Parallel Update Research Articles

Decentralized Cooperation Strategies in Two-Dimensional Traffic of Cellular Automata

We study the two-dimensional traffic of cellular automata using computer simulation. We propose two type of decentralized cooperation strategies, which are called stepping aside (CS-SA) and choosing alternative routes (CS-CAR) respectively. We introduce them into an existing two-dimensional cellular automata (CA) model. CS-SA is designed to prohibit a kind of ping-pong jump when two objects standing together try to move in opposite directions. CS-CAR is designed to change the solution of conflict in parallel update. CS-CAR encourages the objects involved in parallel conflicts choose their alternative routes instead of waiting. We also combine the two cooperation strategies (CS-SA-CAR) to test their combined effects. It is found that the system keeps on a partial jam phase with nonzero velocity and flow until the density reaches one. The ratios of the ping-pong jump and the waiting objects involved in conflict are decreased obviously, especially at the free phase. And the average flow is improved by the three cooperation strategies. Although the average travel time is lengthened a bit by CS-CAR, it is shorten by CS-SA and CS-SA-CAR. In addition, we discuss the advantage and applicability of decentralized cooperation modeling.

JAMMING TRANSITION OF POINT-TO-POINT TRAFFIC THROUGH CO-OPERATIVE MECHANISMS

We study the jamming transition of two-dimensional point-to-point traffic through co-operative mechanisms (DCM) using computer simulation. We propose two decentralized co-operative mechanisms CM which are incorporated into the point-to-point traffic models: stepping aside (CM-SA) and choosing alternative routes (CM-CAR). Incorporating CM-SA is to prevent a type of ping-pong jumps from happening when two objects standing face-to-face want to move in opposite directions. Incorporating CM-CAR is to handle the conflict when more than one object competes for the same point in parallel update. We investigate and compare four models mainly from fundamental diagrams, jam patterns and the distribution of co-operation probability. It is found that although it decreases the average velocity a little, the CM-SA increases the critical density and the average flow. Despite increasing the average velocity, the CM-CAR decreases the average flow by creating substantially vacant areas inside jam clusters. We investigate the jam patterns of four models carefully and explain this result qualitatively. In addition, we discuss the advantage and applicability of decentralized co-operation modeling.

Phase diagrams of traffic flow at an unsignalized intersection

This paper studies phase diagrams of traffic flow at an unsignalized intersection consisting of two perpendicular one-lane roads, employing parallel update rules and yield dynamics. Based on theoretical analysis and computer simulations, we have presented the phase diagram for the cases of various maximum vehicle velocities, and it is found that the phase diagram has several different topology structures. The flow formulas in all regions in the phase diagram have been derived. The results of theoretical analysis are in good agreement with the simulation ones.

Conserved mass models with stickiness and chipping

We study a chipping model in a one-dimensional periodic lattice with continuous mass, where a fixed fraction of the mass is chipped off from a site and distributed randomly among the departure site and its neighbours; the remaining mass sticks to the site. In the asymmetric version the chipped off mass is distributed among the site and the right neighbour, whereas in the symmetric version the redistribution occurs among the two neighbours. The steady state mass distribution of the model is obtained using a perturbation method for both parallel and random sequential updates. In most cases, this perturbation theory provides a steady state distribution with reasonable accuracy.

Dynamics of competitive learning: The role of updates and memory

We examine the effects of memory and different updating paradigms in a game-theoretic model of competitive learning, where agents are influenced in their choice of strategy by both the choices made by, and the consequent success rates of, their immediate neighbors. We apply parallel and sequential updates in all possible combinations to the two competing rules and find, typically, that the phase diagram of the model consists of a disordered phase separating two ordered phases at coexistence. A major result is that the corresponding critical exponents belong to the generalized universality class of the voter model. When the two strategies are distinct but not too different, we find the expected linear-response behavior as a function of their difference. Finally, we look at the extreme situation when a superior strategy, accompanied by a short memory of earlier outcomes, is pitted against its inverse; interestingly, we find that a long memory of earlier outcomes can occasionally compensate for the choice of a globally inferior strategy.

