Multilevel Graph Partitioning Scheme to Solve Traveling Salesman Problem

Khan, Atif Ali; Khan, Muhammad Umair; Iqbal, Muneeb

doi:10.1109/itng.2012.106

Cited by 17 publications

(12 citation statements)

References 3 publications

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“…Our work is different from them in a number of aspects: 1). To generate abstraction, existing methods [14,15,17,19,22,25,27,28,31,43] usually require complex graph transformations that can only be implemented efficiently in a full memory environment. Thus, they are not applicable in an out-of-core environment.…”

Section: Related Workmentioning

confidence: 99%

Wonderland

Zhang

Zhuo

et al. 2018

SIGPLAN Not.

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Many important graph applications are iterative algorithms that repeatedly process the input graph until convergence. For such algorithms, graph abstraction is an important technique: although much smaller than the original graph, it can bootstrap an initial result that can significantly accelerate the final convergence speed, leading to a better overall performance. However, existing graph abstraction techniques typically assume either fully in-memory or distributed environment, which leads to many obstacles preventing the application to an out-of-core graph processing system. In this paper, we propose Wonderland, a novel out-of-core graph processing system based on abstraction. Wonderland has three unique features: 1) A simple method applicable to out-of-core systems allowing users to extract effective abstractions from the original graph with acceptable cost and a specific memory limit; 2) Abstraction-enabled information propagation, where an abstraction can be used as a bridge over the disjoint on-disk graph partitions; 3) Abstractionguided priority scheduling, where an abstraction can infer the better priority-based order in processing on-disk graph partitions. Wonderland is a significant advance over the stateof-the-art because it not only makes graph abstraction feasible to out-of-core systems, but also broadens the applications of the concept in important ways. Evaluation results of Wonderland reveal that Wonderland achieves a drastic speedup over the other state-of-the-art systems,-up to two orders of magnitude for certain cases.

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Section: Related Workmentioning

confidence: 99%

Wonderland

Zhang

Zhuo

et al. 2018

SIGPLAN Not.

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“…The task at hand is providing proper dividing methodologies and the algorithms that will interconnect them. Works such as [38] describe the effectiveness of this approach and a discussion of multiple algorithms for undertaking the task is presented. In [29], a hybrid Neural Network, with local search via modified Lin-Kernighan algorithm that tries to solve a million city TSP, was introduced, although the work proves to be very successful in allowing for a higher degree of division of this huge TSP instance, thus enabling more scalability; the next inherent issue is presented in their results: faster solution with lower quality.…”

Section: Related Workmentioning

confidence: 99%

Reducing the Size of Combinatorial Optimization Problems Using the Operator Vaccine by Fuzzy Selector with Adaptive Heuristics

Montiel

Delgadillo²

2015

Mathematical Problems in Engineering

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Nowadays, solving optimally combinatorial problems is an open problem. Determining the best arrangement of elements proves being a very complex task that becomes critical when the problem size increases. Researchers have proposed various algorithms for solving Combinatorial Optimization Problems (COPs) that take into account the scalability; however, issues are still presented with larger COPs concerning hardware limitations such as memory and CPU speed. It has been shown that the Reduce-OptimizeExpand (ROE) method can solve COPs faster with the same resources; in this methodology, the reduction step is the most important procedure since inappropriate reductions, applied to the problem, will produce suboptimal results on the subsequent stages. In this work, an algorithm to improve the reduction step is proposed. It is based on a fuzzy inference system to classify portions of the problem and remove them, allowing COPs solving algorithms to utilize better the hardware resources by dealing with smaller problem sizes, and the use of metadata and adaptive heuristics. The Travelling Salesman Problem has been used as a case of study; instances that range from 343 to 3056 cities were used to prove that the fuzzy logic approach produces a higher percentage of successful reductions.

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“…The main objective and theme of this method is to divide the graph into smaller partitions and based on the concept that it first divides the problem into multiple sub-partitions by dividing the total number of the nodes by the number of sub problem you want and assign the nodes to the partitions which is near to the specific cluster [28].…”

Section: K-means Matchingmentioning

confidence: 99%

K-Ways Partitioning of Polyhedral Process Networks: A Multi-level Approach

Cattaneo

Moradmand

Sciuto

et al. 2015

2015 IEEE International Parallel and Distributed Processing Symposium Workshop

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Abstract-Process Networks(PNs)-based models of computation have proven as a successful framework for describing multiple kinds of applications in the Reconfigurable Hardware (RH) domain. Due to their intrinsically parallel and reactive behavior, and well-known techniques to automatically manipulate some of their instances, they are very amenable to Field Programmable Gate Arrays (FPGAs). One problem associated with PNs is that the number of nodes is usually proportional with the parallel portions of computation, and a tool to automatically map tasks to FPGAs is required when multiple FPGAs are employed to improve performance (via increased parallelism). While it is possible to solve this problem in an exact manner via dynamic programming approaches, this is not the case when practical graphs are under examination, i.e. graphs with potentially thousands nodes. In this work we extend a wellknown graph partitioning technique, namely Multi-Level K-ways partitioning algorithm, in order to cope with such scenario.

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Multilevel Graph Partitioning Scheme to Solve Traveling Salesman Problem

Cited by 17 publications

References 3 publications

Wonderland

Wonderland

Reducing the Size of Combinatorial Optimization Problems Using the Operator Vaccine by Fuzzy Selector with Adaptive Heuristics

K-Ways Partitioning of Polyhedral Process Networks: A Multi-level Approach

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