Simulation Of Large-scale Systems Research Articles

Accelerating UNISIM-Based Cycle-Level Microarchitectural Simulations on Multicore Platforms

UNISIM has been shown to ease the development of simulators for multi-/many-core systems. However, UNISIM cycle-level simulations of large-scale multiprocessor systems could be very time consuming. In this article, we propose a systematic framework for accelerating UNISIM cycle-level simulations on multicore platforms. The proposed framework relies on exploiting the fine-grained parallelism within the simulated cycles using POSIX threads. A multithreaded simulation engine has been devised from the single-threaded UNISIM SystemC engine to facilitate the exploitation of inherent parallelism. An adaptive technique that manages the overall computation workload by adjusting the number of threads employed at any given time is proposed. In addition, we have introduced a technique to balance the workloads of multithreaded executions. This load balancing involves the distributions of SystemC objects among threads. A graph-partitioning-based technique has been introduced to automate such distributions. Finally, two strategies are proposed for realizing nonautomated and fully automated adaptive multithreaded simulations, respectively. Our investigations show that notable acceleration can be achieved by deploying the proposed framework. In particular, we show that simulations on an 8-core multicore platform can provide for close to 6X speedups when simulating many-core systems with large number of cores.

Hierarchical 3D multiphysics modelling in the design and optimisation of SOFC system components

This paper presents a novel bottom-up modelling approach to aid in the design and optimisation of the Research Centre Jülich type integrated module components. The approach is demonstrated by employing the air pre-heater component. A feasibility study considering the thermo fluid, thermo mechanical behaviour of the physical air pre-heater design compared to a simplified design is introduced. Analogue design simplifications are performed to the afterburner and pre-reformer components. The results reveal that a simplified design can be feasible for thermo fluid flow analyses, but are not representative for mechanical analyses. The integrated module considering the simplified system components is simulated in 3D, considering the multiphysics that occur within each component. The predictions of the air pre-heater component obtained from the integrated module analysis are compared to the stand-alone air pre-heater simulation results. The results are in very good agreement. The approach is proven to be useful for the optimisation of the integrated module. Moreover, the investigation of local processes, critical regions can be visualised. The methodology demonstrates the effective simulation of large scale systems in 3D.

10.2478/s11814-009-0220-9

The hydrodynamics of a bubbling gas-solid fluidized bed of 57.4 μm FCC particles was simulated by using a state-of-the-art two-fluid model integrating the kinetic theory of granular flow for particulate phase stresses. The overestimation of the bed expansion was resolved by using a suitable scale factor in the drag model as suggested by McKeen and Pugsley (T.R. McKeen, T.S. Pugsley, Powder Technol., 129, 139 (2003)). This study showed that the method was appropriate in simulation of a gas-solid fluidized bed of Geldart A particles at high gas velocities (0.3 to 0.61 m/s). The reduction of computational time especially for simulation of large-scale systems was achieved. The time-averaged local voidage was compared with the experimental data and the trend of varying several parameters on the hydrodynamic of the bed was investigated. The simulation results showed both qualitative and quantitative agreement with the literature.

On Markovian approximations for virtual-build-to-order systems

The Virtual Build-to-Order (VBTO) approach strives to allow a producer to fulfil customers with the specific product variants they seek more efficiently than a conventional order fulfilment system. It does so by opening the planning pipeline. Here the feasibility of modelling the VBTO system as a Markov process is investigated. Two system configurations are considered—a random pipeline feed policy that assumes only knowledge of the overall demand pattern and an informed policy that ensures a mix of different variants in the system. First-order Markov models, which assume stationarity requirements are satisfied, are developed for small VBTO systems. The model for the informed feed policy has excellent agreement with simulation results and confirms the superiority of this policy over the random policy. The model for the random policy is more accurate at high variety than at low variety levels. Accuracy is improved with a second-order Markov model. Although impractical for modelling large scale VBTO systems for either configuration, the Markov approach is valuable in providing insights, theoretical foundations and validation for simulation models. It aids the interpretation of observations from simulations of large scale systems and explains the mechanism by which an unrepresentative stock mix develops over time for the random policy.

