Parallel Discrete Event Simulation Algorithms Research Articles

Parallel algorithm development and testing using Petri-object simulation

Parallel algorithms are problematic to develop because of the negative influence of synchronisation, complicated behaviour of threads’ capturing computing resources. Experimental results show performance time’s strong dependence on algorithm parameters, such as the number of subtasks and the complexity of each task. The optimal value of subtask complexity is revealed for the particular algorithm. It is the same for different complexity of the parallelised task (with the same computing resource). To guarantee algorithm speed-up it is important to have a method for investigating the efficiency of parallel algorithm before its implementation on specified computing resources. Stochastic Petri net potentially could be a high accuracy tool for investigating the efficiency of a parallel algorithm. However, a huge number of elements are needed to compose a model of non-trivial algorithm that limits the application of this tool in practice. Petri-object simulation method allows replication of Petri nets with specified parameters and model creation of a list of linked Petri-objects. Basic templates for the model creation of a multithreaded algorithm are developed. Applying these templates, the model of the parallel discrete event simulation algorithm is developed and investigated. By the model results, the algorithm parameters providing the least performance time can be determined.

Efficient modelling of solute transport in heterogeneous media with discrete event simulation

To address the problem of different time scales present in the simulation of solute transport through systems with a complex permeability structure such as fractured porous rocks, we propose a parallel discrete event simulation (DES) algorithm based on local time stepping criteria, specifically developed for the hybrid finite-element node-centred finite volume (FV) framework. A preemptive-event-processing (PEP) approach is applied to synchronise discrete events with sufficiently close time stamps, thereby facilitating the parallelisation for shared memory architectures. The accuracy of the presented DES-PEP scheme is first verified against the analytical solution of a 1D advection equation with spatially variable coefficients. The DES scheme is then applied to simulate tracer advection through a 3D model of highly heterogeneous fractured rock represented by an unstructured adaptively refined mesh with over 1 million elements. DES produces results comparable to those of a conventional time-driven simulation (TDS), but uses less than 1% of the execution time. Analysis of event distributions shows that updates occur almost exclusively in a small number of FV cells marked by order-of-magnitudes faster fluid flow and advection-dominated transfer, while slow-flowing cells remain inactive and excluded from computations. This focusing of the computational effort leads to high simulation efficiency while simultaneously diminishing round-off errors. Scalability tests with a parallel version of DES on shared memory demonstrate further computational speedups mirroring the increased number of threads. With the use of 20 threads, execution time is reduced from 42.5 days (with TDS) to only 1.5 hours, equivalent to a speedup of over 670. This parallel DES algorithm therefore enables efficient multi-core simulation of solute transport in heterogeneous geologically realistic systems.

Simulation of virtual time profile in conservative parallel discrete event simulation algorithm for small-world network

We simulate model for evolution of local virtual time profile in conservative parallel discrete event the simulation (PDES) algorithm with long-range communication links. The main findings of simulation are that i) growth exponent depends logarithmically on the concentration p of long-range links; ii) utilisation of processing elements time decreases slowly with p. Thismeans that the conservative PDES with long-range communication links is fully scalable.

New advances in large‐scale distributed simulation and real‐time applications

New advances in large‐scale distributed simulation and real‐time applications

Relation of Parallel Discrete Event Simulation algorithms with physical models

We extend concept of local simulation times in parallel discrete event simulation (PDES) in order to take into account architecture of the current hardware and software in high-performance computing. We shortly review previous research on the mapping of PDES on physical problems, and emphasise how physical results may help to predict parallel algorithms behaviour.

Fine-Grained Parallel and Distributed Spatial Stochastic Simulation of Biological Reactions

Fine-Grained Parallel and Distributed Spatial Stochastic Simulation of Biological Reactions

The Triangular Pyramid Scheduling Model and algorithm for PDES in Grid

Grid is a perfect environment for the large scale Parallel Discrete Event Simulation (PDES), because its distribution and collaboration features match the PDES applications well. The PDES tasks or applications are modeled as a Directed Acyclic Graph (DAG), in which the simulation resources consist of three critical factors, simulation hosting machine (SHM), simulation service (SS) and simulation data (SD) in Grid environment. By solving the model we attempt to obtain an optimized triangular matching of the simulation resources on Grid, so that it can support the PDES activities better. We name the algorithm of solving the model Triangular Pyramid Scheduling (TPS). The PDES DAG is divided into three basic graph structures: Sequential structure, Fork structure, and Join structure. The TPS algorithm is developed based on these graph structures. The simulation results show that TPS algorithm can reduce the makespan and congestion, improve the simulation efficiency, and increase the resource utilization efficiency, compared to the existing algorithms.

