Increasing the Simulation Performance of Large-Scale Evacuations Using Parallel Computing Techniques Based on Domain Decomposition

Abstract

Abstract. Evacuation simulation has the potential to be used as part of a decision support system during large-scale incidents to provide advice to incident commanders. To be viable in these applications, it is essential that the simulation can run many times faster than real time. Parallel processing is a method of reducing run times for very large computational simulations by distributing the workload amongst a number of processors. This paper presents the development of a parallel version of the rule based evacuation simulation software buildingEXODUS using domain decomposition. Four Case Studies (CS) were tested using a cluster, consisting of 10 Intel Core 2 Duo (dual core) 3.16 GHz CPUs. CS-1 involved an idealised large geometry, with 20 exits, intended to illustrate the peak computational speed up performance of the parallel implementation, the population consisted of 100,000 agents; the peak computational speedup (PCS) was 14.6 and the peak real-time speedup (PRTS) was 4.0. CS-2 was a long area with a single exit area with a population of 100,000 agents; the PCS was 13.2 and the PRTS was 17.2. CS-3 was a 50 storey high rise building with a population of 8000/16,000 agents; the PCS was 2.48/4.49 and the PRTS was 17.9/12.9. CS-4 is a large realistic urban area with 60,000/120,000 agents; the PCS was 5.3/6.89 and the PRTS was 5.31/3.0. This type of computational performance opens evacuation simulation to a range of new innovative application areas such as real-time incident support, dynamic signage in smart buildings and virtual training environments.

Design of Distributed Discrete-Event Simulation Systems Using Deep Belief Networks

“…It is the time to the solution: the number of seconds of wall-clock time to satisfy the termination criterion of detecting the aircraft in this case. Many research initiatives have used speedup and its relationship with the wall-clock time as a performance measure [44][45][46][47]. Table 5 indicates the training vector for the case study of Figure 7, with four global nodes and one local node using block as the distribution policy, with TW as the best performance (best wall-clock time).…”

“…Another limitation was the utilization of only popular optimistic synchronization schemes. There is the potential to use other newer optimistic synchronization schemes and study load balancing among nodes [46,47]. The speedup based on the best wall-clock time was the only performance measure studied.…”

The simulation of urban-scale evacuation scenarios with application to the Swinley forest fire

“…This type of performance, and better, is required for populations not measured in one or two thousand, but in the tens and hundreds of thousands and areas measuring tens or hundreds of square kilometres. It is suggested that further improvements in performance could be achieved using the parallel implementation of the EXODUS engine (Grandison et al, 2017) and with the use of more powerful PC computers.…”