High performance statistical computing with parallel R: applications to biology and climate modelling

Autor:	Chongle Pan, Nagiza F. Samatova, Marcia L. Branstetter, Robert L. Hettich, Xiaosong Ma, Jiangtian Li, Shiraj Khan, Arie Shoshani, Auroop R. Ganguly, Guruprasad Kora, Srikanth B. Yoginath
Rok vydání:	2006
Předmět:	History Range (mathematics) Open source Computer science Scalability Computational statistics Statistical analysis Distributed memory Raw data Data science Computer Science Applications Education
Zdroj:	Journal of Physics: Conference Series. 46:505-509
ISSN:	1742-6596 1742-6588
DOI:	10.1088/1742-6596/46/1/069
Popis:	Ultrascale computing and high-throughput experimental technologies have enabled the production of scientific data about complex natural phenomena. With this opportunity, comes a new problem – the massive quantities of data so produced. Answers to fundamental questions about the nature of those phenomena remain largely hidden in the produced data. The goal of this work is to provide a scalable high performance statistical data analysis framework to help scientists perform interactive analyses of these raw data to extract knowledge. Towards this goal we have been developing an open source parallel statistical analysis package, called Parallel R, that lets scientists employ a wide range of statistical analysis routines on high performance shared and distributed memory architectures without having to deal with the intricacies of parallelizing these routines.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a75ba7b880b3465c2d2db7bd8982f06f https://doi.org/10.1088/1742-6596/46/1/069 Zobrazit plný text záznamu