Cache-Aware and Cache-Oblivious Adaptive Sorting

Brodal, Gerth Stølting; Fagerberg, Rolf; Moruz, Gabriel

doi:10.1007/11523468_47

Cited by 25 publications

(27 citation statements)

References 20 publications

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“…Two optimal algorithms, the Funnelsort and a variant of Distributionsort are presented [14] for the cache-oblivious model. In [5], Brodal et al proposed a reduction from adaptivesorting to non-adaptive, cache-oblivious sorting which also yields a comparision as well as I/O optimal cache-aware and cache-oblivious sorting algorithms with respect to the measure Inv. Furthermore, cache-aware and cache-oblivious versions of Genericsort [12], are also proposed in [5].…”

Section: Related Workmentioning

confidence: 99%

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Sorting on architecturally diverse computer systems

Chamberlain

Ganesan

2009

Proceedings of the Third International Workshop on High-Performance Reconfigurable Computing Technology and Applications

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Sorting is an important problem that forms an essential component of many high-performance applications. Here, we explore the design space of sorting algorithms in reconfigurable hardware, looking to maximize the benefit associated with high-bandwidth, multiple-port access to memory. Rather than focus on an individual implementation, we investigate a family of approaches that exploit characteristics fairly unique to reconfigurable hardware.

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Section: Related Workmentioning

confidence: 99%

“…In cacheaware sorting algorithms for general-purpose processors [5], the group size is typically determined by available cache size. The point is to exploit locality and use the fastest available memory.…”

Section: Application Topologymentioning

confidence: 99%

Sorting on architecturally diverse computer systems

Chamberlain

Ganesan

2009

Proceedings of the Third International Workshop on High-Performance Reconfigurable Computing Technology and Applications

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“…We recommend the readers to consult a survey by Estivill-Castro and Wood [3]. Adaptive sorting algorithms are also discussed in terms of integer sorting [7] and I/O-efficiency (both cache-aware and cacheoblivious) [8].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Algorithms for Planar Convex Hull Problems

Ahn

Okamoto

2011

IEICE Trans. Inf. & Syst.

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SUMMARYWe study problems in computational geometry from the viewpoint of adaptive algorithms. Adaptive algorithms have been extensively studied for the sorting problem, and in this paper we generalize the framework to geometric problems. To this end, we think of geometric problems as permutation (or rearrangement) problems of arrays, and define the "presortedness" as a distance from the input array to the desired output array. We call an algorithm adaptive if it runs faster when a given input array is closer to the desired output, and furthermore it does not make use of any information of the presortedness. As a case study, we look into the planar convex hull problem for which we discover two natural formulations as permutation problems. An interesting phenomenon that we prove is that for one formulation the problem can be solved adaptively, but for the other formulation no adaptive algorithm can be better than an optimal outputsensitive algorithm for the planar convex hull problem. To further pursue the possibility of adaptive computational geometry, we also consider constructing a kd-tree. key words: adaptive algorithms, convex hulls, computational geometry

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“…The division protocol that we use, GreedySplit, is a generalization of the binary division protocol introduced in [1]. It partitions the input in d + 1 subsequences S 0 , .…”

Section: ⊓ ⊔mentioning

confidence: 99%

“…This tradeoff is achieved by GenericSort introduced by Estivill-Castro and Wood [5] by adopting a d-way division protocol and d-way merging that performs a low number of branch mispredictions. The division protocol is a d-way generalization of the binary greedy division protocol considered in [1].…”

Section: Introductionmentioning

confidence: 99%

Tradeoffs Between Branch Mispredictions and Comparisons for Sorting Algorithms

Brodal

Moruz

2005

Lecture Notes in Computer Science

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Abstract. Branch mispredictions is an important factor affecting the running time in practice. In this paper we consider tradeoffs between the number of branch mispredictions and the number of comparisons for sorting algorithms in the comparison model. We prove that a sorting algorithm using O(dn log n) comparisons performs Ω(n log d n) branch mispredictions. We show that Multiway MergeSort achieves this tradeoff by adopting a multiway merger with a low number of branch mispredictions. For adaptive sorting algorithms we similarly obtain that an algorithm performing O(dn(1 + log(1 + Inv/n))) comparisons must perform Ω(n log d (1 + Inv/n)) branch mispredictions, where Inv is the number of inversions in the input. This tradeoff can be achieved by GenericSort by Estivill-Castro and Wood by adopting a multiway division protocol and a multiway merging algorithm with a low number of branch mispredictions.

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Cache-Aware and Cache-Oblivious Adaptive Sorting

Cited by 25 publications

References 20 publications

Sorting on architecturally diverse computer systems

Sorting on architecturally diverse computer systems

Adaptive Algorithms for Planar Convex Hull Problems

Tradeoffs Between Branch Mispredictions and Comparisons for Sorting Algorithms

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