Parallel discrete event simulation algorithm for manufacturing supply chains

Parallel discrete event simulation (PDES) is concerned with the distributed execution of large-scale system models on multiple processors. It is an enabler in the implementation of the virtual enterprise concept, integrating semi-autonomous models of production cells, factories, or units of a supply chain. The key issue in PDES is to maintain causality relationships between system events, while maximizing parallelism in their execution. Events can be executed conservatively only when it is safe to do so, sacrificing the extent to which potential parallelism of the system can be exploited. Alternatively, they can be processed optimistically without guarantee of correctness, but incurring the overhead of a rollback to an earlier saved state when causality error is detected. The paper proposes a modified optimistic scheme for distributed simulation of constituent models of a supply chain in manufacturing, which exploits the inherent operating characteristics of its domain.

Analysis and Simulation of Mixed-Technology VLSI Systems

Circuit simulation has proven to be one of the most important computer aided design (CAD) methods for verification and analysis of, integrated circuit designs. A popular approach to modeling circuits for simulation purposes is to use a hardware description language such as VHDL. VHDL has had a tremendous impact in fostering and accelerating CAD systems development in the digital arena. Similar efforts have also been carried out in the analog domain which has resulted in tools such as SPICE. However, with the growing trend of hardware designs that contain both analog and digital components, comprehensive design environments that seamlessly integrate analog and digital circuitry are needed. Simulation of digital or analog circuits is, however, exacerbated by high-resource (CPU and memory) demands that increase when analog and digital models are integrated in a mixed-mode (analog and digital) simulation. A cost-effective solution to this problem is the application of parallel discrete-event simulation (PDES) algorithms on a distributed memory platform such as a cluster of workstations. In this paper, we detail our efforts in architecting an analysis and simulation environment for mixed-technology VLSI systems. In addition, we describe the design issues faced in the application of PDES algorithms to mixed-technology VLSI system simulation.

Dag consistent parallel simulation

We present a novel approach to parallel discrete event simulation based on the Cilk model of multithreaded computation. Cilk's runtime system not only manages the low-level aspects of program execution, but also provides the user with an algorithmic model of performance which can be used to predict the execution time of a parallel simulation. Moreover, a Cilk application can ``scale down'' to run on a single processor with nearly the same performance as that of serial code.A conservative parallel discrete event simulation algorithm has been developed in which communication between logical processes is achieved using Cilk's virtual memory model, dag consistent shared memory. The simulation executes in cycles, where each cycle involves a divide and conquer computation. Although local lookahead information can be exploited, the algorithm is robust in that it also calculates a global simulation time for each cycle. It can therefore be used for applications where zero lookahead may occur.

BULK SYNCHRONOUS PARALLEL ALGORITHMS FOR CONSERVATIVE DISCRETE EVENT SIMULATION∗

All the parallel discrete event simulation algorithms developed so far have been designed to suit a specific parallel model (e.g., a PRAM model, a MP-RAM model, etc.). This paper presents several versions of conservative parallel discrete event simulation algorithms developed around a unifying model for general purpose parallel computer design and programming, namely around the Bulk Synchronous Parallel (BSP) model. The new algorithms are analysed in terms of the BSP model parameters and the effectiveness of simulators based on these algorithms is evaluated. The performance achieved even for a loosely coupled distributed system is comparable with that reported in previous research work, while the generality of the BSP model provides portability to the new approaches.

Commentary—Parallel Discrete Event Simulation: Fact or Fiction?

Can a discrete event simulation model be executed in parallel? Fujimoto's article presents the challenges from the perspective of a parallel discrete event simulation (PDES) researcher. A somewhat different answer arises by reexamining the question from the viewpoint of simulation modelers that study complex systems. A simulationist in any simulation study faces the central concern of how to define a model. Often the choice of what to include versus exclude from a model is a significant intellectual accomplishment. Indeed, success in selecting a model maximizes the model's ability to meet the objectives of a simulation study, but minimizes the computation required, to the point that it may obviate the need for parallel simulation! The model is cast into a specification, and the specification is implemented as a program. The issues of verification—whether we are building the model right—and validation—whether we are building the right model—become paramount. An added complexity is designing a model that is maintainable over a lifetime of many years, during which the model may be adapted to new simulation study objectives, run on new hardware configurations or even a new computer architecture, and run on new system software releases. Therefore, conducting a successful simulation study using sequential simulation is already a taxing activity. What additional work must be done to use PDES? Fundamentally, for the PDES programmer, the chief intellectual problem is algorithm design—to devise an operational view of model execution that can be partitioned into an efficient program for the target computer architecture. The efficiency of the algorithm chosen and its match to the target architecture are overriding decisions. In selecting the parallel simulation algorithm used for execution, the PDES programmer will assess whether assumptions required by different algorithms are met by the model. This raises the question of whether a model can be modified to allow use of a more efficient PDES algorithm. In particular, the modeler may have made arbitrary design decisions which do not affect the validity of the model but do affect the ability to parallelize the model. Therefore, model definition and PDES algorithm design are not independent processes. Simulation model definition and parallel algorithm design are very different activities. Therefore success in a large PDES study implies a need for a team combining individuals with “hard-core” simulation modeling knowledge as well as individuals with PDES algorithm knowledge. Such a team could determine if an efficiency problem should be solved by model redefinition, by changing the PDES algorithm, or both. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.