2,916 research outputs found
Memory lower bounds for deterministic self-stabilization
In the context of self-stabilization, a \emph{silent} algorithm guarantees
that the register of every node does not change once the algorithm has
stabilized. At the end of the 90's, Dolev et al. [Acta Inf. '99] showed that,
for finding the centers of a graph, for electing a leader, or for constructing
a spanning tree, every silent algorithm must use a memory of
bits per register in -node networks. Similarly, Korman et al. [Dist. Comp.
'07] proved, using the notion of proof-labeling-scheme, that, for constructing
a minimum-weight spanning trees (MST), every silent algorithm must use a memory
of bits per register. It follows that requiring the algorithm
to be silent has a cost in terms of memory space, while, in the context of
self-stabilization, where every node constantly checks the states of its
neighbors, the silence property can be of limited practical interest. In fact,
it is known that relaxing this requirement results in algorithms with smaller
space-complexity.
In this paper, we are aiming at measuring how much gain in terms of memory
can be expected by using arbitrary self-stabilizing algorithms, not necessarily
silent. To our knowledge, the only known lower bound on the memory requirement
for general algorithms, also established at the end of the 90's, is due to
Beauquier et al.~[PODC '99] who proved that registers of constant size are not
sufficient for leader election algorithms. We improve this result by
establishing a tight lower bound of bits per
register for self-stabilizing algorithms solving -coloring or
constructing a spanning tree in networks of maximum degree~. The lower
bound bits per register also holds for leader election
The finite element machine: An experiment in parallel processing
The finite element machine is a prototype computer designed to support parallel solutions to structural analysis problems. The hardware architecture and support software for the machine, initial solution algorithms and test applications, and preliminary results are described
Asynchronous neighborhood task synchronization
Faults are likely to occur in distributed systems. The motivation for designing self-stabilizing system is to be able to automatically recover from a faulty state. As per Dijkstra\u27s definition, a system is self-stabilizing if it converges to a desired state from an arbitrary state in a finite number of steps. The paradigm of self-stabilization is considered to be the most unified approach to designing fault-tolerant systems. Any type of faults, e.g., transient, process crashes and restart, link failures and recoveries, and byzantine faults, can be handled by a self-stabilizing system; Many applications in distributed systems involve multiple phases. Solving these applications require some degree of synchronization of phases. In this thesis research, we introduce a new problem, called asynchronous neighborhood task synchronization ( NTS ). In this problem, processes execute infinite instances of tasks, where a task consists of a set of steps. There are several requirements for this problem. Simultaneous execution of steps by the neighbors is allowed only if the steps are different. Every neighborhood is synchronized in the sense that all neighboring processes execute the same instance of a task. Although the NTS problem is applicable in nonfaulty environments, it is more challenging to solve this problem considering various types of faults. In this research, we will present a self-stabilizing solution to the NTS problem. The proposed solution is space optimal, fault containing, fully localized, and fully distributed. One of the most desirable properties of our algorithm is that it works under any (including unfair) daemon. We will discuss various applications of the NTS problem
Bounds for self-stabilization in unidirectional networks
A distributed algorithm is self-stabilizing if after faults and attacks hit
the system and place it in some arbitrary global state, the systems recovers
from this catastrophic situation without external intervention in finite time.
Unidirectional networks preclude many common techniques in self-stabilization
from being used, such as preserving local predicates. In this paper, we
investigate the intrinsic complexity of achieving self-stabilization in
unidirectional networks, and focus on the classical vertex coloring problem.
When deterministic solutions are considered, we prove a lower bound of
states per process (where is the network size) and a recovery time of at
least actions in total. We present a deterministic algorithm with
matching upper bounds that performs in arbitrary graphs. When probabilistic
solutions are considered, we observe that at least states per
process and a recovery time of actions in total are required (where
denotes the maximal degree of the underlying simple undirected graph).
We present a probabilistically self-stabilizing algorithm that uses
states per process, where is a parameter of the
algorithm. When , the algorithm recovers in expected
actions. When may grow arbitrarily, the algorithm
recovers in expected O(n) actions in total. Thus, our algorithm can be made
optimal with respect to space or time complexity
A parallel progressive radiosity algorithm based on patch data circulation
Cataloged from PDF version of article.Current research on radiosity has concentrated on increasing the accuracy and the speed of the solution. Although algorithmic and meshing techniques decrease the execution time, still excessive computational power is required for complex scenes. Hence, parallelism can be exploited for speeding up the method further. This paper aims at providing a thorough examination of parallelism in the basic progressive refinement radiosity, and investigates its parallelization on distributed-memory parallel architectures. A synchronous scheme, based on static task assignment, is proposed to achieve better coherence for shooting patch selections. An efficient global circulation scheme is proposed for the parallel light distribution computations, which reduces the total volume of concurrent communication by an asymptotical factor. The proposed parallel algorithm is implemented on an Intel's iPSC/2 hypercube multicomputer. Load balance qualities of the proposed static assignment schemes are evaluated experimentally. The effect of coherence in the parallel light distribution computations on the shooting patch selection sequence is also investigated. Theoretical and experimental evaluation is also presented to verify that the proposed parallelization scheme yields equally good performance on multicomputers implementing the simplest (e.g. ring) as well as the richest (e.g. hypercube) interconnection topologies. This paper also proposes and presents a parallel load re-balancing scheme which enhances our basic parallel radiosity algorithm to be usable in the parallelization of radiosity methods adopting adaptive subdivision and meshing techniques. (C) 1996 Elsevier Science Lt
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