Extending a Work-Stealing Framework with Probabilistic Guards

Yoritaka, Hiroshi; Matsui, Ken; Yasugi, Mariko; Hiraishi, Tasuku; Umatani, Seiji

doi:10.1109/icppw.2016.37

Cited by 3 publications

(13 citation statements)

References 13 publications

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“…In this section, we explain probabilistic guards and virtual probabilistic guards [7], [8], which we previously proposed to obtain better performance than uniformly random victim selection.…”

Section: Probabilistic Guardsmentioning

confidence: 99%

“…Supposing that g i is constant while a thief is attempting to steal a task, as shown in Ref. [7], we obtain the probability s i that the thief eventually succeeds in stealing a task from the i-th victim as follows:…”

Section: Probabilistic Guardsmentioning

confidence: 99%

“…In contrast to uniformly random selection, we proposed probabilistic guards (and virtual probabilistic guards) [7], [8] as a nonuniformly random steal strategy. Probabilistic guards can prevent thieves from stealing small tasks from victims probabilistically.…”

Section: Introductionmentioning

confidence: 99%

“…The Barnes-Hut O(N log N) tree algorithm is widely used for N-body simulations. In Tascell, Treecode [12] (a fast "highly serial" algorithm [13]) is well parallelized [7], [8].…”

Section: Introductionmentioning

confidence: 99%

“…• We extended the Tascell framework and implemented the four proposed strategies. • Based on a parallelized implementation [7], [8] of the Barnes-Hut algorithm [11], [12], [13], we present a simplified parallelized implementation for priority-and weightbased selection. • We present the evaluation results of the parallelized Tascell programs.…”

Section: Introductionmentioning

confidence: 99%

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Work-stealing Strategies That Consider Work Amount and Hierarchy

Nakashima

Yasugi

Yoritaka

et al. 2021

Journal of Information Processing

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This paper proposes work-stealing strategies for an idle worker (thief) to select a victim worker. These strategies avoid small tasks being stolen to reduce the total task-division cost. We implemented these strategies on a work-stealing framework called Tascell. First, we propose new types of priority-and weight-based steal strategies. Programmers can let each worker estimate and declare, as a real number, the amount of remaining work required to complete its current task so that declared values are used as "priorities" or "weights". With a priority-based strategy, a thief selects the victim that has the highest known priority at that time. With a weight-based non-uniformly random strategy, a thief uses the relative weights of victim candidates as their selection probabilities. Second, we propose work-stealing strategies to alleviate excessive intra-node work stealing and excessive "steal backs" (or leapfroggings); for example, we allow workers to steal tasks from external nodes with some frequency even if work remains inside the current node. Our evaluation uses a parallel implementation of the "highly serial" version of the Barnes-Hut force-calculation algorithm in a shared memory environment and five benchmark programs in a distributed memory environment.

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Section: Probabilistic Guardsmentioning

confidence: 99%

Section: Probabilistic Guardsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The Barnes-Hut O(N log N) tree algorithm is widely used for N-body simulations. In Tascell, Treecode [12] (a fast "highly serial" algorithm [13]) is well parallelized [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Work-stealing Strategies That Consider Work Amount and Hierarchy

Nakashima

Yasugi

Yoritaka

et al. 2021

Journal of Information Processing

Self Cite

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Extending a Work-Stealing Framework with Priorities and Weights

Nakashima

Yoritaka

Yasugi

et al. 2019

2019 IEEE/ACM 9th Workshop on Irregular Applications: Architectures and Algorithms (IA3)

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Parallelization of Matrix Partitioning in Hierarchical Matrix Construction on Distributed Memory Systems

Bai

Hiraishi

Ida

et al. 2022

Journal of Information Processing

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A hierarchical matrix (H-matrix) is an approximated form that represents N × N correlations of N objects. H-matrix construction is achieved by dividing a matrix into submatrices (partitioning), followed by calculating these submatrices' element values (filling). Matrix partitioning consists of two steps: cluster tree (CT) construction, where objects are divided into clusters hierarchically; and block cluster tree (BCT) construction, which involves observing all cluster pairs at the same CT level that satisfies the admissibility condition. This study proposes two parallel implementation methods of partitioning operations on distributed memory systems (DMSs): distributed cluster tree construction (DCTC) and redundant cluster tree construction (RCTC). In DCTC, both CT and BCT constructions are parallelized using workers in all computing nodes. In RCTC, CT is constructed in every computing node redundantly by employing only intra-node work stealing. The BCT is then constructed in parallel using workers in all computing nodes. RCTC cannot achieve speedup using multiple computing nodes, but can eliminate the data exchange cost incurred by DCTC. We used the task-parallel language Tascell, which employs both intra-and inter-node work stealing, to handle arbitrary unbalanced tree construction and traversal on DMSs. Our RCTC implementations achieved a 1.11-1.20-fold speedup using up to 8 nodes × 36 workers in numerical experiments with 3D electric field analyses and N 10 8 .

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Extending a Work-Stealing Framework with Probabilistic Guards

Cited by 3 publications

References 13 publications

Work-stealing Strategies That Consider Work Amount and Hierarchy

Work-stealing Strategies That Consider Work Amount and Hierarchy

Extending a Work-Stealing Framework with Priorities and Weights

Parallelization of Matrix Partitioning in Hierarchical Matrix Construction on Distributed Memory Systems

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