Distributed Processing
Algorithm Parameters
Parameter

Default Value

Description
 

computeStep 
Not applicable

The parameter required to initialize the algorithm. Can be:
 
algorithmFPType 
float 
The floatingpoint type that the algorithm uses for intermediate computations. Can be
float
or
double
.
 
method 
defaultDense 
Available methods for computation of low order moments:
 
estimatesToCompute 
estimatesAll 
Estimates to be computed by the algorithm:

Step 1  on Local Nodes
Input ID

Input
 

data 
Pointer to the
n _{
i}
x
p
numeric table 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.

Result ID

Result
 

nObservations 
Pointer to the 1 x 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 x
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
.
 
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
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
.

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.

Optimization Notice


Intel's compilers may or may not optimize to the same degree for nonIntel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessordependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.
Notice revision #20110804
