically partition hydrological networks (catchments) into clusters of similar size. Such clustering is useful for parallel simulation of catchments on distributed computing systems and is typically based on heuristic graph algorithms.

This paper describes Alya Red CCM, a cardiac computational modelling tool for supercomputers. It is based on Alya, a parallel simulation code for multiphysics and multiscale problems, which can deal with all the complexity of biological systems simulations. The final goal is to simulate the pumping action of the heart: the model includes the electrical propagation, the mechanical contraction and relaxation and the blood flow in the heart cavities and main vessels. All sub-problems are treated as fully transient and solved in a staggered fashion. Electrophysiology and mechanical deformation are solved on the same mesh, with no interpolation. Fluid flow is simulated on a moving mesh using an Arbitrary Lagrangian-Eulerian (ALE) strategy, being the mesh deformation computed through an anisotropic Laplacian equation. The parallel strategy is based on an automatic mesh partition using Metis and MPI tasks. When required and in order to take profit of multicore clusters, an additional OpenMP parallelization layer is added. The paper describes the tool and its different parts. Alya’s flexibility allows to easily program a large variety of physiological models for each of the sub-problems, including the mutual coupling. This flexibility, added to the parallel efficiency to solve multiphysics problems in complex geometries render Alya Red CCM a well suited tool for cardiac biomedical research at either industrial or academic environments.

In order to utilize parallel processing for low power and high performance in embedded systems, ESL models must contain explicit and efficient parallelism.

Dynamic Fault Trees (DFT) are a generalization of Fault Trees which allow the evaluation of the reliability of complex and redundant systems. We propose to analyze DFT by a new version of time-parallel simulation method we have recently introduced. This method takes into account the monotonicity of the sample-paths to derive upper and lower bounds of the paths which become tighter when we increase the simulation time. As some gates of the DFT are not monotone, we adapt our method.

The paper is an analysis of a parallelized simulation of solar halo phenomena based on an algorithm of inverse ray tracing with the use of Monte Carlo methods. In addition to the description of the basis for the phenomena the simulation algorithm is also discussed. In order to present it in an accessible way, a sequential algorithm is described at first and it is followed by its parallelization method. Both sequential and parallelized approaches are already implemented in author’s solar halo effect simulators so that the practical use of these methods constitutes an excellent base to carry out useful comparisons of performance and quality in the context of obtaining very subtle details in varying types of solar halo phenomena.

The compact Discontinuous Galerkin 2 (CDG2) method was successfully tested for elliptic problems, scalar convection-diffusion equations and compressible Navier-Stokes equations. In this paper we use the newly developed DG method to solve a mathematical model for early stages of atherosclerotic plaque formation. Atherosclerotic plaque is mainly formed by accumulation of lipid-laden cells in the arterial walls which leads to a heart attack in case the artery is occluded or a thrombus is built through a rupture of the plaque. After describing a mathematical model and the discretization scheme, we present some benchmark tests comparing the CDG2 method to other commonly used DG methods. Furthermore, we take parallelization and higher order discretization schemes into account.