## 2 Literature Review
Those messages have to be annihilated or cancelled in the anti-messaging process [4]. Among many possible ways of dividing a large-scale simulation over different processors, two approaches are popular; (1) task parallelization and (2) domain decomposition [14; 15] as summarized in Table 2. MITSIM, DynaMIT, and DYNASMART are using task parallelization for faster processing [16; 17; 18] and different modules of a traffic simulation package (vehicle generation, signal operation, routing, etc.) are assigned to different computers in those models. This approach is conceptually straightforward and fairly insensitive to network bottlenecks. On the other hand, domain decomposition is splitting a simulation with respect to time or space. For time decomposition, the domain is partitioned into several time intervals and each processor is responsible for running simulation of an assigned time interval. Space decomposition is more popular for traffic simulation. In this scheme, a simulation network is divided into multiple sub-networks and each sub-network is assigned to a different machine. Several traffic simulation models have implemented this domain decomposition approach to split computational loads over different computers in order to achieve fast running speed. The models include Transportation Analysis and Simulation System (TRANSIMS) [19; 20; 21], Advanced Interactive Microscopic Simulator for Urban and Non-Urban Networks (AIMSUN) [22; 23], and Parallel Microscopic Simulation (Paramics) [24; 25]. TRANSIMS is an agent-based transportation forecast model developed by Los Alamos National Laboratory. It is a micro-simulation-based model using cellular automata (CA) approach to simulate second-by-second movements of every vehicle in a large metropolitan area. In TRANSIMS the network can be partitioned into tiles of similar size and boundary information is exchanged between processors for global synchronization [19; 20; 21]. AIMSUN2 is a microscopic simulation program originally developed as a sequential version, but later parallel computing architectures AIMSUN2/MT (the multi-thread parallelized AIMSUN2) were added. For distributed simulation implementation, a network is divided into layers, blocks, and entities. AIMSUN simulates each vehicle based on lane changing and car following model at the level of the entity (section and junction entity) which are updated at every time step. Entities updated together are grouped into blocks that may be allocated to a single thread. Blocks which need to be updated simultaneously by threads are grouped into a layer. Threads can be executed in parallel by the multiple machines. It was reported that the parallel AIMSUN2 operating on a SUN SPARC station with four processors completed a network simulation consisting of 561 sections and 428 junctions 3.5 times faster than its sequential version [22; 23]. PARAMICS (PARallel MICroscopic Simulation) is a suite of microscopic traffic simulation tools. [PERSON] et al. [24; 25] implemented data parallel programming in Paramics. In their study, multiple simple processors connected in a tightly coupled network executed the same code while with their own input data. Researchers at the National University of Singapore [26] divided the whole network and each sub network was dedicated to a different processor. To maintain the spatial connectivity between regions simulated separately vehicles were transferred to the next processor when they cross the network boundary. The method was implemented on a hypothetical grid-type network with over 150 sq. km, 500 nodes, 1000 links and 72 signalized intersections. Their results showed speed increase from 1.50 to 2.25 times when using two processors and from 1.75 to 3.75 times when using three processors, compared with the speed of simulation without parallel execution. [PERSON] et al. [14] have developed a distributed modeling framework with low-cost networked PCs.