Fast algorithm for data exchange in reconfigurable tree structures

This paper presents a fast algorithm for data exchange in a network of processors organized as a reconfigurable tree structure. For a given data exchange table, the algorithm generates a sequence of tree configurations in which the data exchanges are to be executed. A significant feature of the algorithm is that each exchange is executed in a tree configuration in which the source and destination nodes are adjacent to each other. It has been proved in a theorem that for every pair of nodes in the reconfigurable tree structure, there always exists two and only two configurations in which these two nodes are adjacent to each other. The algorithm utilizes this fact and determines the solution so as to optimize both the number of configurations required and the time to perform the data exchanges. Analysis of the algorithm shows that it has linear time complexity, and provides a large reduction in run-time as compared to a previously proposed algorithm. This is well-confirmed from the experimental results obtained by executing a large number of randomly-generated data exchange tables. Another significant feature of the algorithm is that the bit-size of the routing information code is always two bits, irrespective of the number of nodes in the tree. This not only increases the speed of the algorithm but also results in simpler hardware inside each node.

Pipelined ring algorithm for matrix multiplication on transputer networks: performance analysis and estimation

A parallel matrix multiplication algorithm is presented, and studies of its performance and estimation are discussed. The algorithm is implemented on a network of transputers connected in a ring topology. An efficient scheme for partitioning the input matrices is introduced which enables overlapping computation with communication. This makes the algorithm achieve near-ideal speed-up for reasonably large matrices. Analytical expressions for the execution time of the algorithm have been derived by analysing its computation and communication characteristics. These expressions are validated by comparing the theoretical results of the performance with the experimental values obtained on a four-transputer network for both square and irregular matrices. The analytical model is also used to estimate the performance of the algorithm for a varying number of transputers and varying problem sizes. Although the algorithm is implemented on transputers, the methodology and the partitioning scheme presented in this paper are quite general and can be implemented on other processors which have the capability of overlapping computation with communication. The equations for performance prediction can also be extended to other multiprocessor systems.

An Efficient Robust Algorithm for the Surface-Potential Calculation of Independent DG MOSFET

Although the recently proposed single-implicit-equation-based input voltage equations (IVEs) for the independent double-gate (IDG) MOSFET promise faster computation time than the earlier proposed coupled-equations-based IVEs, it is not clear how those equations could be solved inside a circuit simulator as the conventional Newton-Raphson (NR)-based root finding method will not always converge due to the presence of discontinuity at the G-zero point (GZP) and nonremovable singularities in the trigonometric IVE. In this paper, we propose a unique algorithm to solve those IVEs, which combines the Ridders algorithm with the NR-based technique in order to provide assured convergence for any bias conditions. Studying the IDG MOSFET operation carefully, we apply an optimized initial guess to the NR component and a minimized solution space to the Ridders component in order to achieve rapid convergence, which is very important for circuit simulation. To reduce the computation budget further, we propose a new closed-form solution of the IVEs in the near vicinity of the GZP. The proposed algorithm is tested with different device parameters in the extended range of bias conditions and successfully implemented in a commercial circuit simulator through its Verilog-A interface.

The actor-critic algorithm as multi-time-scale stochastic approximation

The actor-critic algorithm of Barto and others for simulation-based optimization of Markov decision processes is cast as a two time Scale stochastic approximation. Convergence analysis, approximation issues and an example are studied.

A fault-tolerant distributed deadlock detection algorithm

In this paper, we propose a new fault-tolerant distributed deadlock detection algorithm which can handle loss of any resource release message. It is based on a token-based distributed mutual exclusion algorithm. We have evaluated and compared the performance of the proposed algorithm with two other algorithms which belong to two different classes, using simulation studies. The proposed algorithm is found to be efficient in terms of average number of messages per wait and average deadlock duration compared to the other two algorithms in all situations, and has comparable or better performance in terms of other parameters.

A token-based distributed algorithm for total order atomic broadcast

In this paper, we propose a new token-based distributed algorithm for total order atomic broadcast. We have shown that the proposed algorithm requires lesser number of messages compared to the algorithm where broadcast servers use unicasting to send messages to other broadcast servers. The traditional method of broadcasting requires 3(N - 1) messages to broadcast an application message, where N is the number of broadcast servers present in the system. In this algorithm, the maximum number of token messages required to broadcast an application message is 2N. For a heavily loaded system, the average number of token messages required to broadcast an application message reduces to 2, which is a substantial improvement over the traditional broadcasting approach.

Identification of delamination in composite beams using spectral estimation and a genetic algorithm

An efficient strategy for identification of delamination in composite beams and connected structures is presented. A spectral finite-element model consisting of a damaged spectral element is used for model-based prediction of the damaged structural response in the frequency domain. A genetic algorithm (GA) specially tailored for damage identification is derived and is integrated with finite-element code for automation. For best application of the GA, sensitivities of various objective functions with respect to delamination parameters are studied and important conclusions are presented. Model-based simulations of increasing complexity illustrate some of the attractive features of the strategy in terms of accuracy as well as computational cost. This shows the possibility of using such strategies for the development of smart structural health monitoring softwares and systems.

