@stdlib/blas-ext-base-sdssumpw
v0.2.2
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Calculate the sum of single-precision floating-point strided array elements using pairwise summation with extended accumulation.
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sdssumpw
Calculate the sum of single-precision floating-point strided array elements using pairwise summation with extended accumulation.
Installation
npm install @stdlib/blas-ext-base-sdssumpw
Usage
var sdssumpw = require( '@stdlib/blas-ext-base-sdssumpw' );
sdssumpw( N, x, stride )
Computes the sum of single-precision floating-point strided array elements using pairwise summation with extended accumulation.
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );
var N = x.length;
var v = sdssumpw( N, x, 1 );
// returns 1.0
The function has the following parameters:
- N: number of indexed elements.
- x: input
Float32Array
. - 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 Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ] );
var v = sdssumpw( 4, x, 2 );
// returns 5.0
Note that indexing is relative to the first index. To introduce an offset, use typed array
views.
var Float32Array = require( '@stdlib/array-float32' );
var x0 = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var v = sdssumpw( 4, x1, 2 );
// returns 5.0
sdssumpw.ndarray( N, x, stride, offset )
Computes the sum of single-precision floating-point strided array elements using pairwise summation with extended accumulation and alternative indexing semantics.
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );
var v = sdssumpw.ndarray( 3, 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 Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var v = sdssumpw.ndarray( 4, x, 2, 1 );
// returns 5.0
Notes
- If
N <= 0
, both functions return0.0
. - Accumulated intermediate values are stored as double-precision floating-point numbers.
Examples
var discreteUniform = require( '@stdlib/random-base-discrete-uniform' ).factory;
var filledarrayBy = require( '@stdlib/array-filled-by' );
var sdssumpw = require( '@stdlib/blas-ext-base-sdssumpw' );
var x = filledarrayBy( 10, 'float32', discreteUniform( 0, 100 ) );
console.log( x );
var v = sdssumpw( x.length, x, 1 );
console.log( v );
References
- Higham, Nicholas J. 1993. "The Accuracy of Floating Point Summation." SIAM Journal on Scientific Computing 14 (4): 783–99. doi:10.1137/0914050.
See Also
@stdlib/blas-ext/base/dssumpw
: calculate the sum of single-precision floating-point strided array elements using pairwise summation with extended accumulation and returning an extended precision result.@stdlib/blas-ext/base/dsumpw
: calculate the sum of double-precision floating-point strided array elements using pairwise summation.@stdlib/blas-ext/base/sdsnansumpw
: calculate the sum of single-precision floating-point strided array elements, ignoring NaN values and using pairwise summation with extended accumulation.@stdlib/blas-ext/base/sdssum
: calculate the sum of single-precision floating-point strided array elements using extended accumulation.@stdlib/blas-ext/base/ssumpw
: calculate the sum of single-precision floating-point strided array elements using pairwise summation.@stdlib/blas-ext/base/gsumpw
: calculate the sum of strided array elements using pairwise summation.
Notice
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.
Community
License
See LICENSE.
Copyright
Copyright © 2016-2024. The Stdlib Authors.