14,010 research outputs found
Computational experiments with an asynchronous parallel branch and bound algorithm
In this paper we present an asynchronous branch and bound algorithm for execution on an MIMD system, state sufficient conditions to prevent the parallelism from degrading the performance of this algorithm, and investigate the consequences of having the algorithm executed by nonhomogeneous processing elements.
We introduce the notions of perfect parallel time and achieved efficiency to empirically
measure the effects of parallelism, because the traditional notions of speedup and
processor utilization are not adequate for fully characterizing the actual execution of an
asynchronous parallel branch and bound algorithm.
Finally we present some computational results obtained for the symmetric traveling
salesman problem
Asynchronous parallel branch and bound and anomalies
The parallel execution of branch and bound algorithms can result in seemingly unreasonable speedups or slowdowns. Almost never the speedup is equal to the increase in computing power. For synchronous parallel branch and bound, these effects have been studiedd extensively. For asynchronous parallelizations, only little is known.
In this paper, we derive sufficient conditions to guarantee that an asynchronous parallel
branch and bound algorithm (with elimination by lower bound tests and dominance) will be
at least as fast as its sequential counterpart. The technique used for obtaining the results seems to be more generally applicable.
The essential observations are that, under certain conditions, the parallel algorithm will
always work on at least one node, that is branched from by the sequential algorithm, and
that the parallel algorithm, after elimination of all such nodes, is able to conclude that
the optimal solution has been found.
Finally, some of the theoretical results are brought into connection with a few practical
experiments
Parallel branch and bound on an MIMD system
In this paper we give a classification of parallel branch and bound algorithms and
develop a class of asynchronous branch and bound algorithms for execution on an MIMD system.
We develop sufficient conditions to prevent the anomalies that can occur due to the
parallelism, the asynchronicity or the nondeter- minism, from degrading the performance of
the algorithm. Such conditions were known already for the synchronous case. It turns out that these conditions are sufficient for asynchronous algorithms as well. We also investigate the consequences of nonhomogeneous processing elements in a parallel computer system.
We introduce the notions of perfect parallel time and achieved efficiency to empirically
measure the effects of parallelism, because the traditional notions of speedup and efficiency are not capable of fully characterizing the actual execution of an asyn-chronous parallel algorithm.
Finally we present some computational results obtained for the symmetric traveling
salesman problem
A Parallel Branch-and-Bound Method for Cluster Analysis
Cluster analysis is a generic term coined for procedures that are used objectively to group entities based on their similarities and differences. The primary objective of these procedures is to group n items into K mutually exclusive clusters so that items within each cluster are relatively homogeneous in nature while the clusters themselves are distinct. In this research, we have developed, implemented and tested an asynchronous, dynamic parallel branchand-bound algorithm to solve the clustering problem. In the developmental environment, several processes (tasks) work independently on various subproblems generated by the branch-and-bound procedure. This parallel algorithm can solve very large-scale, optimal clustering problems in a reasonable amount of wall-clock time. Linear and superlinear speedups are obtained. Thus, solutions to real-world, complex clustering problems, which could not be solved due to the lack of efficient parallel algorithms, can now be attempted
Partially ordered distributed computations on asynchronous point-to-point networks
Asynchronous executions of a distributed algorithm differ from each other due
to the nondeterminism in the order in which the messages exchanged are handled.
In many situations of interest, the asynchronous executions induced by
restricting nondeterminism are more efficient, in an application-specific
sense, than the others. In this work, we define partially ordered executions of
a distributed algorithm as the executions satisfying some restricted orders of
their actions in two different frameworks, those of the so-called event- and
pulse-driven computations. The aim of these restrictions is to characterize
asynchronous executions that are likely to be more efficient for some important
classes of applications. Also, an asynchronous algorithm that ensures the
occurrence of partially ordered executions is given for each case. Two of the
applications that we believe may benefit from the restricted nondeterminism are
backtrack search, in the event-driven case, and iterative algorithms for
systems of linear equations, in the pulse-driven case
Multi-Agent Deployment for Visibility Coverage in Polygonal Environments with Holes
This article presents a distributed algorithm for a group of robotic agents
with omnidirectional vision to deploy into nonconvex polygonal environments
with holes. Agents begin deployment from a common point, possess no prior
knowledge of the environment, and operate only under line-of-sight sensing and
communication. The objective of the deployment is for the agents to achieve
full visibility coverage of the environment while maintaining line-of-sight
connectivity with each other. This is achieved by incrementally partitioning
the environment into distinct regions, each completely visible from some agent.
Proofs are given of (i) convergence, (ii) upper bounds on the time and number
of agents required, and (iii) bounds on the memory and communication
complexity. Simulation results and description of robust extensions are also
included
Minimum mean-squared error iterative successive parallel arbitrated decision feedback detectors for DS-CDMA systems
In this paper we propose minimum mean squared error (MMSE) iterative successive parallel arbitrated decision feedback (DF) receivers for direct sequence code division multiple access (DS-CDMA) systems. We describe the MMSE design criterion for DF multiuser detectors along with successive, parallel and iterative interference cancellation structures. A novel efficient DF structure that employs successive cancellation with parallel arbitrated branches and a near-optimal low complexity user ordering algorithm are presented. The proposed DF receiver structure and the ordering algorithm are then combined with iterative cascaded DF stages for mitigating the deleterious effects of error propagation for convolutionally encoded systems with both Viterbi and turbo decoding as well as for uncoded schemes. We mathematically study the relations between the MMSE achieved by the analyzed DF structures, including the novel scheme, with imperfect and perfect feedback. Simulation results for an uplink scenario assess the new iterative DF detectors against linear receivers and evaluate the effects of error propagation of the new cancellation methods against existing ones
Greedy Graph Colouring is a Misleading Heuristic
State of the art maximum clique algorithms use a greedy graph colouring as a
bound. We show that greedy graph colouring can be misleading, which has
implications for parallel branch and bound
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