dispel4py: An Agile Framework for Data-Intensive methods using HPC

Rosa Filgueira Vicente; Malcolm Atkinson; Amrey Krause

doi:10.1007/978-3-319-33742-5_6

dispel4py: An Agile Framework for Data-Intensive methods using HPC

Rosa Filgueira Vicente, Malcolm Atkinson, Amrey Krause

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Abstract / Description of output

Today’s data bonanza and increasing computational power provide many new opportunities for combining observations with sophisticated simulation results to improve complex models and make forecasts by analyzing their relationships. This should lead to well-presented actionable information that can support decisions and contribute trustworthy knowledge. Practitioners in all disciplines: computational scientists, data scientists and decision makers need improved tools to realize such potential. The Python library dispel4py is such a tool. It delivers a simple abstract model in familiar development environments with a fluent path to production use that automatically addresses scale without its users having to reformulate their methods. This depends on optimal mappings to many current HPC and data-intensive platforms.

Original language	English
Title of host publication	Conquering Big Data Using High Performance Computing
Publisher	Springer
Pages	109-137
Number of pages	29
ISBN (Electronic)	978-3-319-33742-5
ISBN (Print)	978-3-319-33740-1
DOIs	https://doi.org/10.1007/978-3-319-33742-5_6
Publication status	Published - 17 Sept 2016

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10.1007/978-3-319-33742-5_6

https://link.springer.com/chapter/10.1007%2F978-3-319-33742-5_6

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title = "dispel4py: An Agile Framework for Data-Intensive methods using HPC",

abstract = "Today{\textquoteright}s data bonanza and increasing computational power provide many new opportunities for combining observations with sophisticated simulation results to improve complex models and make forecasts by analyzing their relationships. This should lead to well-presented actionable information that can support decisions and contribute trustworthy knowledge. Practitioners in all disciplines: computational scientists, data scientists and decision makers need improved tools to realize such potential. The Python library dispel4py is such a tool. It delivers a simple abstract model in familiar development environments with a fluent path to production use that automatically addresses scale without its users having to reformulate their methods. This depends on optimal mappings to many current HPC and data-intensive platforms.",

author = "{Filgueira Vicente}, Rosa and Malcolm Atkinson and Amrey Krause",

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BT - Conquering Big Data Using High Performance Computing

PB - Springer

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dispel4py: An Agile Framework for Data-Intensive methods using HPC

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