AutoTune: A Plugin-Driven Approach to the Automatic Tuning of Parallel Applications

Miceli, Renato; Civario, Gilles; Sikora, Anna; Eduardo, César; Gerndt, Michael; Haitof, Houssam; Navarrete, Carmen B.; Benkner, Siegfried; Sandrieser, Martin; Morin, Laurent; Bodin, François

doi:10.1007/978-3-642-36803-5_24

Cited by 55 publications

(42 citation statements)

References 9 publications

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“…However, such specializations should not come at the expense of good software engineering practices, like modular design and code reuse. With this in mind, a plugin approach, like the one used for customizing webbrowsers and other heterogeneous software packages (Miceli et al, 2013;SchedMD, 2004), may merit consideration. The basic idea is for the software to use a standard algorithm implementation if no plugin is provided.…”

Section: Research Challenges and Future Directionsmentioning

confidence: 99%

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

521

317

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In all cases accepted manuscripts should:  link to the formal publication via its DOI  bear a CC-BY-NC-ND license -this is easy to do, click here to find out how  if aggregated with other manuscripts, for example in a repository or other site, be shared in alignment with our hosting policy  not be added to or enhanced in any way to appear more like, or to substitute for, the published journal article AbstractThe development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g., model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.

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Section: Research Challenges and Future Directionsmentioning

confidence: 99%

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

521

317

View full text Add to dashboard Cite

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“…Most of the current tools for performance engineering focus on collecting and presenting information for the users, while only few focus on the automation of the performance optimization process (auto-tuning), e.g., the Periscope Tuning Framework (PTF) developed in the EU FP7 ICT AutoTune project [4], [5]. PTF automatically finds optimized system configurations for whole application runs, effectively averaging the benefits of system adaptation over the whole runtime of the application (static tuning).…”

Section: B Static Auto-tuning Of Hpc Applicationsmentioning

confidence: 99%

“…It consists of a split design time and runtime approach to enable dynamic adaption to changing system requirements without a prohibitively large runtime overhead. In the HPC domain auto-tuning is used either at design time to statically tune the system configuration, or at runtime through compute intensive estimation of the dynamic requirements [4], [5]. Combining the approaches from embedded systems and HPC has potential of giving substantial synergies in both domains.…”

Section: Introductionmentioning

confidence: 99%

READEX: Linking two ends of the computing continuum to improve energy-efficiency in dynamic applications

Kjeldsberg

Gocht

Gerndt

et al. 2017

Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 2017

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Abstract-In both the embedded systems and High Performance Computing domains, energy-efficiency has become one of the main design criteria. Efficiently utilizing the resources provided in computing systems ranging from embedded systems to current petascale and future Exascale HPC systems will be a challenging task. Suboptimal designs can potentially cause large amounts of underutilized resources and wasted energy. In both domains, a promising potential for improving efficiency of scalable applications stems from the significant degree of dynamic behaviour, e.g., runtime alternation in application resource requirements and workloads. Manually detecting and leveraging this dynamism to improve performance and energy-efficiency is a tedious task that is commonly neglected by developers. However, using an automatic optimization approach, application dynamism can be analysed at design time and used to optimize system configurations at runtime.The European Union Horizon 2020 READEX (Runtime Exploitation of Application Dynamism for Energy-efficient eXascale computing) project will develop a tools-aided auto-tuning methodology inspired by the system scenario methodology used in embedded systems. Dynamic behaviour of HPC applications will be exploited to achieve improved energy-efficiency and performance. Driven by a consortium of European experts from academia, HPC resource providers, and industry, the READEX project aims at developing the first of its kind generic framework to split design time and runtime automatic tuning while targeting heterogeneous system at the Exascale level. This paper describes plans for the project as well as early results achieved during its first year. Furthermore, it is shown how project results will be brought back into the embedded systems domain.

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“…Most of the current tools for performance engineering focus on collecting and presenting information for the users, while only few focus on the automation of the performance optimization process (auto-tuning), e.g., the Periscope Tuning Framework (PTF) developed in the EU FP7 ICT AutoTune project [10], [11]. PTF automatically finds optimized system configurations for whole application runs, effectively averaging the benefits of system adaptation over the whole run-time of the application (static tuning).…”

Section: B Static Auto-tuning Of Hpc Applicationsmentioning

confidence: 99%

Run-Time Exploitation of Application Dynamism for Energy-Efficient Exascale Computing (READEX)

Oleynik

Gerndt

Schuchart³

et al. 2015

2015 IEEE 18th International Conference on Computational Science and Engineering

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Abstract-Efficiently utilizing the resources provided on current petascale and future exascale systems will be a challenging task, potentially causing a large amount of underutilized resources and wasted energy. A promising potential to improve efficiency of HPC applications stems from the significant degree of dynamic behavior, e.g., run-time alternation in application resource requirements in HPC workloads. Manually detecting and leveraging this dynamism to improve performance and energy-efficiency is a tedious task that is commonly neglected by developers. However, using an automatic optimization approach, application dynamism can be analyzed at design-time and used to optimize system configurations at run-time.The European Union Horizon 2020 READEX project will develop a tools-aided scenario based auto-tuning methodology to exploit the dynamic behavior of HPC applications to achieve improved energy-efficiency and performance. Driven by a consortium of European experts from academia, HPC resource providers, and industry, the READEX project aims at developing the first of its kind generic framework for split design-time runtime automatic tuning for heterogeneous system at the exascale level.

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AutoTune: A Plugin-Driven Approach to the Automatic Tuning of Parallel Applications

Cited by 55 publications

References 9 publications

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

READEX: Linking two ends of the computing continuum to improve energy-efficiency in dynamic applications

Run-Time Exploitation of Application Dynamism for Energy-Efficient Exascale Computing (READEX)

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