Patterns for High Performance Multiscale Computing

Alowayyed, Saad; Piontek, Tomasz; Suter, James L.; Hoenen, O.; Groen, Derek; Luk, O. O.; Bosak, Bartosz; Kopta, Piotr; Kurowski, Krzysztof; Perks, Oliver; Brabazon, Keeran; Jancauskas, Vytautas; Coster, D.; Coveney, Peter V.; Hoekstra, Alfons G.

doi:10.1016/j.future.2018.08.045

Cited by 22 publications

(22 citation statements)

References 28 publications

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“…Best practices of software quality management applied by us in other works [32] [33] are in this case transferred to PoC projects, which are usually shorter than typical software projects while keeping agile methodologies. However, maintaining the quality of the software development process in the PoC paradigm guarantees smooth use of software artifacts in the deployment phase [34]. This allows to avoid a frequent situation, when pilot projects or prototypes implemented in the R&D&I area, encounter barriers related to scalability, security or change management in software, at entering the stage of product development.…”

Section: Impact and Sustainabilitymentioning

confidence: 99%

A Concept of Innovation Hub for Smart Applications, Enabling Pro-active Approach to Urban Policy and Planning Processes

Mazurek

Stroiński

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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In recent years, the question of how to develop smart applications in environments where solutions already exist has become a challenge for many trends of technological transformation e.g. in industry, transport or smart cities. The latter trend is additionally very sensitive because of the social dimension of the impact of transformation, where the human being should be in the centre of attention. In this paper we present the concept of the Innovation Hub in Poznań, which is a framework for digital transformation focused on integration and development towards proactive digital city system. Starting from the pillars of the digital transformation, i.e. the City Development Strategy and existing wellfunctioning smart city components, we justify the evolutionary model of transformation by creating favorable conditions for the implementation of Proof of Concept projects with the active participation of residents. In addition, we point out examples of projects that were linked to decision-making processes.

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Section: Impact and Sustainabilitymentioning

confidence: 99%

A Concept of Innovation Hub for Smart Applications, Enabling Pro-active Approach to Urban Policy and Planning Processes

Mazurek

Stroiński

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…Here we present a part of the software and algorithmic solutions proposed in ComPat, which we use in our implementation of a fusion application. Additional solutions from ComPat are described in [12,13] and out of scope in this work, as they fit in other categories of applications.…”

Section: The Compat Projectmentioning

confidence: 99%

“…To estimate the performance of the coupled application, a smaller set of metrics has to be extracted from such comprehensive profiles in order to combine them with the coupling topology description. The Optimization part [13] of the ComPat software stack is a service that allows benchmarks to run automatically on all available resource types, gathers profiling information into a performance database, allows end-user to select the metric to be optimized (cpu-hours, time to completion, energy), and chooses the proper configuration to minimize cost of the selected metric.…”

Section: (B) Estimate the Performance Of Multiscale Applicationsmentioning

confidence: 99%

Application of the extreme scaling computing pattern on multiscale fusion plasma modelling

Luk

Hoenen

Perks

et al. 2019

Phil. Trans. R. Soc. A.

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The extreme scaling pattern of the ComPat project is applied to a multi-scale workflow relevant to the magnetically confined fusion problem. This workflow combines transport, turbulence and equilibrium codes (together with additional auxiliaries such as initial conditions and numerical module), which aims at calculating the behaviour of a fusion plasma on long (transport) time scales based on information from much faster (turbulence) time scales. Initial findings of profile measurements are reported in this paper and indicate that, depending on the chosen performance metric for defining ‘cost’, such as time to completion, efficiency and total energy consumption of the mutliscale workflow, different choices on the number of cores would be made when determining the optimal execution configuration. A variant of the workflow which increases the inherent parallelism is presented, and shown to produce equivalent results at (typically) lower cost compared with the original workflow. This article is part of the theme issue ‘Multiscale modelling, simulation and computing: from the desktop to the exascale’.

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“…Generally, applications running in cloud environment are designed to deal with small to extremely large number of users, is termed as scalability.Multi Scale Applications in cloud consists of variable payload i.e. the applications whose payload gets changed with time scale [1,2].Usability of cloud applications is increasing day by day and QoS is an important factor to understand the cloud application's performance.The service response time, throughput, bandwidth, resources utilization, waiting time, processing time are the factors on which performance of cloud application is defined and QoS is measured to complement the performance for what the cloud is intended. Hence, on increase in number of users & service…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Multi Scale Applications on Cloud Systems

Kumbhar*,

Kumar

2019

IJRTE

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Cloud Computing is the most adoptable technology in use recently where bigger and complex applications are deployed and used on cloud server. Multi scaled applications are such applications which have multiple attributes (data size, number of requests, number of concurrent users etc.) that can cause to poor performance of its application service. For better performance, it needs to be measured, analyzed and optimized using tools, strategies or algorithms. The multi scale application’s performance is measured at end user level, network level and data center level. In this paper, the focus is measuring the performance of multi scale applications in terms of response time and throughput using CloudAnalyst simulator. Multiple scenarioslike varying data size,varying number of concurrent users and varying number of requests are simulated for multiscale application and tested.The results obtained shown that response time and throughput of application gets reflected mostly by Data Size, number of requests per user and number of concurrent users. It is also observed that the most significant factor is data size along with huge volume of concurrent users, impacts the response time of multi scale application.Hench this research recommends that multi scale data I.e. data type and data size plays vital role in cloud based multi scale application’s performance measurement.

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Patterns for High Performance Multiscale Computing

Cited by 22 publications

References 28 publications

A Concept of Innovation Hub for Smart Applications, Enabling Pro-active Approach to Urban Policy and Planning Processes

A Concept of Innovation Hub for Smart Applications, Enabling Pro-active Approach to Urban Policy and Planning Processes

Application of the extreme scaling computing pattern on multiscale fusion plasma modelling

Performance Analysis of Multi Scale Applications on Cloud Systems

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