statistics

Build R-3.0.1 with Intel® C++ Compiler and Intel® MKL on Linux*

R is ‘GNU S’, a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, etc. This guide will show how to build with the Intel® C++ Compiler and BLAS and LAPACK libraries within Intel® Math Kernel Library (Intel® MKL) to improve the performance of R runtime framework.
  • Linux*
  • C/C++
  • Compilateur Intel® C++
  • Bibliothèque Intel® Math Kernel Library
  • R
  • statistics
  • r with icc
  • Using Intel® MKL with R

    Overview

    R is a programming language for statistical computing. The open source package also provides an environment for creating and running R programs. This guide will show how to use the BLAS and LAPACK libraries within Intel® Math Kernel Library (Intel® MKL) to improve the performance of R. To use other Intel MKL functions, you read this article on Extending R with Intel MKL.

    Reference: http://www.r-project.org/

  • Développeurs
  • Intermédiaire
  • Bibliothèque Intel® Math Kernel Library
  • r with mkl
  • statistics
  • R
  • MKL_LIB_PATH
  • Outils de développement
  • Informatique parallèle
  • Intel® Summary Statistics Library: how to deal with missing observations?

    Real life datasets can have missing values. Sociological surveys and measurement of complex biological systems are two examples where the researcher can arrive at the point in which he should do something with missing observations. One can also treat outliers in datasets as samples which are also lost. Intel® Summary Statistics Library already contains functionality to detect outliers or get robust estimates in presence of “suspicious” observations.

    Intel® Summary Statistics Library: why not to use multi-core advantages?

    In my previous posts I described some features and usage model of Intel® Statistics Library. However, there are many available statistical packages that provide good similar functionality. Does Intel® Summary Statistics Library deliver difference, bring something new and specific? The answer is yes. 

    Intel® Summary Statistics Library: how to detect outliers in datasets?

    Earlier I computed various statistical estimates like mean or variance-covariance matrix using Intel® Summary Statistics Library. In those cases I knew for sure that my datasets did not contain “bad” observations (points which do not belong to the distribution which I observed) or outliers. However, in some cases we need to deal with datasets which are contaminated with outliers.

    Intel® Summary Statistics Library: how to process data in chunks?

    In my previous post I considered computation of statistical estimates for in-memory datasets using tools available in Intel® Summary Statistics Library. New days bring new problems, and today I need to compute the same estimates for data which can not fit into memory of a computer.

    Intel® Summary Statistics Library: Several Estimates at One Stroke

    Today it was necessary for me to compute statistical estimates for a dataset. The observations are weighted, and only several components of the random vector had to be analyzed. How often do we solve such tasks and how do we solve them in our every day life? If we meet such problems rarely or their size is small then use of a popular statistical package or development of a data processing program will be a proper way to address the problem. What if I need to process huge data arrays regularly analyzing gene expression levels for example?

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