Tatiana Tabirca

A Dynamic Model for Fire Emergency Evacuation Based on Wireless Sensor Networks

This work introduces a dynamic model for the fire emergency evacuation problem. The model extends the concept safety introduced by Barnes et.al. for the situation when the navigation graph is dynamic. The two possible scenarios are described for using the dynamic model with a Wireless Sensor Network for fire emergency evacuation.

Feedback guided dynamic loop scheduling; A theoretical approach

In this paper we review existing loop scheduling algorithms and also describe the feedback-guided dynamic loop scheduling (FGDLS) algorithm that was proposed in Bull et al. (1996) and Bull (1998). The FGDLS algorithm uses a feedback mechanism to schedule a parallel loop within a sequential outer loop. It has been shown to perform well for scheduling problems for which the load associated with the parallel loop changes relatively slowly as the outer sequential loop executes. However the question of convergence of the FGDLS algorithm has remained an open question. In this paper we are able to establish sufficient conditions (essentially requiring that the workload does not change too rapidly with loop iteration count) for the (global) convergence of a continuous analogue of the feedback-guided algorithm.

A static workload balance scheduling algorithm

This article studies a static scheduling method based on workload balancing. An equation is presented for the case when the workload is equally distributed onto all the processors. An efficient load balance scheduling algorithm is developed assuming that the workload has certain properties. Finally, some computational results are given for the product between an upper diagonal matrix and a vector.

A Convergence Study of the Discrete FGDLS Algorithm

The Feedback-Guided Dynamic Loop Scheduling (FGDLS) algorithm [1] is a recent dynamic approach to the scheduling of a parallel loop within a sequential outer loop. Earlier papers have analysed convergence under the assumption that the workload is a positive, continuous, function of a continuous argument (the iteration number). However, this assumption is unrealistic since it is known that the iteration number is a discrete variable. In this paper we extend the proof of convergence of the algorithm to the case where the iteration number is treated as a discrete variable. We are able to establish convergence of the FGDLS algorithm for the case when the workload is monotonically decreasing.

AN O∥LOG P) PARALLEL IMPLEMENTATION OF FEEDBACK GUIDED DYNAMIC LOOP SCHEDULING

Feedback Guided Dynamic Loop Scheduling (FGDLS) is a recently proposed dynamic algorithm for loop scheduling. The original algorithm required an O(p) serial computation at each stage to compute the updated loop schedule. In this paper, it is shown that this computation can be implemented in O(log p) operations on p processors

An O(log p) algorithm for the discrete feedback guided dynamic loop scheduling

In this paper we investigate a new algorithm for the feedback-guided dynamic loop scheduling (FGDLS) method in the discrete case. The method uses a feedback-guided mechanism to schedule a parallel loop within a sequential outer loop. The execution times and the scheduling bounds for the current outer iteration are used to find the scheduling bounds of the next outer iteration. An O(p+log p) algorithm has been proposed for the discrete case where it was proved to achieve optimal bounds in only a few iterations. This articles introduces an O(log p) algorithm for the discrete case and presents some properties of it.

A Theoretical Application of Feedback Guided Dynamic Loop Scheduling

In this paper we briefly describe the Feedback-Guided Dynamic Loop Scheduling (FGDLS) algorithm that was proposed in Bull et al. [2] and Bull [1]. The FGDLS algorithm uses a feedback mechanism, based on measured execution times, to schedule a parallel loop within a sequential outer loop. We compare the FGDLS algorithm with other scheduling algorithms for a simple model problem -- the parallel computation of the inverse of a triangular matrix.

Fire Hazard Safety Optimization

Abstract This article provides a study for fire hazard safety in building environments. The working hypothesis is that the navigation costs and hazard spread are deterministically modeled over time. Based on the dynamic navigation costs under fire hazard, the article introduces the notion of dynamic safety in a recursive manner. Using the recursive equations, an algorithm is proposed to calculate the dynamic safety and successor matrices.

Snapshot Centrality Indices in Dynamic FIFO Networks

The article introduces the concept of snapshot dynamic indices as centrality measures to analyse how the importance of nodes changes over time in dynamic networks. In particular, the dynamic stress-snapshot and dynamic betweenness snapshot are investigated. We present theoretical results on dynamic shortest paths in first-in first-out dynamic networks, and then introduce some algorithms for computing these indices in the discrete-time case. Finally, we present some experimental results exploring the algorithms’ efficiency and illustrating the variation of the dynamic betweenness snapshot index for some sample dynamic networks.

Feedback Guided Dynamic Integral Partition

In this article we introduce a new iterative method for integral partition called the feedback guided dynamic integral partition (FGDIP) algorithm. The problem to study is the partition of a definite integral into p identical sub-integrals. The method generates iteratively a sequence of integral bounds by re-balancing the previous integral partition to achieve a better one. A simple convergence condition is also proposed. Experimental results show that the proposed method FGDIP achieves better performance than the classical Newtons method