Sharp Asymptotics for Stochastic Dynamics with Parallel Updating Rule

In this paper we study the metastability problem for a stochastic dynamics with a parallel updating rule; in particular we consider a finite volume Probabilistic Cellular Automaton (PCA) in a small external field at low temperature regime. We are interested in the nucleation of the system, i.e., the typical excursion from the metastable phase (the configuration with all minuses) to the stable phase (the configuration with all pluses), triggered by the formation of a critical droplet. The main result of the paper is the sharp estimate of the nucleation time: we show that the nucleation time divided by its average converges to an exponential random variable and that the rate of the exponential random variable is an exponential function of the inverse temperature β times a prefactor that does not scale with β. Our approach combines geometric and potential theoretic arguments.

Multisensor Tracking of a Maneuvering Target in Clutter with Proposed Parallel Updating Approach

To solve the problem of measurement original uncertainty, we present a proposed parallel updating approach for tracking a maneuvering target in cluttered environment using multiple sensors. A parallel updating method is followed where the raw sensor measurements are passed to a central processor and fed directly to the target tracker. A past approach using parallel sensor processing has ignored certain data association probabilities. Simulation results show that compared with an existing IMMPDAF algorithm with parallel sensor approach, the IMMPDAF algorithm with proposed parallel updating approach solves the problem of measurements' origins and achieves significant improvement in the accuracy of track estimation.

Symmetric Nonnegative Matrix Factorization: Algorithms and Applications to Probabilistic Clustering

Nonnegative matrix factorization (NMF) is an unsupervised learning method useful in various applications including image processing and semantic analysis of documents. This paper focuses on symmetric NMF (SNMF), which is a special case of NMF decomposition. Three parallel multiplicative update algorithms using level 3 basic linear algebra subprograms directly are developed for this problem. First, by minimizing the Euclidean distance, a multiplicative update algorithm is proposed, and its convergence under mild conditions is proved. Based on it, we further propose another two fast parallel methods: α-SNMF and β -SNMF algorithms. All of them are easy to implement. These algorithms are applied to probabilistic clustering. We demonstrate their effectiveness for facial image clustering, document categorization, and pattern clustering in gene expression.

Asymmetric exclusion process for modeling of information flow

In this paper we investigate the dynamics of totally asymmetric exclusion process (TASEPs) on a one-dimensional lattice with long-range hopping and parallel update. The model is inspired by information traffic over networks. Compared to other TASEP models, particles in our model will firstly try to hop to the last site with a probability q. The probability q may reflect network reliability or available bandwidth. A mean-field approach is developed to deal with the long-range hopping. Theoretical calculations for stationary particle currents, density profiles and a phase diagram have been obtained. There are two possible stationary phases (LD and HD) in the system, corresponding to free flow and jammed flow, respectively. Interestingly, bulk density in the LD phase tends to zero, while in the HD phase it is the same as that of the normal TASEP. The LD and HD phase regions are obtained numerically. The steady-state currents and density profiles obtained from computer simulations show a very good agreement with theoretical predictions. The model and theoretical results may provide a better understanding of information traffic flow over networks.

An adaptive hierarchical domain decomposition method for parallel contact dynamics simulations of granular materials

A fully parallel version of the contact dynamics (CD) method is presented in this paper. For large enough systems, 100% efficiency has been demonstrated for up to 256 processors using a hierarchical domain decomposition with dynamic load balancing. The iterative scheme to calculate the contact forces is left domain-wise sequential, with data exchange after each iteration step, which ensures its stability. The number of additional iterations required for convergence by the partially parallel updates at the domain boundaries becomes negligible with increasing number of particles, which allows for an effective parallelization. Compared to the sequential implementation, we found no influence of the parallelization on simulation results.

