ssumkbn2

Calculate the sum of single-precision floating-point strided array elements using a second-order iterative Kahan–Babuška algorithm.

Installation

npm install @stdlib/blas-ext-base-ssumkbn2

Usage

var ssumkbn2 = require( '@stdlib/blas-ext-base-ssumkbn2' );

ssumkbn2( N, x, stride )

Computes the sum of single-precision floating-point strided array elements using a second-order iterative Kahan–Babuška algorithm.

var Float32Array = require( '@stdlib/array-float32' );

var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );

var v = ssumkbn2( 3, 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 the strided array,

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 = ssumkbn2( 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 = ssumkbn2( 4, x1, 2 );
// returns 5.0

ssumkbn2.ndarray( N, x, stride, offset )

Computes the sum of single-precision floating-point strided array elements using a second-order iterative Kahan–Babuška algorithm and alternative indexing semantics.

var Float32Array = require( '@stdlib/array-float32' );

var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );

var v = ssumkbn2.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 the strided array 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 = ssumkbn2.ndarray( 4, x, 2, 1 );
// returns 5.0

Notes

• If N <= 0, both functions return 0.0.

Examples

var discreteUniform = require( '@stdlib/random-base-discrete-uniform' ).factory;
var filledarrayBy = require( '@stdlib/array-filled-by' );
var ssumkbn2 = require( '@stdlib/blas-ext-base-ssumkbn2' );

var x = filledarrayBy( 10, 'float32', discreteUniform( 0, 100 ) );
console.log( x );

var v = ssumkbn2( x.length, x, 1 );
console.log( v );

References

• Klein, Andreas. 2005. "A Generalized Kahan-Babuška-Summation-Algorithm." Computing 76 (3): 279–93. doi:10.1007/s00607-005-0139-x.

See Also

• @stdlib/blas-ext/base/dsumkbn2: calculate the sum of double-precision floating-point strided array elements using a second-order iterative Kahan–Babuška algorithm.
• @stdlib/blas-ext/base/gsumkbn2: calculate the sum of strided array elements using a second-order iterative Kahan–Babuška algorithm.
• @stdlib/blas-ext/base/snansumkbn2: calculate the sum of single-precision floating-point strided array elements, ignoring NaN values and using a second-order iterative Kahan–Babuška algorithm.
• @stdlib/blas-ext/base/ssum: calculate the sum of single-precision floating-point strided array elements.
• @stdlib/blas-ext/base/ssumkbn: calculate the sum of single-precision floating-point strided array elements using an improved Kahan–Babuška algorithm.
• @stdlib/blas-ext/base/ssumors: calculate the sum of single-precision floating-point strided array elements using ordinary recursive summation.
• @stdlib/blas-ext/base/ssumpw: calculate the sum of single-precision floating-point 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.