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Calculate the sum of doubleprecision floatingpoint strided array elements using pairwise summation.
npm install @stdlib/blasextbasedsumpw
var dsumpw = require( '@stdlib/blasextbasedsumpw' );
Computes the sum of doubleprecision floatingpoint strided array elements using pairwise summation.
var Float64Array = require( '@stdlib/arrayfloat64' );
var x = new Float64Array( [ 1.0, 2.0, 2.0 ] );
var N = x.length;
var v = dsumpw( N, x, 1 );
// returns 1.0
The function has the following parameters:
 N: number of indexed elements.

x: input
Float64Array
. 
stride: index increment for
x
.
The N
and stride parameters determine which elements in the strided array are accessed at runtime. For example, to compute the sum of every other element in x
,
var Float64Array = require( '@stdlib/arrayfloat64' );
var x = new Float64Array( [ 1.0, 2.0, 2.0, 7.0, 2.0, 3.0, 4.0, 2.0 ] );
var v = dsumpw( 4, x, 2 );
// returns 5.0
Note that indexing is relative to the first index. To introduce an offset, use typed array
views.
var Float64Array = require( '@stdlib/arrayfloat64' );
var x0 = new Float64Array( [ 2.0, 1.0, 2.0, 2.0, 2.0, 2.0, 3.0, 4.0 ] );
var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var v = dsumpw( 4, x1, 2 );
// returns 5.0
Computes the sum of doubleprecision floatingpoint strided array elements using pairwise summation and alternative indexing semantics.
var Float64Array = require( '@stdlib/arrayfloat64' );
var x = new Float64Array( [ 1.0, 2.0, 2.0 ] );
var N = x.length;
var v = dsumpw.ndarray( N, x, 1, 0 );
// returns 1.0
The function has the following additional parameters:

offset: starting index for
x
.
While typed array
views mandate a view offset based on the underlying buffer
, the offset
parameter supports indexing semantics based on a starting index. For example, to calculate the sum of every other value in x
starting from the second value
var Float64Array = require( '@stdlib/arrayfloat64' );
var x = new Float64Array( [ 2.0, 1.0, 2.0, 2.0, 2.0, 2.0, 3.0, 4.0 ] );
var v = dsumpw.ndarray( 4, x, 2, 1 );
// returns 5.0
 If
N <= 0
, both functions return0.0
.  In general, pairwise summation is more numerically stable than ordinary recursive summation (i.e., "simple" summation), with slightly worse performance. While not the most numerically stable summation technique (e.g., compensated summation techniques such as the Kahan–BabuškaNeumaier algorithm are generally more numerically stable), pairwise summation strikes a reasonable balance between numerical stability and performance. If either numerical stability or performance is more desirable for your use case, consider alternative summation techniques.
var discreteUniform = require( '@stdlib/randombasediscreteuniform' ).factory;
var filledarrayBy = require( '@stdlib/arrayfilledby' );
var dsumpw = require( '@stdlib/blasextbasedsumpw' );
var x = filledarrayBy( 10, 'float64', discreteUniform( 0, 100 ) );
console.log( x );
var v = dsumpw( x.length, x, 1 );
console.log( v );
 Higham, Nicholas J. 1993. "The Accuracy of Floating Point Summation." SIAM Journal on Scientific Computing 14 (4): 783–99. doi:10.1137/0914050.

@stdlib/blasext/base/dnansumpw
: calculate the sum of doubleprecision floatingpoint strided array elements, ignoring NaN values and using pairwise summation. 
@stdlib/blasext/base/dsum
: calculate the sum of doubleprecision floatingpoint strided array elements. 
@stdlib/blasext/base/dsumkbn2
: calculate the sum of doubleprecision floatingpoint strided array elements using a secondorder iterative Kahan–Babuška algorithm. 
@stdlib/blasext/base/dsumors
: calculate the sum of doubleprecision floatingpoint strided array elements using ordinary recursive summation. 
@stdlib/blasext/base/gsumpw
: calculate the sum of strided array elements using pairwise summation. 
@stdlib/blasext/base/ssumpw
: calculate the sum of singleprecision floatingpoint strided array elements using pairwise summation.
This package is part of stdlib, a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.
For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.
See LICENSE.
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