Prediction models for performance, power, and energy efficiency of software executed on heterogeneous hardware

Bán, Dénes; Ferenć, Rudolf; Siket, István; Kiss, Ákos; Gyimóthy, Tibor

doi:10.1007/s11227-018-2252-6

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…Metrics can be obtained from the source code, from the project management system, or even from the execution traces of the source code. We can deduce higher-level software characteristics from lower level ones [44], such as the maintainability of the source code or the distribution of defects, but they can be also used to build a cost estimation model, apply performance optimization, or to improve activities supporting software quality [45,46,47]. In this work, we used static source code metrics (also known as software product metrics).…”

Section: Metricsmentioning

confidence: 99%

An Automatically Created Novel Bug Dataset and its Validation in Bug Prediction

Ferenc,

Gyimesi,

Gyimesi

et al. 2020

Preprint

Self Cite

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Bugs are inescapable during software development due to frequent code changes, tight deadlines, etc.; therefore, it is important to have tools to find these errors. One way of performing bug identification is to analyze the characteristics of buggy source code elements from the past and predict the present ones based on the same characteristics, using e.g. machine learning models.To support model building tasks, code elements and their characteristics are collected in so-called bug datasets which serve as the input for learning.We present the BugHunter Dataset: a novel kind of automatically constructed and freely available bug dataset containing code elements (files, classes, methods) with a wide set of code metrics and bug information. Other available bug datasets follow the traditional approach of gathering the characteristics of all source code elements (buggy and non-buggy) at only one or more pre-selected release versions of the code. Our approach, on the other hand, captures the buggy and the fixed states of the same source code elements from the narrowest timeframe we can identify for a bug's presence, regardless of release versions. To show the usefulness of the new dataset, we built and evaluated bug prediction models and achieved F-measure values over 0.74.

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Section: Metricsmentioning

confidence: 99%

An Automatically Created Novel Bug Dataset and its Validation in Bug Prediction

Ferenc,

Gyimesi,

Gyimesi

et al. 2020

Preprint

Self Cite

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“…In "Prediction models for performance, power, and energy efficiency of software executed on heterogeneous hardware" [8], Bán et al make use of both static source code metrics, and dynamic execution measuring time, power and energy to build predictive models on improvements. Using those models for training, they found that using static code metrics to predict concrete continuous values of dynamic properties cannot be achieved in general.…”

Section: Special Issue Presentationmentioning

confidence: 99%

Refactoring software to heterogeneous parallel platforms

García

2019

J Supercomput

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“…Therefore, with the establishment of the CCA(common component architecture) [1] forum in 1998 as a sign, researchers began to study the parallel component technology applicable to the field of scientific computing based on the traditional serial component technology. Performance prediction or adaptive methods are commonly used to improve the performance of parallel component programs [2]. After using the common strategy of performance optimization of parallel component program, most of the execution time of parallel component program is spent on some complex structure codes which can not be optimized by common means of The associate editor coordinating the review of this manuscript and approving it for publication was Stavros Souravlas .…”

Section: Introductionmentioning

confidence: 99%

Extending OpenMP for the Optimization of Parallel Component Applications

Peng

Liu

2020

IEEE Access

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Motivated by the promotion of development efficiency and performance, we research on the common performance optimization technology for parallel component applications. We define OpenMP pragmas to describe the parallel component instance calls in three types of complex structure code. By data flow analysis, we can find the parallelism potential of these codes. We extends the OpenMP pragma to support virtual computing resources and the deployment of complex structure codes on them. We provide three reference rules for the component instance tasks to be scheduled on certain resources to get better performance. To better utilize resources, we provide mapping of virtual computing resources to real resources. We extends the OpenMP programming model and execution model to accommodate the extended pragmas. The extended Babel compiler is responsible for data flow analysis. The extended CCAFFEINE framework is in charge of generating the task schedule policy. Experiments show that our performance optimization platform Bomp can get better scalability and performance than other methods existed.

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Prediction models for performance, power, and energy efficiency of software executed on heterogeneous hardware

Cited by 5 publications

References 14 publications

An Automatically Created Novel Bug Dataset and its Validation in Bug Prediction

An Automatically Created Novel Bug Dataset and its Validation in Bug Prediction

Refactoring software to heterogeneous parallel platforms

Extending OpenMP for the Optimization of Parallel Component Applications

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