SEParAT: scheduling support environment for parallel application task graphs

Dümmler, Jörg; Kunis, Raphael; Rünger, Gudula

doi:10.1007/s10586-012-0211-1

Cited by 10 publications

(6 citation statements)

References 25 publications

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“…Euler models Scientific computation [104] Statistical methods [105] [ 106,154] Graph theory Automata [124] Graph/complex network analysis [37,[108][109][110][111][112][113][114][115][116][117] Engineering methods…”

Section: Computational Sciencementioning

confidence: 99%

Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

Álvarez-Rodríguez

Alor-Hernández

Mejia-Miranda

2018

Scientific Programming

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The present paper introduces and reviews existing technology and research works in the field of scientific programming methods and techniques in data-intensive engineering environments. More specifically, this survey aims to collect those relevant approaches that have faced the challenge of delivering more advanced and intelligent methods taking advantage of the existing large datasets. Although existing tools and techniques have demonstrated their ability to manage complex engineering processes for the development and operation of safety-critical systems, there is an emerging need to know how existing computational science methods will behave to manage large amounts of data. That is why, authors review both existing open issues in the context of engineering with special focus on scientific programming techniques and hybrid approaches. 1193 journal papers have been found as the representative in these areas screening 935 to finally make a full review of 122. Afterwards, a comprehensive mapping between techniques and engineering and nonengineering domains has been conducted to classify and perform a meta-analysis of the current state of the art. As the main result of this work, a set of 10 challenges for future data-intensive engineering environments have been outlined.

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Section: Computational Sciencementioning

confidence: 99%

Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

Álvarez-Rodríguez

Alor-Hernández

Mejia-Miranda

2018

Scientific Programming

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“…This section gives a short overview of the scheduling framework SEParAT (Scheduling Support Environment for Parallel Application Task Graphs) [7], [8] that supports the scheduling of parallel applications in various ways. The main focus of SEParAT lies on static scheduling of applications consisting of precedence-constrained parallel tasks, i.e., tasks that can be executed by multiple execution units cooperatively.…”

Section: The Scheduling Toolkit Separatmentioning

confidence: 99%

“…In particular, it proposes appropriate extensions to the scheduling framework SEParAT [7], which provides a uniform infrastructure for scheduling algorithms for homogeneous and heterogeneous architectures. The extensions are built atop a programming model for hybrid target architectures that consists of an application model, a platform model, and a corresponding scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

Extending the Scheduling Toolkit SEParAT to Support Hybrid Parallel Platforms

Dümmler¹,

Schulze²

2016

IJCTE

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Abstract-Current parallel platforms are increasingly equipped with additional accelerators leading to hybrid system architectures. Parallel applications for these platforms can be implemented using a task-based programming approach. Such an approach facilitates the exploitation of all available execution units including the processor cores and the accelerators. The execution of a task-based application requires scheduling decisions, which may be provided by a suitable scheduling tool.This article discusses the extensions of the scheduling toolkit SEParAT to support hybrid cluster architectures. In particular, it first defines the extended programming model for hybrid platforms and the corresponding scheduling problem. The second part of the article describes the integration of this model into SEParAT. A particular focus lies on the extension of SEParAT's input and output interfaces.

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“…In the following, we describe scheduling experiments for randomly generated task sets and consider the resulting energy consumption. For a comparison with other scheduling algorithms using the makespan as objective function, we refer to [21,22]. Figure 8 illustrates the result of the scheduling algorithms for the scheduling of 20 (left), 50 (middle), and 100 (right) tasks executed on 10 processors.…”

Section: Scheduling Experimentsmentioning

confidence: 99%

“…The time M.1/ is determined by the processor P e that finishes its work last. An experimental comparison of this greedy scheduling algorithm with other scheduling algorithms using the overall execution time (makespan) as objective function is included in [21,22]. From an energy-consumption perspective, the resulting schedule can be improved by applying the results from Section 4:…”

Section: Time-based Scheduling With Energy Improvementmentioning

confidence: 99%

Modeling and analyzing the energy consumption of fork‐join‐based task parallel programs

Rauber

Rünger

2014

Concurrency and Computation

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SUMMARYBecause of environmental and monetary concerns, it is increasingly important to reduce the energy consumption in all areas, including parallel and high performance computing. In this article, we propose an approach to reduce the energy consumption needed for the execution of a set of tasks computed in parallel in a fork-join fashion. The approach consists of an analytical model for the energy consumption of a parallel computation in fork-join form on dynamic voltage frequency scaling processors, a theoretical specification of an energy-optimal frequency-scaled state, and the energy minimization by computing optimal scaling factors. For larger numbers of tasks, the approach is extended by scheduling algorithms, which exploit the analytical result and aim at a reduction of the energy. Energy measurements of a complex numerical method and the SPEC CPU2006 benchmarks as well as simulations for a large number of randomly generated tasks illustrate and validate the energy modeling, the minimization, and the scheduling results.

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SEParAT: scheduling support environment for parallel application task graphs

Cited by 10 publications

References 25 publications

Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

Survey of Scientific Programming Techniques for the Management of Data-Intensive Engineering Environments

Extending the Scheduling Toolkit SEParAT to Support Hybrid Parallel Platforms

Modeling and analyzing the energy consumption of fork‐join‐based task parallel programs

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