Machine-tool selection and operation allocation in FMS: solving a fuzzy goal-programming model using a genetic algorithm

The decision-making process for machine-tool selection and operation allocation in a flexible manufacturing system (FMS) usually involves multiple conflicting objectives. Thus, a fuzzy goal-programming model can be effectively applied to this decision problem. The paper addresses application of a fuzzy goal-programming concept to model the problem of machine-tool selection and operation allocation with explicit considerations given to objectives of minimizing the total cost of machining operation, material handling and set-up. The constraints pertaining to the capacity of machines, tool magazine and tool life are included in the model. A genetic algorithm (GA)-based approach is adopted to optimize this fuzzy goal-programming model. An illustrative example is provided and some results of computational experiments are reported.

Neural network based optimal routing algorithm for communication networks

This paper presents the capability of the neural networks as a computational tool for solving constrained optimization problem, arising in routing algorithms for the present day communication networks. The application of neural networks in the optimum routing problem, in case of packet switched computer networks, where the goal is to minimize the average delays in the communication have been addressed. The effectiveness of neural network is shown by the results of simulation of a neural design to solve the shortest path problem. Simulation model of neural network is shown to be utilized in an optimum routing algorithm known as flow deviation algorithm. It is also shown that the model will enable the routing algorithm to be implemented in real time and also to be adaptive to changes in link costs and network topology. (C) 2002 Elsevier Science Ltd. All rights reserved.

Performance analysis of the random early detection algorithm

In this article we consider a finite queue with its arrivals controlled by the random early detection algorithm. This is one of the most prominent congestion avoidance schemes in the Internet routers. The aggregate arrival stream from the population of transmission control protocol sources is locally considered stationary renewal or Markov modulated Poisson process with general packet length distribution. We study the exact dynamics of this queue and provide the stability and the rates of convergence to the stationary distribution and obtain the packet loss probability and the waiting time distribution. Then we extend these results to a two traffic class case with each arrival stream renewal. However, computing the performance indices for this system becomes computationally prohibitive. Thus, in the latter half of the article, we approximate the dynamics of the average queue length process asymptotically via an ordinary differential equation. We estimate the error term via a diffusion approximation. We use these results to obtain approximate transient and stationary performance of the system. Finally, we provide some computational examples to show the accuracy of these approximations.

Two timescale analysis of the Alopex algorithm for optimization

Alopex is a correlation-based gradient-free optimization technique useful in many learning problems. However, there are no analytical results on the asymptotic behavior of this algorithm. This article presents a new version of Alopex that can be analyzed using techniques of two timescale stochastic approximation method. It is shown that the algorithm asymptotically behaves like a gradient-descent method, though it does not need (or estimate) any gradient information. It is also shown, through simulations, that the algorithm is quite effective.

PocketAlign A Novel Algorithm for Aligning Binding Sites in Protein Structures

A fundamental task in bioinformatics involves a transfer of knowledge from one protein molecule onto another by way of recognizing similarities. Such similarities are obtained at different levels, that of sequence, whole fold, or important substructures. Comparison of binding sites is important to understand functional similarities among the proteins and also to understand drug cross-reactivities. Current methods in literature have their own merits and demerits, warranting exploration of newer concepts and algorithms, especially for large-scale comparisons and for obtaining accurate residue-wise mappings. Here, we report the development of a new algorithm, PocketAlign, for obtaining structural superpositions of binding sites. The software is available as a web-service at http://proline.physicslisc.emetin/pocketalign/. The algorithm encodes shape descriptors in the form of geometric perspectives, supplemented by chemical group classification. The shape descriptor considers several perspectives with each residue as the focus and captures relative distribution of residues around it in a given site. Residue-wise pairings are computed by comparing the set of perspectives of the first site with that of the second, followed by a greedy approach that incrementally combines residue pairings into a mapping. The mappings in different frames are then evaluated by different metrics encoding the extent of alignment of individual geometric perspectives. Different initial seed alignments are computed, each subsequently extended by detecting consequential atomic alignments in a three-dimensional grid, and the best 500 stored in a database. Alignments are then ranked, and the top scoring alignments reported, which are then streamed into Pymol for visualization and analyses. The method is validated for accuracy and sensitivity and benchmarked against existing methods. An advantage of PocketAlign, as compared to some of the existing tools available for binding site comparison in literature, is that it explores different schemes for identifying an alignment thus has a better potential to capture similarities in ligand recognition abilities. PocketAlign, by finding a detailed alignment of a pair of sites, provides insights as to why two sites are similar and which set of residues and atoms contribute to the similarity.