Intel Summary Statistics Library 1.0 Update is available for downloading

Intel Summary Statistics Library 1.0 Update is available for downloading

Intel Summary Statistics Library 1.0 Update is available for downloading. It includes several benefits and features:

Algorithm for parameterization of correlation matrix. The algorithm transforms the input which lacks property of positive semidefiniteness into the output meeting properties of correlation matrix. The algorithm is based on spectral decomposition method and can be used in financial computations.

Algorithm for computation of quantiles for streaming data. Computation of quantiles is done with pre-defined deterministic error and is highly efficient in terms of memory and speed. The algorithm can be used for processing of datasets which arrive in blocks.

Optimized algorithm for detection of outliers in datasets. The algorithm effectively utilizes all available cores of a multi-core system.

Dynamic libraries for IA-32/Intel 64 Windows* and Linux* based platforms. Intel Summary Statistics Library now provides greater flexibility of linking your application.

Bug fixes and other improvements. Incorrect processing of data in chunks for case of moments/covariance estimators and incorrect memory operations are fixed in the Update. Support of the case when most of the observations have zero weights is integrated into Intel Summary Statistics Library. For greater flexibility rejection level alpha/n is replaced with alpha in the algorithm for detection of outliers. Set of examples that demonstrate use of the library is extended.

Known limitations. Intel Summary Statistics Library algorithms for computation of quantiles (including streaming data case) should be used for task dimension 1.

Intel Summary Statistics Library is solution for parallel processing and analysis of multi-dimensional datasets. It contains algorithms for computation of moments, skewness, kurtosis, variation coefficient, quantiles/order statistics. The library includes rich set of variance-covariance matrix estimators. The solution provides robust methods, tools for detection of outliers and support of missing values in datasets. Extended set of the library algorithms provide progressive processing support that is, allows analyzing the data which arrive in blocks.

The algorithms of the libraryexploit multi-core/SSE advantages of processors.

The library supports C and Fortran API. Windows*/Linux* versions of the library for IA-32/Intel 64 platforms can be downloaded at

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