Modeling the Large-Scale Device Collaboration System Based on PI-Calculus

The modeling and simulation of large-scale Device Collaboration systems is an important issue for seamless integration and collaboration of distributed device integration. Based on the advantage of PI-Calculus in modeling concurrent and dynamic systems, a method concentrating on the modeling of large-scale Device Collaboration system is presented. Firstly, it transforms device behaviors and interactions into PI-Calculus processes. Secondly, weak dissimulation of multiple internal control flows is defined through the application of behavior equivalent theory of PI-Calculus. Following that, external behavior equivalence of system dynamic ontology is discussed. With the help of operational and transitional semantics and a formal deduction, our method can discover system problems in design stage, thus improving architecture correctness and execution stability of Device Collaboration systems.

Information models for queueing system simulation

When planning simulations of large-scale systems, it is important to anticipate what information is required to model the system and obtain desired output. This can be done without tying the study to a specific simulation package or language. It is valuable to do so to avoid unnecessarily long development and execution times. In this article, we offer a simulation information model (SIM) designed to help organize system information in the early stages of a project. (It can also be used to analyze existing models.) The SIM allows complexity analysis of the system to be performed, and may lead to a better selection of simulation language. The SIM is illustrated using two examples, and its relationship to current formalisms is discussed.

CFD simulation of gas-solid bubbling fluidized bed containing FCC particles

The hydrodynamics of a bubbling gas-solid fluidized bed of 57.4 μm FCC particles was simulated by using a state-of-the-art two-fluid model integrating the kinetic theory of granular flow for particulate phase stresses. The overestimation of the bed expansion was resolved by using a suitable scale factor in the drag model as suggested by McKeen and Pugsley (T.R. McKeen, T.S. Pugsley, Powder Technol., 129, 139 (2003)). This study showed that the method was appropriate in simulation of a gas-solid fluidized bed of Geldart A particles at high gas velocities (0.3 to 0.61 m/s). The reduction of computational time especially for simulation of large-scale systems was achieved. The time-averaged local voidage was compared with the experimental data and the trend of varying several parameters on the hydrodynamic of the bed was investigated. The simulation results showed both qualitative and quantitative agreement with the literature.

Dynamic Region Matching Algorithm Based on Index-Order

The HLA Data Distribution Management (DDM) service provides an abstract,application-driven data filtering capability. It can reduce the transmission and reception of irrelevant data. The key problem of its implementation is region matching algorithm. The current algorithms such as directly matching,grid-based,and sort-based approach are all not so perfect. They are either time consuming or inaccuracy in filtering,and are difficult to support large-scale simulation. Aiming at the characteristic of frequently region changing in large-scale simulation systems,a more effective matching algorithm based on intersecting information of region moving is proposed. It represents the upper bound and lower bound of a range with two nodes,and uses two indexed ordered tables to store the publication and subscription nodes of a dimension respectively. It uses range intersecting information during a range moving,and limits the matching computing only in the area of moving. Thereby it can greatly decrease the candidate ranges that need to do matching computing,thus can decrease the matching complexity greatly,and reach precise matching with high performance. This algorithm is extremely fit for the need of large-scale distributed simulation that has a large number of regions.

Practical recipes for the model order reduction, dynamical simulation and compressive sampling of large-scale open quantum systems

Practical recipes are presented for simulating high-temperature and nonequilibrium quantum spin systems that are continuously measured and controlled. The notion of a spin system is broadly conceived, in order to encompass macroscopic test masses as the limiting case of large-j spins. The simulation technique has three stages: first the deliberate introduction of noise into the simulation, then the conversion of that noise into an equivalent continuous measurement and control process, and finally, projection of the trajectory onto state-space manifolds having reduced dimensionality and possessing a Kähler potential of multilinear algebraic form. These state-spaces can be regarded as ruled algebraic varieties upon which a projective quantum model order reduction (MOR) is performed. The Riemannian sectional curvature of ruled Kählerian varieties is analyzed, and proved to be non-positive upon all sections that contain a rule. These manifolds are shown to contain Slater determinants as a special case and their identity with Grassmannian varieties is demonstrated. The resulting simulation formalism is used to construct a positive P-representation for the thermal density matrix. Single-spin detection by magnetic resonance force microscopy (MRFM) is simulated, and the data statistics are shown to be those of a random telegraph signal with additive white noise. Larger-scale spin-dust models are simulated, having no spatial symmetry and no spatial ordering; the high-fidelity projection of numerically computed quantum trajectories onto low dimensionality Kähler state-space manifolds is demonstrated. The reconstruction of quantum trajectories from sparse random projections is demonstrated, the onset of Donoho–Stodden breakdown at the Candès–Tao sparsity limit is observed, a deterministic construction for sampling matrices is given and methods for quantum state optimization by Dantzig selection are given.

