A Scheduling Toolkit for Multiprocessor-Task Programming with Dependencies

Dümmler, Jörg; Kunis, Raphael; Rünger, Gudula

doi:10.1007/978-3-540-74466-5_4

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…Further parameters of the model are machine‐specific and data‐specific parameters . Separating machine‐specific contributions from data‐specific contributions is an idea that has already been pursued before . Machine‐specific parameters only depend on the underlying hardware and software stack.…”

Section: Discussionmentioning

confidence: 99%

Data‐aware tuning of scientific applications with model‐based autotuning

Lang

2016

Concurrency and Computation

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Summary Autotuning is a widely accepted method for adapting the execution of scientific applications to the underlying hardware. One method for speeding up the search for the optimal candidate implementation in autotuning, which often is very time‐consuming, is the performance prediction using an analytical model. Using such a model‐based autotuning method, autotuning may be performed at runtime, which enables the application to consider data characteristics for the tuning decision. The article gives an overview on existing works, which apply model‐based autotuning for data‐aware tuning decisions in scientific computing. It shows that model‐based autotuning is a feasible method for increasing the performance of scientific applications by considering influences from the input data, where ‘performance’ may be execution time or energy, among others. It furthermore presents a systematisation of model‐based autotuning: it proposes a distinction of algorithm parameters, machine‐specific parameters and data‐specific parameters. The article gives suggestions on how to set up an application‐specific analytical model, presents an example for that and discusses the limits of model‐based autotuning. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Discussionmentioning

confidence: 99%

Data‐aware tuning of scientific applications with model‐based autotuning

Lang

2016

Concurrency and Computation

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“…Layer-based scheduling algorithms deliver slightly less good but competitive results in comparison to allocation-and-scheduling-based algorithms, and have the benefit of being very fast [20].…”

Section: The Algorithm Move-blocksmentioning

confidence: 95%

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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“…However, as there exist many algorithms and architectures to choose from, and each algorithm has its own parameters and may be separated into sub-algorithms (defined as variations with different properties and strengths), choosing the optimal one for an application is not obvious. A few works propose scheduling toolkits, such as [24], but they focus on a limited number of algorithms and the only way to select the appropriate one for an application is to try them all and compare the results. This approach is computationally intensive when many algorithms must be evaluated and a more efficient selection methodology is needed.…”

Section: Introductionmentioning

confidence: 99%

Towards an automated framework for task scheduling

Dubois

Boukadoum

2010

2010 International Conference on Microelectronics

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the ongoing sh.-inkage of semiconductor geometr'ies allows for increasingly higher system-on-chip (SoC) densities, with more and more on-chip pl·ocessol·s. As a result, task scheduling has become an important concern in system design and reseal'ch in this area has pmduced substantial and dive.-sified knowledge. This paper addl'esses the issue of how to effectively repl'esent and use this knowledge in the context of design automation tools. A new methodology based on functional concept analysis is pl'esented that structul'es the available task scheduling knowledge for optimal application to multiprocessOi' SoC design.

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A Scheduling Toolkit for Multiprocessor-Task Programming with Dependencies

Cited by 8 publications

References 10 publications

Data‐aware tuning of scientific applications with model‐based autotuning

Data‐aware tuning of scientific applications with model‐based autotuning

Optimizing layer‐based scheduling algorithms for parallel tasks with dependencies

Towards an automated framework for task scheduling

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