Parallel discrete event simulation

Abstract

Parallel discrete event simulation (PDES), sometimes called distributed simulation, refers to the execution of a single discrete event simulation program on a parallel computer. PDES has attracted a considerable amount of interest in recent years. From a pragmatic standpoint, this interest arises from the fact that large simulations in engineering, computer science, economics, and military applications, to mention a few, consume enormous amounts of time on sequential machines. From an academic point of view, parallel simulation is interesting because it represents a problem domain that often contains substantial amounts of parallelism (e.g., see [59]), yet paradoxically, is surprisingly difficult to parallelize in practice. A sufficiently general solution to the PDES problem may lead to new insights in parallel computation as a whole. Historically, the irregular, data-dependent nature of PDES programs has identified it as an application where vectorization techniques using supercomputer hardware provide little benefit [14]. A discrete event simulation model assumes the system being simulated only changes state at discrete points in simulated time. The simulation model jumps from one state to another upon the occurrence of an event. For example, a simulator of a store-and-forward communication network might include state variables to indicate the length of message queues, the status of communication links (busy or idle), etc. Typical events might include arrival of a message at some node in the network, forwarding a message to another network node, component failures, etc. We are especially concerned with the simulation of asynchronous systems where events are not synchronized by a global clock, but rather, occur at irregular time intervals. For these systems, few simulator events occur at any single point in simulated time; therefore parallelization techniques based on lock-step execution using a global simulation clock perform poorly or require assumptions in the timing model that may compromise the fidelity of the simulation. Concurrent execution of events at different points in simulated time is required, but as we shall soon see, this introduces interesting synchronization problems that are at the heart of the PDES problem. This article deals with the execution of a simulation program on a parallel computer by decomposing the simulation application into a set of concurrently executing processes. For completeness, we conclude this section by mentioning other approaches to exploiting parallelism in simulation problems. Comfort and Shepard et al. have proposed using dedicated functional units to implement specific sequential simulation functions, (e.g., event list manipulation and random number generation [20, 23, 47]). This method can provide only a limited amount of speedup, however. Zhang, Zeigler, and Concepcion use the hierarchical decomposition of the simulation model to allow an event consisting of several subevents to be processed concurrently [21, 98]. A third alternative is to execute independent, sequential simulation programs on different processors [11, 39]. This replicated trials approach is useful if the simulation is largely stochastic and one is performing long simulation runs to reduce variance, or if one is attempting to simulate a specific simulation problem across a large number of different parameter settings. However, one drawback with this approach is that each processor must contain sufficient memory to hold the entire simulation. Furthermore, this approach is less suitable in a design environment where results of one experiment are used to determine the experiment that should be performed next because one must wait for a sequential execution to be completed before results are obtained.

Speeding Up Decision Support

“…A causality error occurs when a simulation has processed an event with timestamp T1 and subsequently receives another event with timestamp T2, wherein T1 > T2. Since the execution of the event with timestamp T1 may have changed the state variables that will be used by the event with timestamp T2, this would amount to simulating a system in which the future could affect the past (Fujimoto, 1990). For a serial simulator that has only one event list and one logical clock it is trivial to avoid causality errors.…”

“…As such, the next section of Figure 1. Execution of events in a distributed simulation (Fujimoto, 1990) this chapter discusses the HLA-RTI middleware for distributed simulation.…”

A Conservative Approach to SystemC Parallelization

“…Indeed, VHDL or Verilog applications, as well as SystemC applications, are conceptually Discrete Event Systems (DES) [6,7]: in short, upon the occurrence of some events, processes are awaken and some computation produce in turn new events. There is a large body of literature about PDES, their implementation [8,9,10] and their utility for parallel Verilog or VHDL simulation [11,12,13], but they have not yet been introduced for SystemC.…”

Small-World Synchronized Computing Networks for Scalable Parallel Discrete-Event Simulations

“…To achieve it in an autonomous fashion can be a particularly challenging task from a system design viewpoint. In this chapter we discuss such a problem in the context of scalable Parallel Discrete-Event Simulations (PDES) [2][3][4]. Examples of PDES applications include dynamic channel allocation in cell phone communication network [4,5], models of the spread of diseases [6], battle-field simulations [7], and dynamic phenomena in highly anisotropic magnetic systems [8][9][10].…”