Parallel Bi-objective Shortest Paths Using Weight-Balanced B-trees with Bulk Updates

Erb, Stephan; Kobitzsch, Moritz; Sanders, Peter

doi:10.1007/978-3-319-07959-2_10

Cited by 25 publications

(20 citation statements)

References 19 publications

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“…Bökler et al [31] raise the point that node-selection label-correction alternatives can be much faster if carefully implemented. In addition, label-setting algorithms have their raison d'être and seem to work well with a small number of objectives and sparse graphs [32]. Additionally, there exist approximation algorithms by Warburton [33] as well as by Tsaggouris and Zaroliagis [34].…”

Section: State Of the Art In Multi-objective Shortest Path Methodsmentioning

confidence: 99%

Multi-Objective Optimisation Based Planning of Power-Line Grid Expansions

Bachmann

Bökler

Kopec

et al. 2018

IJGI

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German nuclear power phase out in 2022 leads to significant reconstruction of the energy transmission system. Thus, efficient identification of practical transmission routes with minimum impact on ecological and economical interests is of growing importance. Due to the sensitivity of Germany's public to grid expansion (especially in case of overhead lines), the participation and planning process needs to provide a high degree of openness and accountability. Therefore, a new methodological approach for the computer-assisted finding of optimal power-line routes considering planning, ecological and economic decision criteria is presented. The approach is implemented in a tool-chain for the determination of transmission line routes (and sets of transmission line route alternatives) based on multi-criteria optimisation. Additionally, a decision support system, based on common Geographic Information Systems (GIS), consisting of interactive visualisation and exploration of the solution space is proposed.

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Section: State Of the Art In Multi-objective Shortest Path Methodsmentioning

confidence: 99%

Multi-Objective Optimisation Based Planning of Power-Line Grid Expansions

Bachmann

Bökler

Kopec

et al. 2018

IJGI

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“…Several batched search trees exist, including 2-3 trees [33], weightbalanced B-trees [14], and red-black trees [16]. Moreover, some of these data structures [14,16] exhibit good practical performance.…”

Section: Related Workmentioning

confidence: 99%

Provably good scheduling for parallel programs that use data structures through implicit batching

Agrawal

Fineman

et al. 2014

Proceedings of the 26th ACM Symposium on Parallelism in Algorithms and Architectures

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Although concurrent data structures are commonly used in practice on shared-memory machines, even the most efficient concurrent structures often lack performance theorems guaranteeing linear speedup for the enclosing parallel program. Moreover, efficient concurrent data structures are difficult to design. In contrast, parallel batched data structures do provide provable performance guarantees, since processing a batch in parallel is easier than dealing with the arbitrary asynchrony of concurrent accesses. They can limit programmability, however, since restructuring a parallel program to use batched data structure instead of a concurrent data structure can often be difficult or even infeasible. This paper presents BATCHER, a scheduler that achieves the best of both worlds through the idea of implicit batching, and a corresponding general performance theorem. BATCHER takes as input (1) a dynamically multithreaded program that makes arbitrary parallel accesses to an abstract data type, and (2) an implementation of the abstract data type as a batched data structure that need not cope with concurrent accesses. BATCHER extends a randomized work-stealing scheduler and guarantees provably good performance to parallel algorithms that use these data structures. In particular, suppose a parallel algorithm has T 1 work, T ∞ span, and n data-structure operations. Let W (n) be the total work of datastructure operations and let s(n) be the span of a size-P batch. Then BATCHER executes the program in O((T 1 + W (n) + ns(n))/P + s(n)T ∞ ) expected time on P processors. For higher-cost data structures like search trees and large enough n, this bound becomes ((T 1 +n lg n)/P+T ∞ lg n), provably matching the work of a sequential search tree but with nearly linear speedup, even though the data structure is accessed concurrently. The BATCHER runtime bound also readily extends to data structures with amortized bounds.

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“…More recently, in the dynamic multithreading model, there have been several elegant papers on parallel treaps [9] and how to parallelize a variety of different binary search trees [7] supporting unions and intersections, and also work on how to achieve batch parallel search trees with optimal work and span [4]. Other batch parallel search trees include red-black trees [23] and weight-balanced B-trees [21]. (We are unaware of any batched self-adjusting data structures.…”

Section: Parallel Search Structuresmentioning

confidence: 99%

Parallel Working-Set Search Structures

Agrawal

Gilbert

Lim

2018

Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures

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In this paper 1 we present two versions of a parallel working-set map on p processors that supports searches, insertions and deletions. In both versions, the total work of all operations when the map has size at least p is bounded by the working-set bound, i.e., the cost of an item depends on how recently it was accessed (for some linearization): accessing an item in the map with recency r takes O(1+logr ) work. In the simpler version each map operation has O (logp) 2 + logn span (where n is the maximum size of the map). In the pipelined version each map operation on an item with recency r has O (logp) 2 + logr span. (Operations in parallel may have overlapping span; span is additive only for operations in sequence.)Both data structures are designed to be used by a dynamic multithreading parallel program that at each step executes a unittime instruction or makes a data structure call. To achieve the stated bounds, the pipelined version requires a weak-priority scheduler, which supports a limited form of 2-level prioritization. At the end we explain how the results translate to practical implementations using work-stealing schedulers.To the best of our knowledge, this is the first parallel implementation of a self-adjusting search structure where the cost of an operation adapts to the access sequence. A corollary of the working-set bound is that it achieves work static optimality: the total work is bounded by the access costs in an optimal static search tree.

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Parallel Bi-objective Shortest Paths Using Weight-Balanced B-trees with Bulk Updates

Cited by 25 publications

References 19 publications

Multi-Objective Optimisation Based Planning of Power-Line Grid Expansions

Multi-Objective Optimisation Based Planning of Power-Line Grid Expansions

Provably good scheduling for parallel programs that use data structures through implicit batching

Parallel Working-Set Search Structures

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