Intel® oneAPI Data Analytics Library Developer Guide and Reference

Developer Guide and Reference

  • 2021.6
  • 04/11/2022
  • Public Content
Contents

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 LaTex Math image. that represents the LaTex Math image.-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 LaTex Math image. numeric table that contains the number of observations processed so far on the local node.
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 LaTex Math image. numeric table with the cross-product matrix computed so far on the local node.
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 LaTex Math image. numeric table with partial sums computed so far on the local node.
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
).
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 LaTex Math image. variance-covariance matrix.
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 LaTex Math image. correlation matrix.
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 LaTex Math image. numeric table with means.
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

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.