A Survey of the State of the Art in Performance Modeling and Prediction of Parallel and Distributed Computing Systems

Pllana, Sabri; Brandić, Ivona; Benkner, Siegfried

doi:10.5019/j.ijcir.2008.121

Cited by 15 publications

(19 citation statements)

References 27 publications

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“…Consequently, the modeling language must feature massively parallel operators over such task-level resource footprints [5]. A similar notion of resource footprints and composition can be used to express the properties of the architecture in a machine model to capture the resources that the architecture can make available to the code.…”

Section: A Formal Modelingmentioning

confidence: 99%

Preparing HPC Applications for Exascale: Challenges and Recommendations

Ábrahám

Bekas

Brandić

et al. 2015

2015 18th International Conference on Network-Based Information Systems

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Abstract-While the HPC community is working towards the development of the first Exaflop computer (expected around 2020), after reaching the Petaflop milestone in 2008 still only few HPC applications are able to fully exploit the capabilities of Petaflop systems. In this paper we argue that efforts for preparing HPC applications for Exascale should start before such systems become available. We identify challenges that need to be addressed and recommend solutions in key areas of interest, including formal modeling, static analysis and optimization, runtime analysis and optimization, and autonomic computing. Furthermore, we outline a conceptual framework for porting HPC applications to future Exascale computing systems and propose steps for its implementation.

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Section: A Formal Modelingmentioning

confidence: 99%

Preparing HPC Applications for Exascale: Challenges and Recommendations

Ábrahám

Bekas

Brandić

et al. 2015

2015 18th International Conference on Network-Based Information Systems

Self Cite

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“…Most of the prediction methods used by WMSs were crafted for characterizing parallel applications [17]. Although such techniques provide accurate predictions, they require the supervision of an expert for constructing and tuning the prediction models.…”

Section: Introductionmentioning

confidence: 99%

Ensemble learning of runtime prediction models for gene-expression analysis workflows

et al. 2015

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The adequate management of scientific workflow applications strongly depends on the availability of accurate performance models of sub-tasks. Numerous approaches use machine learning to generate such models autonomously, thus alleviating the human effort associated to this process. However, these standalone models may lack robustness, leading to a decay on the quality of information provided to workflow systems on top. This paper presents a novel approach for learning ensemble prediction models of tasks runtime. The ensemble-learning method entitled bootstrap aggregating (bagging) is used to produce robust ensembles of M5P regression trees of better predictive performance than could be achieved by standalone models. Our approach has been tested on gene expression analysis workflows. The results show that the ensemble method leads to significant prediction-error reductions when compared with learned standalone models. This is the first initiative using ensemble learning for generating performance prediction models. These promising results encourage further research in this direction.

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“…In HPC, most performance modeling techniques can be classified into analytical predictive methods, code analysis or profiling [4,5]. Analytical methods require developers to provide a model of their application.…”

Section: Related Workmentioning

confidence: 99%

Heterogeneous Resource Selection for Arbitrary HPC Applications in the Cloud

Iordache¹,

Buyukkaya²,

Pierre³

2015

Distributed Applications and Interoperable Systems

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Abstract. Cloud infrastructures offer a wide variety of resources to choose from. However, most cloud users ignore the potential benefits of dynamically choosing cloud resources among a wide variety of VM instance types with different configuration/cost tradeoffs. We propose to automate the choice of resources that should be assigned to arbitrary non-interactive applications. During the first executions of the application, the system tries various resource configurations and builds a custom performance model for this application. Thereafter, cloud users can specify their execution time or financial cost constraints, and let the system automatically select the resources which best satisfy this constraint.

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A Survey of the State of the Art in Performance Modeling and Prediction of Parallel and Distributed Computing Systems

Cited by 15 publications

References 27 publications

Preparing HPC Applications for Exascale: Challenges and Recommendations

Preparing HPC Applications for Exascale: Challenges and Recommendations

Ensemble learning of runtime prediction models for gene-expression analysis workflows

Heterogeneous Resource Selection for Arbitrary HPC Applications in the Cloud

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