Performance evaluation of the VF graph matching algorithm

Cordella, Luigi P.; Foggia, Pasquale; Sansone, Carlo; Vento, Mario

doi:10.1109/iciap.1999.797762

Cited by 153 publications

(140 citation statements)

References 4 publications

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“…The reasons topology identification is difficult are 1) establishing if a given communication graph matches a given topology is equivalent to solving the well known graph The VF2 graph matching algorithm [13] was used.…”

Section: Trace Logicalizationmentioning

confidence: 99%

Construction and Evaluation of Coordinated Performance Skeletons

Xu¹,

Subhlok²

2008

High Performance Computing - HiPC 2008

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Abstract. Performance prediction is particularly challenging for dynamic environments that cannot be modeled well due to reasons such as resource sharing and foreign system components. The approach to performance prediction taken in this work is based on the concept of a performance skeleton which is a short running program whose execution time in any scenario reflects the estimated execution time of the application it represents. The fundamental technical challenge addressed in this paper is the automatic construction of performance skeletons for parallel MPI programs. The steps in the skeleton construction procedure are 1) generation of process execution traces and conversion to a single coordinated logical program trace, 2) compression of the logical program trace, and 3) conversion to an executable parallel skeleton program. Results are presented to validate the construction methodology and prediction power of performance skeletons. The execution scenarios analyzed involve network sharing, different architectures and different MPI libraries. The emphasis is on identifying the strength and limitations of this approach to performance prediction.

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Section: Trace Logicalizationmentioning

confidence: 99%

Construction and Evaluation of Coordinated Performance Skeletons

Xu¹,

Subhlok²

2008

High Performance Computing - HiPC 2008

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“…There is an extensive literature about algorithms for special cases of the subgraph isomorphism problem, e.g., for graphs of bounded genus [41], grids [42], or planar graphs [23]. Algorithms based on searching and pruning [18,19,59] as well as constraint programming [38,70] have been used to solve the general case of the problem. Other filtering approaches that have been proposed for the subgraph isomorphism problem include filtering algorithms based on local alldifferent constraints [57] and reasoning on vertex dominance properties based on scoring and neighborhood functions [3].…”

Section: Subgraph Isomorphismmentioning

confidence: 99%

ClouDiA: a deployment advisor for public clouds

et al. 2015

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An increasing number of distributed data-driven applications are moving into shared public clouds. By sharing resources and operating at scale, public clouds promise higher utilization and lower costs than private clusters. To achieve high utilization, however, cloud providers inevitably allocate virtual machine instances non-contiguously; i.e., instances of a given application may end-up in physically distant machines in the cloud. This allocation strategy can lead to large differences in average latency between instances. For a large class of applications, this difference can result in significant performance degradation, unless care is taken in how application components are mapped to instances. In this paper, we propose ClouDiA, a general deployment advisor that selects application node deployments minimizing either (i) the largest latency between application nodes, or (ii) the longest critical path among all application nodes. ClouDiA employs a number of algorithmic techniques, including mixed-integer programming and constraint programming techniques, to efficiently search the space of possible mappings of application nodes to instances. Through experiments with synthetic and real applications in Amazon EC2, we show that mean latency is a robust metric to model

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“…This section assesses the performance of the proposed CP(Graph+Map) framework for graph matching. We compare our proposed solution with vflib [38,39], the current state of the art algorithm for subgraph isomorphism, improving over Ullman's algorithm [40].…”

Section: Global Constraintsmentioning

confidence: 99%

Combining Two Structured Domains for Modeling Various Graph Matching Problems

Deville

Dooms

Zampelli

2008

Lecture Notes in Computer Science

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Abstract. Graph pattern matching is a central application in many fields. In various areas, the structure of the pattern can only be approximated and exact matching is then too accurate. We focus here on approximations declared by the user within the pattern (optional nodes and forbidden arcs), covering graph/subgraph mono/isomorphism problems. In this paper, we show how the integration of two domains of computation over countable structures, graphs and maps, can be used for modeling and solving various graph matching problems from the simple graph isomorphism to approximate graph matching. To achieve this, we extend map variables allowing the domain and range to be non-fixed and constrained. We describe how such extended maps are designed then realized on top of finite domain and finite set variables with specific propagators. We show how a single monomorphism constraint is sufficient to model and solve those multiples graph matching problems. Furthermore, our experimental results show that our CP approach is competitive with a state of the art algorithm for subgraph isomorphism.

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Performance evaluation of the VF graph matching algorithm

Cited by 153 publications

References 4 publications

Construction and Evaluation of Coordinated Performance Skeletons

Construction and Evaluation of Coordinated Performance Skeletons

ClouDiA: a deployment advisor for public clouds

Combining Two Structured Domains for Modeling Various Graph Matching Problems

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