XPDL: Extensible Platform Description Language to Support Energy Modeling and Optimization

Keßler, Christoph; Li, Lu; Atalar, Aras; Dobre, Adrian

doi:10.1109/icppw.2015.17

Cited by 10 publications

(15 citation statements)

References 9 publications

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“…For example, a platform description language such as XPDL [14] can be used to guide the automated selection of specializations of user functions and user-defined multibackend components [6].…”

Section: Discussionmentioning

confidence: 99%

“…In the future, the precompiler role will be expanded to include selection of systemspecific user function specializations, guided by a platform description language [14]. The precompiler can either select the most appropriate specialization directly, or include multiple variants and generate logic to select the best one at run-time based on dynamic conditions.…”

Section: Source-to-source Compilermentioning

confidence: 99%

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SkePU 2: Flexible and Type-Safe Skeleton Programming for Heterogeneous Parallel Systems

Ernstsson

Keßler

2017

Int J Parallel Prog

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In this article we present SkePU 2, the next generation of the SkePU C++ skeleton programming framework for heterogeneous parallel systems. We critically examine the design and limitations of the SkePU 1 programming interface. We present a new, flexible and type-safe, interface for skeleton programming in SkePU 2, and a source-to-source transformation tool which knows about SkePU 2 constructs such as skeletons and user functions. We demonstrate how the source-to-source compiler transforms programs to enable efficient execution on parallel heterogeneous systems. We show how SkePU 2 enables new use-cases and applications by increasing the flexibility from SkePU 1, and how programming errors can be caught earlier and easier thanks to improved type safety. We propose a new skeleton, Call, unique in the sense that it does not impose any predefined skeleton structure and can encapsulate arbitrary user-defined multi-backend computations. We also discuss how the sourceto-source compiler can enable a new optimization opportunity by selecting among multiple user function specializations when building a parallel program. Finally, we show that the performance of our prototype SkePU 2 implementation closely matches that of SkePU 1.

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Section: Discussionmentioning

confidence: 99%

Section: Source-to-source Compilermentioning

confidence: 99%

SkePU 2: Flexible and Type-Safe Skeleton Programming for Heterogeneous Parallel Systems

Ernstsson

Keßler

2017

Int J Parallel Prog

Self Cite

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“…Drake generates executables whose performance is optimized with the use of static schedulers such as Crown Schedulers [108]. Power monitoring can take profit of measurement tools such as MeterPU [94] while scheduling can take profit of execution platform modeling languages as XPDL [85] for retargetability. We demonstrate the ability of Drake and its platform backend for Intel architectures to take profit of frequency scaling for individual tasks that run for 1 millisecond or more in a pipeline stage.…”

Section: Resultsmentioning

confidence: 99%

“…We model target platforms using the AMPL [41] syntax features such as the number of cores or topology in islands. While such description format makes difficult the expression of non-numeric parameters, we can easily use other formats such as XPDL [85] thanks to the flexible Pelib framework. Finally, we represent schedules in XML to express the task sequence and starting time for each processor.…”

Section: Softwarementioning

confidence: 99%

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Algorithms and Framework for Energy Efficient Parallel Stream Computing on Many-Core Architectures

Melot¹

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The rise of many-core processor architectures in the market answers to a constantly growing need of processing power to solve more and more challenging problems such as the ones in computing for big data. Fast computation is more and more limited by the very high power required and the management of the considerable heat produced. Many programming models compete to take profit of many-core architectures to improve both execution speed and energy consumption, each with their advantages and drawbacks. The work described in this thesis is based on the dataflow computing approach and investigates the benefits of a carefully pipelined execution of streaming applications, focusing in particular on off-and on-chip memory accesses. As case study, we implement classic and on-chip pipelined versions of mergesort for Intel SCC and Xeon. We see how the benefits of the on-chip pipelining technique are bounded by the underlying architecture, and we explore the problem of fine tuning streaming applications for many-core architectures to optimize for energy given a throughput budget. We propose a novel methodology to compute schedules optimized for energy efficiency given a fixed throughput target. We introduce Drake, derived from Schedeval, a tool that generates pipelined applications for Many-Core architectures and allows the performance testing in time or energy of their static schedule. We show that streaming applications based on Drake compete with specialized implementations and we use Schedeval to demonstrate performance differences between schedules that are otherwise considered as equivalent by a simple model. This work has been supported in parts by CUGS (the Graduate School in Computer Science, Sweden), Vetenskapsrådet, SeRC and EU FP7 EXCESS. Department of Computer and Information ScienceLinköping University SE-581 83 Linköping, Sweden Acknowledgements I would like to thank all members of PELAB for the inspiring and stimulating working environment as well as the passionate discussions around coffee or tea. In particular, my thanks to Kristian Sandahl for his efforts at maintening a strong group culture. Warm thanks to Christoph Kessler, for all fruitful discussions and ideas when my imagination came short, for his support and patience when results came late and for entrusting me with opportunities and responsibilities that taught me valuable experiences. Many thanks to Jörg Keller for sharing his ideas in details about my work, and for always carefully reviewing and challenging any hypothesis I proposed, as it repeatedly resulted in strengthening good ideas and discarding bad ones. I would like to thank all students who participated in the work, providing a precious help to progress. I am grateful to Intel for providing the Single Chip Cloud computer research prototype and their efforts to help in the numerous moments where nothing worked. Many thanks to the National Supercomputer Center (NSC) for providing powerful computing means to run my experiments, TUS for always providing a quick and precious technica...

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Data and Cloud Polymorphic Application Modelling in Multi-clouds and Fog Environments

Verginadis

Kritikos

Patiniotakis

2021

Advanced Information Systems Engineering

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Multi-cloud management prevents vendor lock-in as well as improves the provisioning of cloud applications. However, the optimal deployment of such applications is still impossible, not only due to the dynamicity of the cloud and hybrid environments that host these applications but also due to the use of potentially unsuitable forms or configurations of application components. As such, the real cloud application optimisation can only be achieved by considering all possible component forms and selecting the best one, based on both application requirements and the current context. This gives rise to the era of polymorphic applications which can change form at runtime based on their context. A major prerequisite for the management of such applications is their proper modelling. Therefore, this paper presents extensions on two well-integrated cloud modelling solutions to support the complete specification of polymorphic applications and it presents an illustrative example of their use.

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XPDL: Extensible Platform Description Language to Support Energy Modeling and Optimization

Cited by 10 publications

References 9 publications

SkePU 2: Flexible and Type-Safe Skeleton Programming for Heterogeneous Parallel Systems

SkePU 2: Flexible and Type-Safe Skeleton Programming for Heterogeneous Parallel Systems

Algorithms and Framework for Energy Efficient Parallel Stream Computing on Many-Core Architectures

Data and Cloud Polymorphic Application Modelling in Multi-clouds and Fog Environments

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