Recent advances in large-scale atomistic and coarse-grained molecular dynamics simulation of clay minerals

We review the recent advances in large-scale and coarse-grained molecular dynamics applied to clay minerals. Recent advances in local and distributed high performance computational resources together with the development of efficient parallelized algorithms has enabled the simulation of increasingly realistic large-scale models of clay mineral systems. Using this improved technology, it is becoming possible to simulate realistic clay platelet sizes at an atomistic level. This has considerably extended the spatial dimensions of microscopic simulation into a domain normally encountered in mesoscopic simulation. The simulation of large-scale model systems is important to further study complex phenomena, such as the structural and mechanical properties of disordered layered materials such as clays. In order to achieve even larger length and longer time-scales coarse-grained methods are increasingly employed, capturing phenomena such as composite failure modes and intercalation.

The PERPLEXUS bio-inspired hardware platform: A flexible and modular approach

This paper introduces the Perplexus hardware platform, a scalable computing substrate made of custom reconfigurable devices endowed with bio-inspired capabilities. This platform will enable the simulation of large-scale complex systems and the study

Area aggregation and time-scale modeling for sparse nonlinear networks

Model reduction and aggregation are of key importance for simulation and analysis of large-scale systems, such as molecular dynamics, large swarms of robotic vehicles, and animal aggregations. We study a nonlinear network which exhibits areas of internally dense and externally sparse interconnections. The densely connected nodes in these areas synchronize in the fast time-scale, and behave as aggregate nodes that dominate the slow dynamics of the network. We first derive a singular perturbation model which makes this time-scale separation explicit and, next, prove the validity of the reduced-model approximation on the infinite time interval.

FASE: A Framework for Scalable Performance Prediction of HPC Systems and Applications

As systems of computers become more compleX in terms of their architecture, interconnect and heterogeneity, the optimum configuration and utilization of these machines becomes a major challenge. To reduce the penalties caused by poorly configured systems, simulation is often used to predict the performance of key applications to be eXecuted on the new systems. Simulation provides the capability to observe component and system characteristics (e.g. performance and power) in order to make vital design decisions. However, simulating high-fidelity models can be very time consuming and even prohibitive when evaluating large-scale systems. The Fast and Accurate Simulation Environment (FASE) framework seeks to support large-scale system simulation by using high-fidelity models to capture the behavior of only the performance-critical components while employing abstraction techniques to capture the effects of those components with little impact on the system. In order to achieve this balance of accuracy and simulation speed, FASE provides a methodology and associated toolset to evaluate numerous architectural options. This approach allows users to make system design decisions based on quantifiable demands of their key applications rather than using manual analysis which can be error prone and impractical for large systems. The framework accomplishes this evaluation through a novel approach of combining discrete-event simulation with an application characterization scheme in order to remove unnecessary details while focusing on components critical to the performance of the application. In this paper, we present the methodology and techniques behind FASE and include several case studies validating systems constructed using various applications and interconnects.

Preface: New Challenges in Large-Scale Computer Systems and Network Modeling and Simulation

Preface: New Challenges in Large-Scale Computer Systems and Network Modeling and Simulation

Effectiveness factor approximations for multiple steady states in porous catalysts

The effectiveness factor is formally determined by solving a two-point boundary value problem, often numerically. To enhance the computational efficiency in simulations of large-scale reactor systems with porous catalysts, a simple approximation formula for the effectiveness factor is often used. For some reaction rate functions, however, the effectiveness factor as a function of Thiele modulus can show multiple values or sharp changes for a small change in the modulus. In this case, single-valued approximations of the effectiveness factor may give rise to large errors. Based on the two well-known asymptotes of the effectiveness factor for small and large Thiele moduli, we proposed equations for the approximation of the effectiveness factor for up to three multiple steady states and two catalyst geometries of an infinite slab and a sphere. The proposed equations were demonstrated to be useful in estimating the effectiveness factor, particularly for the stable steady states, and also in quickly estimating the Thiele modulus range where multiple effectiveness factors should be searched.

