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首页> 外文期刊>LIPIcs : Leibniz International Proceedings in Informatics >External Memory Priority Queues with Decrease-Key and Applications to Graph Algorithms
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External Memory Priority Queues with Decrease-Key and Applications to Graph Algorithms

机译:带有减小键的外部存储器优先级队列及其在图形算法中的应用

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We present priority queues in the external memory model with block size B and main memory size M that support on N elements, operation Update (a combination of operations Insert and DecreaseKey) in O(1/Blog_{M/B} N/B) amortized I/Os and operations ExtractMin and Delete in O(ceil[(M^epsilon)/B log_{M/B} N/B] log_{M/B} N/B) amortized I/Os, for any real epsilon in (0,1), using O(N/Blog_{M/B} N/B) blocks. Previous I/O-efficient priority queues either support these operations in O(1/Blog_2 N/B) amortized I/Os [Kumar and Schwabe, SPDP '96] or support only operations Insert, Delete and ExtractMin in optimal O(1/Blog_{M/B} N/B) amortized I/Os, however without supporting DecreaseKey [Fadel et al., TCS '99]. We also present buffered repository trees that support on a multi-set of N elements, operation Insert in O(1/Blog_M/B N/B) I/Os and operation Extract on K extracted elements in O(M^{epsilon} log_M/B N/B + K/B) amortized I/Os, using O(N/B) blocks. Previous results achieve O(1/Blog_2 N/B) I/Os and O(log_2 N/B + K/B) I/Os, respectively [Buchsbaum et al., SODA '00]. Our results imply improved O(E/Blog_{M/B} E/B) I/Os for single-source shortest paths, depth-first search and breadth-first search algorithms on massive directed dense graphs (V,E) with E = Omega (V^(1+epsilon)), epsilon 0 and V = Omega (M), which is equal to the I/O-optimal bound for sorting E values in external memory.
机译:我们在外部存储器模型中显示优先级队列,其块大小为B,主存储器大小为M,它们支持N个元素,操作更新(操作Insert和DecreaseKey的组合)在O(1 / Blog_ {M / B} N / B)中摊销的I / O和操作O(ceil [(M ^ epsilon)/ B log_ {M / B} N / B] log_ {M / B} N / B)摊销的I / O,用于任何实际epsilon在(0,1)中使用O(N / Blog_ {M / B} N / B)块。以前的具有I / O效率的优先级队列要么在O(1 / Blog_2 N / B)摊销的I / O中支持这些操作[Kumar and Schwabe,SPDP '96],要么仅在最佳O(1 /中支持插入,删除和ExtractMin操作Blog_ {M / B} N / B)分摊了I / O,但是不支持DecreaseKey [Fadel等,TCS '99]。我们还展示了支持N个元素的多集合的缓冲存储库树,在O(1 / Blog_M / BN / B)I / O中插入操作以及在O(M ^ {epsilon} log_M / BN / B + K / B)分摊的I / O,使用O(N / B)块。先前的结果分别实现了O(1 / Blog_2 N / B)I / O和O(log_2 N / B + K / B)I / O [Buchsbaum et al。,SODA '00]。我们的结果表明,针对带有E的大规模有向密集图(V,E)上的单源最短路径,深度优先搜索和宽度优先搜索算法,改进了O(E / Blog_ {M / B} E / B)I / O。 = Omega(V ^(1 + epsilon)),epsilon> 0,V = Omega(M),它等于对外部存储器中的E值进行排序的I / O最佳范围。

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