Improved Distributed Algorithms for SCC Decomposition

Barnat, Jǐŕı; Chaloupka, Jakub; Pol, Jaco van de

doi:10.1016/j.entcs.2008.02.001

Cited by 14 publications

(25 citation statements)

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“…The PRAM model assumes that there are many processors and a shared memory. PRAM algorithms have been proposed for computing CCs [18], BCCs [20], and SCCs [2,3,9]. However, the PRAM model is not suitable for Cloud environments that are built on shared-nothing architectures.…”

Section: Related Workmentioning

confidence: 99%

“…The multi-label algorithm aims to speed up SCC discovery and graph decomposition by propagating k source vertices in parallel, instead of just one randomly picked source as done by the min-label algorithm and existing algorithms [9,3,2,17].…”

Section: Multi-label Algorithmmentioning

confidence: 99%

“…Let SCC(v) be the SCC that contains v, and let Out(v) (and In(v)) be the set of vertices that can be reached from v (and respectively, that can reach v) in G. Some PRAM algorithms [9,3,2] were designed based on the observation that SCC(v) = Out(v) ∩ In(v) (see Figure 10(a)). They compute Out(v) and In(v) by forward/backward BFS from source v that is randomly picked from G. This process then repeats on…”

Section: Strongly Connected Componentsmentioning

confidence: 99%

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Pregel algorithms for graph connectivity problems with performance guarantees

et al. 2014

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Graphs in real life applications are often huge, such as the Web graph and various social networks. These massive graphs are often stored and processed in distributed sites. In this paper, we study graph algorithms that adopt Google's Pregel, an iterative vertexcentric framework for graph processing in the Cloud. We first identify a set of desirable properties of an efficient Pregel algorithm, such as linear space, communication and computation cost per iteration, and logarithmic number of iterations. We define such an algorithm as a practical Pregel algorithm (PPA). We then propose PPAs for computing connected components (CCs), biconnected components (BCCs) and strongly connected components (SCCs). The PPAs for computing BCCs and SCCs use the PPAs of many fundamental graph problems as building blocks, which are of interest by themselves. Extensive experiments over large real graphs verified the efficiency of our algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Multi-label Algorithmmentioning

confidence: 99%

Section: Strongly Connected Componentsmentioning

confidence: 99%

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Pregel algorithms for graph connectivity problems with performance guarantees

et al. 2014

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“…If the subgraphs (slices) should be processed recursively by RECURSIVE OBF [7,8] they first need to be split into rooted chunks. For the pseudo-code of the algorithm see Algorithm 3.…”

Section: Recursive Obf Algorithmmentioning

confidence: 99%

“…In this paper we show how selected parallel SCC decomposition algorithms (namely [16], [21], [9], [7]) can be reformulated in order to be accelerated by NVIDIA CUDA technology. In particular, we design a new CUDA-aware procedure for pivot selection and we redesign the parallel algorithms in order to allow for CUDA accelerated computa-tion.…”

Section: Introductionmentioning

confidence: 99%

Computing Strongly Connected Components in Parallel on CUDA

Barnat

Bauch

Brim

et al. 2011

2011 IEEE International Parallel &Amp; Distributed Processing Symposium

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The problem of decomposition of a directed graph into its strongly connected components is a fundamental graph problem inherently present in many scientific and commercial applications. In this paper we show how existing parallel algorithms can be reformulated in order to be accelerated by NVIDIA CUDA technology. In particular, we design a new CUDA-aware procedure for pivot selection and we redesign the parallel algorithms in order to allow for CUDA accelerated computation. We also experimentally demonstrate that with a single GTX 280 GPU card we can easily outperform optimal serial CPU implementation, which is particularly interesting result as unlike the serial CPU case, the asymptotic complexity of the parallel algorithms is not optimal.

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Local Quantitative LTL Model Checking

Barnat

Brim

Černá

et al. 2009

Formal Methods for Industrial Critical Systems

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Abstract. Quantitative analysis of probabilistic systems has been studied mainly from the global model checking point of view. In the global model-checking, the goal of verification is to decide the probability of satisfaction of a given property for all reachable states in the state space of the system under investigation. On the other hand, in local model checking approach the probability of satisfaction is computed only for the set of initial states. In theory, it is possible to solve the local model checking problem using the global model checking approach. However, the global model checking procedure can be significantly outperformed by a dedicated local model checking one. In this paper we present several particular local model checking techniques that if applied to global model checking procedure reduce the runtime needed from days to minutes.

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Improved Distributed Algorithms for SCC Decomposition

Cited by 14 publications

References 1 publication

Pregel algorithms for graph connectivity problems with performance guarantees

Pregel algorithms for graph connectivity problems with performance guarantees

Computing Strongly Connected Components in Parallel on CUDA

Local Quantitative LTL Model Checking

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