Self-adaptive concurrent components

Österlund, Erik; Löwe, Welf

doi:10.1007/s10515-017-0219-0

Cited by 3 publications

(2 citation statements)

References 42 publications

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“…To that end, these technologies transform data into information into actionable knowledge (value) while managing challenging quantities (volume, velocity) and qualities (variety, veracity, validity) of data (Gandomi & Haider, 2015). Dealing with data quantity and quality relies on technologies such as parallel and real-time computing and compiler technologies (including meta-modelling, interpreting, and composition of heterogeneous data sources), while transforming data into actionable knowledge relies on technologies such as data-mining, machine learning, simulations, and context-awareness (Kessler & Löwe, 2012;Österlund & Löwe, 2018). We highlight two challenges on data driven technologies connected to the objectives of smarter systems.…”

Section: Data Driven Technologiesmentioning

confidence: 99%

A Research Agenda for Smarter Cyber-Physical Systems

Weyns

Andersson

Caporuscio

et al. 2022

JID

Self Cite

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With the advancing digitisation of society and industry we observe a progressing blending of computational, physical, and social processes. The trustworthiness and sustainability of these systems will be vital for our society. However, engineering modern computing systems is complex as they have to: i) operate in uncertain and continuously changing environments, ii) deal with huge amounts of data, and iii) require seamless interaction with human operators. To that end, we argue that both systems and the way we engineer them must become smarter. With smarter we mean that systems and engineering processes adapt and evolve themselves through a perpetual process that continuously improves their capabilities and utility to deal with the uncertainties and amounts of data they face. We highlight key engineering areas: cyber-physical systems, self-adaptation, data-driven technologies, and visual analytics, and outline key challenges in each of them. From this, we propose a research agenda for the years to come.

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Section: Data Driven Technologiesmentioning

confidence: 99%

A Research Agenda for Smarter Cyber-Physical Systems

Weyns

Andersson

Caporuscio

et al. 2022

JID

Self Cite

View full text Add to dashboard Cite

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“…In their work, Österlund and Löwe [10] present an approach for runtime selection of implementation variants based on varying contexts, which shares similar goals as COMPAR. While their focus lies on addressing the optimal variant selection process, our paper shifts the responsibility of selection to the runtime system.…”

Section: Related Workmentioning

confidence: 99%

Work Distribution of Data-Parallel Applications on Heterogeneous Systems

Memeti

Pllana

2016

Lecture Notes in Computer Science

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Heterogeneous systems, consisting of CPUs and GPUs, offer the capability to address the demands of compute-and data-intensive applications. However, programming such systems is challenging, requiring knowledge of various parallel programming frameworks. This paper introduces COMPAR, a component-based parallel programming framework that enables the exposure and selection of multiple implementation variants of components at runtime. The framework leverages compiler directive-based language extensions to annotate the source code and generate the necessary glue code for the StarPU runtime system. COM-PAR provides a unified view of implementation variants and allows for intelligent selection based on runtime context. Our evaluation demonstrates the effectiveness of COMPAR through benchmark applications. The proposed approach simplifies heterogeneous parallel programming and promotes code reuse while achieving optimal performance.

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Enabling Dynamic Selection of Implementation Variants in Component-Based Parallel Programming for Heterogeneous Systems

Memeti

2024

Lecture Notes in Computer Science

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Self-adaptive concurrent components

Cited by 3 publications

References 42 publications

A Research Agenda for Smarter Cyber-Physical Systems

A Research Agenda for Smarter Cyber-Physical Systems

Work Distribution of Data-Parallel Applications on Heterogeneous Systems

Enabling Dynamic Selection of Implementation Variants in Component-Based Parallel Programming for Heterogeneous Systems

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