Event-driven simulation of neural population synchronization facilitated by electrical coupling

Most neural communication and processing tasks are driven by spikes. This has enabled the application of the event-driven simulation schemes. However the simulation of spiking neural networks based on complex models that cannot be simplified to analytical expressions (requiring numerical calculation) is very time consuming. Here we describe briefly an event-driven simulation scheme that uses pre-calculated table-based neuron characterizations to avoid numerical calculations during a network simulation, allowing the simulation of large-scale neural systems. More concretely we explain how electrical coupling can be simulated efficiently within this computation scheme, reproducing synchronization processes observed in detailed simulations of neural populations.

Strategies and Tactics in Multiscale Modeling of Cell-to-Organ Systems

Modeling is essential to integrating knowledge of human physiology. Comprehensive self-consistent descriptions expressed in quantitative mathematical form define working hypotheses in testable and reproducible form, and though such models are always "wrong" in the sense of being incomplete or partly incorrect, they provide a means of understanding a system and improving that understanding. Physiological systems, and models of them, encompass different levels of complexity. The lowest levels concern gene signaling and the regulation of transcription and translation, then biophysical and biochemical events at the protein level, and extend through the levels of cells, tissues and organs all the way to descriptions of integrated systems behavior. The highest levels of organization represent the dynamically varying interactions of billions of cells. Models of such systems are necessarily simplified to minimize computation and to emphasize the key factors defining system behavior; different model forms are thus often used to represent a system in different ways. Each simplification of lower level complicated function reduces the range of accurate operability at the higher level model, reducing robustness, the ability to respond correctly to dynamic changes in conditions. When conditions change so that the complexity reduction has resulted in the solution departing from the range of validity, detecting the deviation is critical, and requires special methods to enforce adapting the model formulation to alternative reduced-form modules or decomposing the reduced-form aggregates to the more detailed lower level modules to maintain appropriate behavior. The processes of error recognition, and of mapping between different levels of model complexity and shifting the levels of complexity of models in response to changing conditions, are essential for adaptive modeling and computer simulation of large-scale systems in reasonable time.

Automatically building customized circuit-based simulation models using symbolic computing

Nowadays, most scientists and engineers rely on computer simulations to analyze, design, and prototype complex systems. Scientific and engineering system models are implemented in a variety of simulation environments; however, limited model libraries are often provided to users who are compelled to create their own customized models. Developing customized simulation models is a time-consuming and error-prone task which requires software developer skills. The difficulty of creating customized models presents the need for user-friendly tools that assist users in their code development process. This paper describes a tool named RCMAG that automatically builds customized models for the Virtual Test Bed (VTB) [ http://vtb.engr.sc.edu/, The Virtual Test Bed Overview, Architecture, and Downloads] simulation environment. VTB is an interactive software system that provides a problem-solving environment (PSE) for simulation, prototyping, and advanced visualization of large-scale multi-disciplinary systems. VTB complies with a simulation standard named VHDL-AMS to enforce natural (circuit), signal, and data coupling between entities from diverse disciplines. The PSE tool RCMAG automatically builds naturally coupled or circuit-based VTB entities from input data provided by the user. RCMAG accepts both numeric and symbolic input data from the user, manages it to create code, and releases the compiled model object, which is ready to be added to the VTB model library and be used in a simulation.

Deterministic Local Sensitivity Analysis of Augmented Systems—I: Theory

This work provides the theoretical foundation for the modular implementation of the Adjoint Sensitivity Analysis Procedure (ASAP) for large-scale simulation systems. The implementation of the ASAP commences with a selected code module and then proceeds by augmenting the size of the adjoint sensitivity system, module by module, until the entire system is completed. Notably, the adjoint sensitivity system for the augmented system can often be solved by using the same numerical methods used for solving the original, nonaugmented adjoint system, particularly when the matrix representation of the adjoint operator for the augmented system can be inverted by partitioning.

Parameter independent model order reduction

Several recently developed model order reduction methods for fast simulation of large-scale dynamical systems with two or more parameters are reviewed. Besides, an alternative approach for linear parameter system model reduction as well as a more efficient method for nonlinear parameter system model reduction are proposed in this paper. Comparison between different methods from theoretical elegancy to complexity of implementation are given. By these methods, a large dimensional system with parameters can be reduced to a smaller dimensional parameter system that can approximate the original large sized system to a certain degree for all the parameters.