Integrating speech enhancement with subband active noise control to improve communication in hearing protectors

Many workers refuse to wear hearing protection devices (HPDs) because they would rather accept the health risks than sacrifice the ability to communicate with coworkers. Integrating active noise reduction (ANR) techniques with speech enhancement algorithms could solve these limitations of modern electronic HPDs. An adaptive delay feedforward subband structure has been implemented by forming parallel signal filtering and filter update paths for each frequency band. Subband ANR provides additional attenuation of environmental noise beyond that of passive HPDs within the lower frequencies associated with speech communication. The subband structure can also provide valuable information regarding the spectral content of the environmental noise that can be exploited to determine where additional power is needed in a communication channel to improve intelligibility. The system, initially developed in simulation, maintains a specified signal-to-noise ratio in each subband while simultaneously limiting the power added by the communication channel to below that associated with hearing damage. Speech Transmission Index models have been used to validate the improvements in speech intelligibility over typical passive and ANR communication headset designs. Preliminary results for a circumaural HPD constructed to implement the concept will be presented for comparison with the simulation. [Work supported by NIOSH (R01 OH008669).]

Pedestrian flow dynamics in a lattice gas model coupled with an evolutionary game

This paper studies unidirectional pedestrian flow by using a lattice gas model with parallel update rules. Game theory is introduced to deal with conflicts that two or three pedestrians want to move into the same site. Pedestrians are either cooperators or defectors. The cooperators are gentle and the defectors are aggressive. Moreover, pedestrians could change their strategy. The fundamental diagram and the cooperator fraction at different system width W have been investigated in detail. It is found that a two-lane system exhibits a first-order phase transition while a multilane system does not. A microscopic mechanism behind the transition has been provided. Mean-field analysis is carried out to calculate the critical density of the transition as well as the probability of games at large value of W. The spatial distribution of pedestrians is investigated, which is found to be dependent (independent) on the initial cooperator fraction when W is small (large). Finally, the influence of the evolutionary game rule has been discussed.

Phase diagrams on an unsignalized intersection for the cases of different maximum velocities of vehicles

Using the cellular automaton traffic flow model, we investigate an unsignalized intersection which consists of two perpendicular one-lane roads. Both the roads cross at a point and the intersecting roads are cyclic. Each vehicle may pass or occupy the intersection where all the vehicles on both roads are not allowed to turn. Different from Ishibashi and Fukui’s studies in which the update is carried out for both roads in turn, the parallel update is proposed and its detailed rules are presented in our model. In this work, the cases of different maximum vehicle velocities on both roads are considered. Based on simulation results and the principle for constructing phase diagrams, phase diagrams are mapped out and their specific flow formulas for all the regions in the phase diagrams are obtained for various vehicle densities, which are seldom done in previous studies. One also finds that the topology of phase diagrams depends on the update rules of eastbound and northbound roads and their maximum velocities of vehicles.

Fast Parallel and Adaptive Updates for Dual-Decomposition Solvers

Dual-decomposition (DD) methods are quickly becoming important tools for estimating the minimum energy state of a graphical model. DD methods decompose a complex model into a collection of simpler subproblems that can be solved exactly (such as trees), that in combination provide upper and lower bounds on the exact solution. Subproblem choice can play a major role: larger subproblems tend to improve the bound more per iteration, while smaller subproblems enable highly parallel solvers and can benefit from re-using past solutions when there are few changes between iterations. We propose an algorithm that can balance many of these aspects to speed up convergence. Our method uses a cluster tree data structure that has been proposed for adaptive exact inference tasks, and we apply it in this paper to dual-decomposition approximate inference. This approach allows us to process large subproblems to improve the bounds at each iteration, while allowing a high degree of parallelizability and taking advantage of subproblems with sparse updates. For both synthetic inputs and a real-world stereo matching problem, we demonstrate that our algorithm is able to achieve significant improvement in convergence time.

Two-level tries: A general acceleration structure for parallel routing table accesses

The stringent performance requirement for the high efficiency of routing protocols on the Internet can be satisfied by exploiting the threaded border gateway protocol (TBGP) on multi- cores, but the state-of-the-art TBGP performance is restricted by a mass of contentions when racing to access the routing table. To this end, the highly-efficient parallel access approach appears to be a promising solution to achieve ultra-high route processing speed. This study proposes a general routing table structure consisting of two-level tries for fast parallel access, and it presents a heuristic-based divide-and-recombine algorithm to solve a mass of contentions, thereby accelerating the parallel route updates of multi-threading and boosting the TBGP performance. As a projected TBGP, this study also modifies the table operations such as insert and lookup, and validates their correctness according to the behaviors of the traditional routing table. Our evaluations on a dual quad-core Xeon server show that the parallel access contentions decrease sharply by 92.5% versus the traditional routing table, and the maximal update time of a thread is reduced by 56.8% on average with little overhead. The convergence time of update messages are improved by 49.7%.

