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Distributed Processing

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

Algorithm Parameters

The low order moments algorithm in the distributed processing mode has the following parameters:

Algorithm Parameters for Low Order Moments (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
estimatesToCompute	estimatesAll	Estimates to be computed by the algorithm: estimatesAll - all supported moments estimatesMinMax - minimum and maximum estimatesMeanVariance - mean and variance

Computation of low order moments follows the general schema described in Algorithms:

Step 1 - on Local Nodes

In this step, the low order moments 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.

Algorithm Input for Low Order Moments (Distributed Processing, Step 1)

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 low order moments 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.

Algorithm Output for Low Order Moments (Distributed Processing, Step 1)

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. 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.

Partial characteristics computed so far on the local node, each in a \(1 \times p\) numeric table. By default, each table is an object of the HomogenNumericTable class, but you can define the tables as objects of any class derived from NumericTable except PackedSymmetricMatrix, PackedTriangularMatrix, and CSRNumericTable.

Partial Characteristics for Low Order Moments (Distributed Processing, Step 1)

Result ID	Result
partialMinimum	Partial minimums
partialMaximum	Partial maximums
partialSum	Partial sums
partialSumSquares	Partial sums of squares
partialSumSquaresCentered	Partial sums of squared differences from the means

Step 2 - on Master Node

In this step, the low order moments 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.

Algorithm Input for Low Order Moments (Distributed Processing, Step 2)

Input ID	Input
partialResults	A collection that contains numeric tables with partial results computed in Step 1 on local nodes (six numeric tables from each local node). These numeric tables can be objects of any class derived from the NumericTable class except PackedSymmetricMatrix and PackedTriangularMatrix.

In this step, the low order moments 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.

Note

Each result is a pointer to the \(1 \times p\) numeric table that contains characteristics for each feature in the data set. By default, the tables are objects of the HomogenNumericTable class, but you can define each table as an object of any class derived from NumericTable except PackedSymmetricMatrix, PackedTriangularMatrix, and CSRNumericTable.

Algorithm Output for Low Order Moments (Distributed Processing, Step 2)

Result ID	Characteristic
minimum	Minimums
maximum	Maximums
sum	Sums
sumSquares	Sums of squares
sumSquaresCentered	Sums of squared differences from the means
mean	Estimates for the means
secondOrderRawMoment	Estimates for the second order raw moments
variance	Estimates for the variances
standardDeviation	Estimates for the standard deviations
variation	Estimates for the variations

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

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