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Erschienen in:
Parallel and Distributed Processing for Unsupervised Patient Phenotype Representation Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Orlando Tools: Development, Training, and Use of Scalable Applications in Heterogeneous Distributed Computing Environments

verfasst von : Andrei Tchernykh, Alexander Feoktistov, Sergei Gorsky, Ivan Sidorov, Roman Kostromin, Igor Bychkov, Olga Basharina, Vassil Alexandrov, Raul Rivera-Rodriguez

Erschienen in: High Performance Computing

Verlag: Springer International Publishing

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Abstract

We address concepts and principles of the development, training, and use of applications in heterogeneous environments that integrate different computational infrastructures including HPC-clusters, grids, and clouds. Existing differences in the Grid and cloud computing models significantly complicate problem-solving processes in such environments for end-users. In this regards, we propose the toolkit named Orlando Tools for creating scalable applications for solving large-scale scientific and applied problems. It provides mechanisms for the subject domain specification, problem formulation, problem-solving time prediction, problem-solving scheme execution, monitoring, etc. The toolkit supports hands-on training skills for end-users. To demonstrate the practicability and benefits of Orlando Tools, we present an example of the development and use of the scalable application for solving practical problems of warehouse logistics.

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Vorheriges Kapitel Power Efficiency Analysis of a Deep Learning Workload on an IBM “Minsky” Platform
Nächstes Kapitel Methodology for Tailored Linux Distributions Development for HPC Embedded Systems
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Metadaten
Titel
Orlando Tools: Development, Training, and Use of Scalable Applications in Heterogeneous Distributed Computing Environments
verfasst von
Andrei Tchernykh
Alexander Feoktistov
Sergei Gorsky
Ivan Sidorov
Roman Kostromin
Igor Bychkov
Olga Basharina
Vassil Alexandrov
Raul Rivera-Rodriguez
Copyright-Jahr
2019
Buch
High Performance Computing

Print ISBN: 978-3-030-16204-7

Electronic ISBN: 978-3-030-16205-4

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-16205-4_20