GkDtree: A group-based parallel update kd-tree for interactive ray tracing

This paper proposes a new group-based acceleration data structure called gkDtree for interactive ray tracing of dynamic scenes. The main idea of the gkDtree is to construct the acceleration structure with a multi-level hierarchy, and to integrate a parallelization approach to result in a faster update and a more efficient tree traversal. A gkDtree can be viewed as a set of kd-trees, each of which is a local acceleration structure corresponding to a group. For a gkDtree, a scene is divided into several groups based on a scene graph. The local acceleration structure of each group involving only dynamic primitives is rebuilt. To achieve higher parallelization, dependencies among groups in different levels are removed before rebuilding occurs in parallel. To enhance the scalability of parallelization, a simple and fast load-balancing scheme is introduced. Furthermore, we apply a variety of accurate SAH (surface area heuristic) algorithms into tree generation for both static and dynamic groups. The experimental results show that a gkDtree has a real-time update performance. It has an update performance that is up to 166 times faster than a kd-tree for our test scenes in a six-core hardware system environment. Furthermore, the results also show that tree traversal performance of a gkDtree is competitive with that of a kd-tree.

Three Lemmas on Dynamic Cavity Method

We study the dynamic cavity method for dilute kinetic Ising models with synchronous update rules. For the parallel update rule we find for fully asymmetric models that the dynamic cavity equations reduce to a Markovian dynamics of the (time-dependent) marginal probabilities. For the random sequential update rule, also an instantiation of a synchronous update rule, we find on the other hand that the dynamic cavity equations do not reduce to a Markovian dynamics, unless an additional assumption of time factorization is introduced. For symmetric models we show that a fixed point of ordinary Belief propagation is also a fixed point of the dynamic cavity equations in the time factorized approximation. For clarity, the conclusions of the paper are formulated as three lemmas.

Parallel High Throughput Soft-Output Sphere Decoding Algorithm

Multiple-Input-Multiple-Output communication systems demand fast sphere decoding with high performance. To speed up the computation, we propose a scheme with multiple fixed complexity sphere decoders to construct a parallel soft-output fixed complexity sphere decoder (PFSD). The proposed decoder is highly parallel and has performance comparable to soft-output list fixed complexity sphere decoder (LFSD) and K-best sphere decoder. In addition, we propose a parallel QR decomposition algorithm to lower the preprocessing overhead, and a low complexity LLR algorithm to allow parallel update of LLR values. We demonstrate that the PFSD algorithm can increase the throughput and reduce bit error rate of a soft-output solution in a 4?×?4 16-QAM system, and has superior performance compared to other soft decoders with comparable throughput and computation complexity. The PFSD algorithm has been mapped onto Xilinx XC4VLX160 FPGA. The resulting PFSD decoder can achieve up to 75 Mbps throughput for 4?×?4 64-QAM configuration at 100MHz with low control overhead.

Shocks induced by junctions in totally asymmetric simple exclusion processes under periodic boundary condition

This Letter investigates a totally asymmetric simple exclusion process (TASEP) with junctions in a one-dimensional transport system. Parallel update rules and periodic boundary condition are adopted. Two cases corresponding to different update rules are studied. The results show that the stationary states of system mainly depend on the selection behavior of particle at the bifurcation point.

Effect of unequal injection rates on asymmetric exclusion processes with junction

In this paper, we investigate the effect of unequal injection rates on totally asymmetric simple exclusion processes (TASEPs) with a 2-input 1-output junction and parallel update. A mean-field approach is developed to deal with the junction that connects two sub-chains and the single main chain. We obtain the stationary particle currents, density profiles and phase diagrams. Interestingly, we find that the number of stationary-state phases is changeable depending on the value of α1 (α1 is the injection rate on the first sub-chain). When α1 > 1/3, there are seven stationary-state phases in the system, however when α1 < 1/3, only six stationary-state phases exist in the system. The theoretical calculations are shown to be in agreement with Monte Carlo simulations.