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One of the main obstacles in obtaining high performance from heterogeneous distributed computing (HDC) system is the inevitable communication overhead. This occurs when tasks executing on different computing nodes exchange data or the assigned sub-task size is very small. In this paper, we present adaptive pre-task assignment (APA) strategy for heterogeneous distributed raytracing system. In this strategy, the master assigns pre-task to the each node. The size of sub-task for each node is proportional to the node's performance. One of the main features of this strategy is that it reduces the inter-processes communication, the cost overhead of the node's idle time and load imbalance, which normally occurs in traditional runtime task scheduling (RTS) strategies. Performances of the RTS and APA strategies are evaluated on manager/master and workers model of HDC system. The experimental results of our proposed (APA) strategy have shown a significant improvement in the performance over RTS strategy. Keywords: Task partitioning and scheduling: Load balancing: Heterogeneous distributed computing; Runtime task scheduling strategy; Adaptive pre-task assignment strategy; Distributed image/raytracing computing; Performance evaluation

The problem of embedding link-disjoint Hamiltonian cycles into torus networks is addressed. The maximum number of link-disjoint cycles is limited to half the degree of the node in a regular network. Simple methods are presented to embed the maximum number of link-disjoint Hamiltonian cycles in an r-dimensional torus network. An algorithm for finding a Hamiltonian cycle in an r-dimensional torus in the presence of a set of faulty links is also given. Key words: Fault-tolerant computing, network embedding, parallel and distributed computing, ring.

This article addresses the ability of Parallel Distributed Processing (PDP) networks to generate stagewise cognitive development in accordance with Piaget's theory of cognitive epigenesis. We carried out a replication study of the simulation experiments by McClelland (1989) and McClelland and Jenkins (1991) in which a PDP network learns to solve balance scale problems. In objective tests motivated from catastrophe theory, a mathematical theory of transitions in epigenetical systems, no evidence for stage transitions in network performance was found. It is concluded that PDP networks lack the ability to recover the positive outcomes of analogous catastrophe analyses of real cognitive developmental data. In an attempt to further characterize the learning behaviour of PDP networks, we carried out a second simulation study using the discrimination-shift paradigm. The results thus obtained indicate that PDP learning is compatible with the learning of stimulus-response relationships, not with the acquisition of mediating rules such as conceived in (neo-)Piagetian theory. In closing, we speculate about the feasibility of simulating stagewise development with alternative network architectures.

A multistage switch connection approach to improve the performance of large local computer networks is devised. The single large LAN is partitioned into smaller networks and these networks are interconnected by a Delta multistage butterfly switch. The performance of the single large network and the proposed partitioned network have been modeled and simulated using SIMSCRIPT II.5. The scheme has been applied to the token bus topology as a case study. It has been shown that the throughput of the proposed partitioned network is increased and there is a significant decrease in the average packet delay compared to the single large network. Key Words: LAN, partitioning, simulation, token bus, switch connection, Delta switch, performance evaluation, clustering techniques.

We have proposed a self-stabilizing algorithm to synchronize multiple digital clocks in a distributed system; whenever any of the clock values gets out of synchronization for any reason, the algorithm is automatically invoked and the system is brought back to a legitimate state in finite time. Key Words: Self-stabilization, distributed system, digital clocks, synchronization, symmetric graph.

An important measure of the performance of an interconnection network is its capacity. The capacity of an [Mathematical Expression Omitted] binomial network is discussed as a measure of the average number of inputs that can be connected to the outputs. It is shown that for input subset of size less than or equal to d + 2, all inputs can be connected to outputs. For input subset of size greater than d + 2, the network is considered in terms of its prime decomposition class (pdc). We present an empirical formula for the capacity of [Mathematical Expression Omitted] networks based on the number of configurations and capacity contribution of different factors in the pdc. It is shown that an [Mathematical Expression Omitted] binomial network can provide capacity close to that of the complete crossbar switch while avoiding the latter's expensive hardware cost of contact switches. Next, we suggest a method to maximize the capacity of binomial network when selecting inputs. We illustrate how concepts of constant weight codes and residue classes can be applied to construct a type of [Mathematical Expression Omitted] network called Optimally Designed Binomial (ODB) Network. We present an algorithm which selects inputs corresponding to different residue classes and uses the left cyclic shift feature to optimize the capacity of the network.