An auction-based weighted matching implementation on massively parallel architectures

Sathe, Madan; Schenk, Olaf; Burkhart, Helmar

doi:10.1016/j.parco.2012.09.001

Cited by 26 publications

(18 citation statements)

References 31 publications

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“…For a more detailed presentation of the auction algorithm we use, we refer readers to Sathe et al [19], where a scalable distributed version of the algorithm is described. This formulation computes weighted matchings on sparse and dense bipartite graphs running on hundreds of compute nodes, while efficiently using multiple cores on each compute node.…”

Section: Methodsmentioning

confidence: 99%

A fast approach to global alignment of protein-protein interaction networks

et al. 2013

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BackgroundGlobal network alignment has been proposed as an effective tool for computing functional orthology. Commonly used global alignment techniques such as IsoRank rely on a two-step process: the first step is an iterative diffusion-based approach for assigning similarity scores to all possible node pairs (matchings); the second step applies a maximum-weight bipartite matching algorithm to this similarity score matrix to identify orthologous node pairs. While demonstrably successful in identifying orthologies beyond those based on sequences, this two-step process is computationally expensive. Recent work on computation of node-pair similarity matrices has demonstrated that the computational cost of the first step can be significantly reduced. The use of these accelerated methods renders the bipartite matching step as the dominant computational cost. This motivates a critical assessment of the tradeoffs of computational cost and solution quality (matching quality, topological matches, and biological significance) associated with the bipartite matching step. In this paper we utilize the state-of-the-art core diffusion-based step in IsoRank for similarity matrix computation, and couple it with two heuristic bipartite matching algorithms – a matrix-based greedy approach, and a tunable, adaptive, auction-based matching algorithm developed by us. We then compare our implementations against the performance and quality characteristics of the solution produced by the reference IsoRank binary, which also implements an optimal matching algorithm.ResultsUsing heuristic matching algorithms in the IsoRank pipeline exhibits dramatic speedup improvements; typically ×30 times faster for the total alignment process in most cases of interest. More surprisingly, these improvements in compute times are typically accompanied by better or comparable topological and biological quality for the network alignments generated. These measures are quantified by the number of conserved edges in the alignment graph, the percentage of enriched components, and the total number of covered Gene Ontology (GO) terms.ConclusionsWe have demonstrated significant reductions in global network alignment computation times by coupling heuristic bipartite matching methods with the similarity scoring step of the IsoRank procedure. Our heuristic matching techniques maintain comparable – if not better – quality in resulting alignments. A consequence of our work is that network-alignment based orthologies can be computed within minutes (as compared to hours) on typical protein interaction networks, enabling a more comprehensive tuning of alignment parameters for refined orthologies.

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Section: Methodsmentioning

confidence: 99%

A fast approach to global alignment of protein-protein interaction networks

et al. 2013

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“…Recently, Sathe et al have reported 4× to 64× speedups on 1024 processors of a Cray XE6 for a parallel auction algorithm [18]. Halfapproximation algorithms for weighted matching have been implemented on both shared and distributed memory computers with good speedups [19,20,21,22].…”

Section: Related Workmentioning

confidence: 99%

A matrix-algebraic formulation of distributed-memory maximal cardinality matching algorithms in bipartite graphs

Azad

Buluç

2016

Parallel Computing

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We describe parallel algorithms for computing maximal cardinality matching in a bipartite graph on distributedmemory systems. Unlike traditional algorithms that match one vertex at a time, our algorithms process many unmatched vertices simultaneously using a matrix-algebraic formulation of maximal matching. This generic matrix-algebraic framework is used to develop three efficient maximal matching algorithms with minimal changes. The newly developed algorithms have two benefits over existing graph-based algorithms. First, unlike existing parallel algorithms, cardinality of matching obtained by the new algorithms stays constant with increasing processor counts, which is important for predictable and reproducible performance. Second, relying on bulk-synchronous matrix operations, these algorithms expose a higher degree of parallelism on distributed-memory platforms than existing graph-based algorithms.We report high-performance implementations of three maximal matching algorithms using hybrid OpenMP-MPI and evaluate the performance of these algorithm using more than 35 real and randomly generated graphs. On real instances, our algorithms achieve up to 200× speedup on 2048 cores of a Cray XC30 supercomputer. Even higher speedups are obtained on larger synthetically generated graphs where our algorithms show good scaling on up to 16,384 cores.

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“…Bertsekas and Castañon 22 presented a parallel asynchronous implementation of HA, which is often faster than its synchronous counterpart. 23,25,26 Although graphics processing units (GPUs) were initially used only for 3D graphics processing, they are nowadays widely employed for high-performance computing (HPC) in different scientific areas, such as engineering, medical science, physics, and genomics. 23,24 These implementations were carried out on application-specific parallel architectures, typically achieving significant speedups.…”

Section: Introductionmentioning

confidence: 99%

Hungarian algorithm for subcarrier assignment problem using GPU and CUDA

Yadav

Lopes

Ilić

et al. 2018

Int J Communication

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General purpose graphics processing units (GPGPUs) have gained much popularity in scientific computing to speedup computational intensive workloads.Resource allocation in terms of power and subcarriers assignment, in current wireless standards, is one of the challenging problems due to its high computational complexity requirement. The Hungarian algorithm (HA), which has been extensively applied to linear assignment problems (LAPs), has been seen to provide encouraging result in resource allocation for wireless communication systems. This paper presents a compute unified device architecture (CUDA) implementation of the HA on graphics processing unit (GPU) for this problem.HA has been implemented on a parallel architecture to solve the subcarrier assignment problem and maximize spectral efficiency. The proposed implementation is achieved by using the "Kuhn-Munkres" algorithm with effective modifications, in order to fully exploit the capabilities of modern GPU devices. A cost matrix for maximum assignment has been defined leading to a low complexity matrix compression along with highly optimized CUDA reduction and parallel alternating path search process. All these optimizations lead to an efficient implementation with superior performance when compared with existing parallel implementations. KEYWORDSCUDA, GPU, OFDMA, parallel Hungarian algorithm, subcarrier assignment Int J Commun Syst. 2019;32:e3884.wileyonlinelibrary.com/journal/dac

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An auction-based weighted matching implementation on massively parallel architectures

Cited by 26 publications

References 31 publications

A fast approach to global alignment of protein-protein interaction networks

A fast approach to global alignment of protein-protein interaction networks

A matrix-algebraic formulation of distributed-memory maximal cardinality matching algorithms in bipartite graphs

Hungarian algorithm for subcarrier assignment problem using GPU and CUDA

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