Efficient Distributed Weighted Matchings on Trees

Hoepman, Jaap-Henk; Kutten, Shay; Lotker, Zvi

doi:10.1007/11780823_10

Cited by 68 publications

(143 citation statements)

References 12 publications

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“…In the special case of trees, Hoepman et al [12] showed that a ( − )-MCM can be computed in expected constant time [12]. Hoepman [11] has shown that a 1 2 -MWM can be computed deterministically in O(n) time.…”

Section: Introductionmentioning

confidence: 99%

Improved Distributed Approximate Matching

Lotker

Patt-Shamir

Pettie

2015

J. ACM

Self Cite

128

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We present improved algorithms for finding approximately optimal matchings in both weighted and unweighted graphs. For unweighted graphs, we give an algorithm providing (1 − )-approximation in O(log n) time for any constant > 0. This result improves on the classical -approximation due to Israeli and Itai. As a by-product, we also provide an improved algorithm for unweighted matchings in bipartite graphs. In the context of weighted graphs, we give another algorithm which provides ( − )-approximation in O(log n) time.

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Section: Introductionmentioning

confidence: 99%

Improved Distributed Approximate Matching

Lotker

Patt-Shamir

Pettie

2015

J. ACM

Self Cite

128

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“…Some community metrics, including modularity [24], form NP-complete optimization problems, so additional work improving the matching may not produce better results. Our approach follows existing parallel algorithms [25], [26]. Differences appear in mapping the matching algorithm to our data structures and platforms.…”

Section: Parallel Agglomerative Community Detectionmentioning

confidence: 99%

“…The algorithm is equivalent to a different ordering of existing parallel algorithms [25], [26] and also produces a maximal matching with weight (total score) within a factor of two of the maximum.…”

Section: B Scoring and Matchingmentioning

confidence: 99%

Scalable Multi-threaded Community Detection in Social Networks

Riedy

Bader

Meyerhenke

2012

2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops &Amp; PhD Forum

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Abstract-The volume of existing graphstructured data requires improved parallel tools and algorithms. Finding communities, smaller subgraphs densely connected within the subgraph than to the rest of the graph, plays a role both in developing new parallel algorithms as well as opening smaller portions of the data to current analysis tools. We improve performance of our parallel community detection algorithm by 20% on the massively multithreaded Cray XMT, evaluate its performance on the next-generation Cray XMT2, and extend its reach to Intel-based platforms with OpenMP. To our knowledge, not only is this the first massively parallel community detection algorithm but also the only such algorithm that achieves excellent performance and good parallel scalability across all these platforms. Our implementation analyzes a moderate sized graph with 105 million vertices and 3.3 billion edges in around 500 seconds on a four processor, 80-logical-core Intel-based system and 1100 seconds on a 64-processor Cray XMT2.

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“…The MWIS algorithm operates on the interference graph and since it is a greedy algorithm, it can be easily implemented in a distributed manner (e.g. the algorithm of [9] that can be applied to a network with primary interference constraints). As in the single-hop case and as in [18], the algorithm is independent of the global network topology and traffic statistics.…”

Section: Backpressure-based Routing and Schedulingmentioning

confidence: 99%

“…In particular, they consider a graph of interfering queues 4 and study the performance of a greedy maximal weight scheduling algorithm (termed Longest Queue First -LQF) that selects the set of served queues greedily according to the queue lengths. They present sufficient conditions for such an algorithm to provide 100% throughput (notice that unlike a maximum weight solution a maximal weight solution can be easily obtained in a distributed manner [9]). These conditions are referred to as Local Pooling (LoP) and are related to the properties of all maximal independent sets in the conflict graph.…”

Section: Introductionmentioning

confidence: 99%

Local pooling conditions for joint routing and scheduling

Brzezinski

Zussman

Modiano

2008

2008 Information Theory and Applications Workshop

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Abstract-A major challenge in the design and operation of wireless networks is to jointly route packets and schedule transmissions to efficiently share the common spectrum among links in the same area. Due to the lack of central control in wireless networks, these algorithms have to be decentralized. It was recently shown that distributed (greedy) algorithms can usually guarantee only fractional throughput. It was also recently shown that if a set of conditions regarding the network topology (known as Local Pooling) is satisfied, simple distributed maximal weight (greedy) scheduling algorithms achieve 100% throughput. In this paper, we focus on networks in which packets have to undergo multihop routing and derive multihop Local Pooling conditions for that setting. In networks satisfying these conditions, a backpressure-based joint routing and scheduling algorithm employing maximal weight scheduling achieves 100% throughput.

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Efficient Distributed Weighted Matchings on Trees

Cited by 68 publications

References 12 publications

Improved Distributed Approximate Matching

Improved Distributed Approximate Matching

Scalable Multi-threaded Community Detection in Social Networks

Local pooling conditions for joint routing and scheduling

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