Distributed Processing

This mode assumes that the data set is split into nblocks blocks across computation nodes.

Algorithm Parameters

The correlation and variance-covariance matrices algorithm in the distributed processing mode has the following parameters:

Algorithm Parameters for Correlation and Variance-Covariance Matrices Algorithm (Distributed Processing)

Parameter	Default Valude	Description
computeStep	Not applicable	The parameter required to initialize the algorithm. Can be: step1Local - the first step, performed on local nodes step2Master - the second step, performed on a master node
algorithmFPType	float	The floating-point type that the algorithm uses for intermediate computations. Can be float or double.
method	defaultDense	Available methods for computation of low order moments: defaultDense default performance-oriented method singlePassDense implementation of the single-pass algorithm proposed by D.H.D. West sumDense implementation of the algorithm in the cases where the basic statistics associated with the numeric table are pre-computed sums; returns an error if pre-computed sums are not defined fastCSR performance-oriented method for CSR numeric tables singlePassCSR implementation of the single-pass algorithm proposed by D.H.D. West; optimized for CSR numeric tables sumCSR implementation of the algorithm in the cases where the basic statistics associated with the numeric table are pre-computed sums; optimized for CSR numeric tables; returns an error if pre-computed sums are not defined
outputMatrixType	covarianceMatrix	The type of the output matrix. Can be: covarianceMatrix - variance-covariance matrix correlationMatrix - correlation matrix

Computation of correlation and variance-covariance matrices follows the general schema described in Algorithms:

Step 1 - on Local Nodes

In this step, the correlation and variance-covariance matrices algorithm accepts the input described below. Pass the Input ID as a parameter to the methods that provide input for your algorithm. For more details, see Algorithms.

Step 1: Algorithm Input for Correlation and Variance-Covariance Matrices Algorithm (Distributed Processing)

Input ID	Input
data	Pointer to the numeric table of size \(n_i \times p\) that represents the \(i\)-th data block on the local node. While the input for defaultDense, singlePassDense, or sumDense method can be an object of any class derived from NumericTable, the input for fastCSR, singlePassCSR, or sumCSR method can only be an object of the CSRNumericTable class.

In this step, the correlation and variance-covariance matrices algorithm calculates the results described below. Pass the Result ID as a parameter to the methods that access the results of your algorithm. For more details, see Algorithms.

Step 1: Algorithm Output for Correlation and Variance-Covariance Matrices Algorithm (Distributed Processing)

Result ID	Result
nObservations	Pointer to the \(1 \times 1\) numeric table that contains the number of observations processed so far on the local node. Note By default, this result is an object of the HomogenNumericTable class, but you can define the result as an object of any class derived from NumericTable except CSRNumericTable.
crossProduct	Pointer to \(p \times p\) numeric table with the cross-product matrix computed so far on the local node. Note By default, this table is an object of the HomogenNumericTable class, but you can define the result as an object of any class derived from NumericTable except PackedSymmetricMatrix, PackedTriangularMatrix, and CSRNumericTable.
sum	Pointer to \(1 \times p\) numeric table with partial sums computed so far on the local node. Note By default, this table is an object of the HomogenNumericTable class, but you can define the result as an object of any class derived from NumericTable except PackedSymmetricMatrix, PackedTriangularMatrix, and CSRNumericTable.

Step 2 - on Master Node

In this step, the correlation and variance-covariance matrices algorithm accepts the input described below. Pass the Input ID as a parameter to the methods that provide input for your algorithm. For more details, see Algorithms.

Step 2: Algorithm Input for Correlation and Variance-Covariance Matrices Algorithm (Distributed Processing)

Input ID	Input
partialResults	A collection that contains results computed in Step 1 on local nodes (nObservations, crossProduct, and sum). Note The collection can contain objects of any class derived from the NumericTable class except PackedSymmetricMatrix and PackedTriangularMatrix.

In this step, the correlation and variance-covariance matrices algorithm calculates the results described in the following table. Pass the Result ID as a parameter to the methods that access the results of your algorithm. For more details, see Algorithms.

Step 2: Algorithm Output for for Correlation and Variance-Covariance Matrices Algorithm (Distributed Processing)

Result ID	Result
covariance	Use when outputMatrixType``=``covarianceMatrix. Pointer to the numeric table with the \(p \times p\) variance-covariance matrix. Note By default, this result is an object of the HomogenNumericTable class, but you can define the result as an object of any class derived from NumericTable except PackedTriangularMatrix and CSRNumericTable.
correlation	Use when outputMatrixType``=``correlationMatrix. Pointer to the numeric table with the \(p \times p\) correlation matrix. Note By default, this result is an object of the HomogenNumericTable class, but you can define the result as an object of any class derived from NumericTable except PackedTriangularMatrix and CSRNumericTable.
mean	Pointer to the \(1 \times p\) numeric table with means. Note By default, this result is an object of the HomogenNumericTable class, but you can define the result as an object of any class derived from NumericTable except PackedTriangularMatrix, PackedSymmetricMatrix, and CSRNumericTable.

Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex​. Notice revision #20201201