Scheduling Malleable Parallel Tasks: An Asymptotic Fully Polynomial Time Approximation Scheme

Jansen, Klaus

doi:10.1007/s00453-003-1078-6

Cited by 57 publications

(28 citation statements)

References 27 publications

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“…Therefore the height of each slot is at most A Z * + Amax S0 . So the inequalities (13) and (14) are still valid and hence it is also asymptotically optimal. However the running time is O(n 2 ).…”

Section: Computational Resultsmentioning

confidence: 94%

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Power strip packing of malleable demands in smart grid

Karbasioun

Shaikhet

Kranakis

et al. 2013

2013 IEEE International Conference on Communications (ICC)

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Abstract-We consider a problem of supplying electricity to a set of N customers in a smart-grid framework. Each customer requires a certain amount of electrical energy which has to be supplied during the time interval [0, 1]. We assume that each demand has to be supplied without interruption, with possible duration between and r, which are given system parameters ( ≤ r). At each moment of time, the power of the grid is the sum of all the consumption rates for the demands being supplied at that moment. Our goal is to find an assignment that minimizes the power peak -maximal power over [0, 1] -while satisfying all the demands. To do this first we find the lower bound of optimal power peak. We show that the problem depends on whether or not the pair , r belongs to a "good" region G. If it does -then an optimal assignment almost perfectly "fills" the rectangle time × power = [0, 1] × [0, A] with A being the sum of all the energy demands -thus achieving an optimal power peak A. Conversely, if , r do not belong to G, we identify the lower bound A > A on the optimal value of power peak and introduce a simple linear time algorithm that almost perfectly arranges all the demands in a rectangle [0, A/A] × [0, A] and show that it is asymptotically optimal.

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Section: Computational Resultsmentioning

confidence: 94%

“…which, using the language of [11] and [13], corresponds to performance ratio being exactly 1 (as an opposite to 1 + ). In next section we will present Theorem 1 which is the main result of the paper.…”

Section: Related Literaturementioning

confidence: 99%

Power strip packing of malleable demands in smart grid

Karbasioun

Shaikhet

Kranakis

et al. 2013

2013 IEEE International Conference on Communications (ICC)

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“…In the past few years, there has been intensive research on the topic of scheduling malleable parallel applications [4][5][6][7], but actually enabling malleability at the application level has lagged behind, as already diagnosed by other authors [8], and has been confined to the computer-science (CS) literature so far.…”

Section: Introductionmentioning

confidence: 99%

Malleable parallelism with minimal effort for maximal throughput and maximal hardware load

Spenke

Balzer

Frick

et al. 2019

Computational and Theoretical Chemistry

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In practice, standard scheduling of parallel computing jobs almost always leaves significant portions of the available hardware unused, even with many jobs still waiting in the queue. The simple reason is that the resource requests of these waiting jobs are fixed and do not match the available, unused resources. However, with alternative but existing and well-established techniques it is possible to achieve a fully automated, adaptive parallelism that does not need pre-set, fixed resources. Here, we demonstrate that such an adaptively parallel program can run productively on a machine that is traditionally considered "full" and thus can indeed fill in all such scheduling gaps, even in real-life situations on large supercomputers in which a fixed-size job could not have started. keywords adaptive parallelism, malleable parallelism, scheduling, genetic algorithms, non-deterministic global optimization

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“…In addition, there are further scheduling algorithms that may be utilized for the scheduling of the independent parts of the application. These are [16] with its extension [17] and [18].Configuration-based algorithms are single-step methods which construct the schedule of a parallel application based on a predefined set of possible configurations for each task. A one-step approach to the scheduling of task graphs was presented in [19].…”

mentioning

confidence: 99%

“…In addition, there are further scheduling algorithms that may be utilized for the scheduling of the independent parts of the application. These are [16] with its extension [17] and [18].…”

mentioning

confidence: 99%

Optimizing layer‐based scheduling algorithms for parallel tasks with dependencies

Kunis

Rünger

2010

Concurrency and Computation

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SUMMARYProgramming with parallel tasks leads to task graphs with dependencies representing a parallel program. Scheduling algorithms are employed to find an efficient execution order of the parallel tasks. A large variety of scheduling algorithms exist, including layer-based scheduling algorithms for homogeneous target platforms that build consecutive layers of independent parallel tasks and schedule each layer separately. Although these scheduling algorithms provide good results in terms of scheduling algorithm runtime and schedule execution time, the resulting schedules leave room for optimization. This article proposes an optimization for arbitrary layer-based scheduling algorithms, which is called Move-blocks algorithm. Given a layer-based schedule of the parallel tasks, this algorithm moves blocks of parallel tasks into preceding layers in order to reduce the overall execution time of a task-based application. Suitable blocks of parallel tasks are identified by the algorithm Find-blocks, which is employed together with the Move-blocks algorithm. The algorithm Move-blocks is applied to four well-known scheduling algorithms. A detailed evaluation for a wide range of test cases is given.

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Scheduling Malleable Parallel Tasks: An Asymptotic Fully Polynomial Time Approximation Scheme

Cited by 57 publications

References 27 publications

Power strip packing of malleable demands in smart grid

Power strip packing of malleable demands in smart grid

Malleable parallelism with minimal effort for maximal throughput and maximal hardware load

Optimizing layer‐based scheduling algorithms for parallel tasks with dependencies

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