npm package: @stdlib/blas-ext-base-wasm-dnansumkbn2
Description: Compute the sum of double-precision floating-point strided array elements, ignoring `NaN` values and using a second-order iterative Kahan–Babuška algorithm.
Author: The Stdlib Authors
Latest version: 0.1.1

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# dnansumkbn2

[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url] <!-- [![dependencies][dependencies-image]][dependencies-url] -->

> Calculate the sum of double-precision floating-point strided array elements, ignoring `NaN` values and using a second-order iterative Kahan–Babuška algorithm.

<section class="intro">

</section>

<!-- /.intro -->

<section class="installation">

## Installation

```bash
npm install @stdlib/blas-ext-base-wasm-dnansumkbn2
```

</section>

<section class="usage">

## Usage

```javascript
var dnansumkbn2 = require( '@stdlib/blas-ext-base-wasm-dnansumkbn2' );
```

#### dnansumkbn2.main( N, x, strideX )

Computes the sum of double-precision floating-point strided array elements, ignoring `NaN` values and using a second-order iterative Kahan–Babuška algorithm.

```javascript
var Float64Array = require( '@stdlib/array-float64' );

var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );

var sum = dnansumkbn2.main( x.length, x, 1 );
// returns 1.0
```

The function has the following parameters:

-   **N**: number of indexed elements.
-   **x**: input [`Float64Array`][@stdlib/array/float64].
-   **strideX**: stride length for `x`.

The `N` and stride parameters determine which elements in the strided arrays are accessed at runtime. For example, to compute the sum of every other element in `x`,

```javascript
var Float64Array = require( '@stdlib/array-float64' );

var x = new Float64Array( [ 1.0, 2.0, NaN, -7.0, NaN, 3.0, 4.0, 2.0 ] );

var v = dnansumkbn2.main( 4, x, 2 );
// returns 5.0
```

Note that indexing is relative to the first index. To introduce an offset, use [`typed array`][mdn-typed-array] views.

<!-- eslint-disable stdlib/capitalized-comments -->

```javascript
var Float64Array = require( '@stdlib/array-float64' );

var x0 = new Float64Array( [ 2.0, 1.0, NaN, -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 = dnansumkbn2.main( 4, x1, 2 );
// returns 5.0
```

#### dnansumkbn2.ndarray( N, x, strideX, offsetX )

Computes the sum of double-precision floating-point strided array elements, ignoring `NaN` values and using a second-order iterative Kahan–Babuška algorithm and alternative indexing semantics.

```javascript
var Float64Array = require( '@stdlib/array-float64' );

var x = new Float64Array( [ 1.0, -2.0, NaN, 2.0 ] );

var v = dnansumkbn2.ndarray( x.length, x, 1, 0 );
// returns 1.0
```

The function has the following additional parameters:

-   **offsetX**: starting index for `x`.

While [`typed array`][mdn-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 element starting from the second element:

```javascript
var Float64Array = require( '@stdlib/array-float64' );

var x = new Float64Array( [ 2.0, 1.0, NaN, -2.0, -2.0, 2.0, 3.0, 4.0 ] );

var v = dnansumkbn2.ndarray( 4, x, 2, 1 );
// returns 5.0
```

* * *

### Module

#### dnansumkbn2.Module( memory )

Returns a new WebAssembly [module wrapper][@stdlib/wasm/module-wrapper] instance which uses the provided WebAssembly [memory][@stdlib/wasm/memory] instance as its underlying memory.

<!-- eslint-disable node/no-sync -->

```javascript
var Memory = require( '@stdlib/wasm-memory' );

// Create a new memory instance with an initial size of 10 pages (640KiB) and a maximum size of 100 pages (6.4MiB):
var mem = new Memory({
    'initial': 10,
    'maximum': 100
});

// Create a BLAS routine:
var mod = new dnansumkbn2.Module( mem );
// returns <Module>

// Initialize the routine:
mod.initializeSync();
```

#### dnansumkbn2.Module.prototype.main( N, xp, sx )

Computes the sum of double-precision floating-point strided array elements, ignoring `NaN` values and using a second-order iterative Kahan–Babuška algorithm.

<!-- eslint-disable node/no-sync -->

```javascript
var Float64Array = require( '@stdlib/array-float64' );
var Memory = require( '@stdlib/wasm-memory' );

// Create a new memory instance with an initial size of 10 pages (640KiB) and a maximum size of 100 pages (6.4MiB):
var mem = new Memory({
    'initial': 10,
    'maximum': 100
});

// Create a BLAS routine:
var mod = new dnansumkbn2.Module( mem );
// returns <Module>

// Initialize the routine:
mod.initializeSync();

// Specify a vector length:
var N = 4;

// Define a pointer (i.e., byte offset) for storing the input vector:
var xptr = 0;

// Write vector values to module memory:
mod.write( xptr, new Float64Array( [ 1.0, 2.0, 3.0, NaN ] ) );

// Perform computation:
var sum = mod.main( N, xptr, 1 );
// returns 6.0
```

The function has the following parameters:

-   **N**: number of indexed elements.
-   **xp**: input [`Float64Array`][@stdlib/array/float64] pointer (i.e., byte offset).
-   **sx**: stride length for `x`.

#### dnansumkbn2.Module.prototype.ndarray( N, xp, sx, ox )

Computes the sum of double-precision floating-point strided array elements, ignoring `NaN` values and using a second-order iterative Kahan–Babuška algorithm and alternative indexing semantics.

<!-- eslint-disable node/no-sync -->

```javascript
var Float64Array = require( '@stdlib/array-float64' );
var Memory = require( '@stdlib/wasm-memory' );

// Create a new memory instance with an initial size of 10 pages (640KiB) and a maximum size of 100 pages (6.4MiB):
var mem = new Memory({
    'initial': 10,
    'maximum': 100
});

// Create a BLAS routine:
var mod = new dnansumkbn2.Module( mem );
// returns <Module>

// Initialize the routine:
mod.initializeSync();

// Specify a vector length:
var N = 4;

// Define a pointer (i.e., byte offset) for storing the input vector:
var xptr = 0;

// Write vector values to module memory:
mod.write( xptr, new Float64Array( [ 1.0, 2.0, 3.0, NaN ] ) );

// Perform computation:
var sum = mod.ndarray( N, xptr, 1, 0 );
// returns 6.0
```

The function has the following additional parameters:

-   **ox**: starting index for `x`.

</section>

<!-- /.usage -->

<section class="notes">

* * *

## Notes

-   If `N <= 0`, both `main` and `ndarray` methods return `0.0`.
-   This package implements routines using WebAssembly. When provided arrays which are not allocated on a `dnansumkbn2` module memory instance, data must be explicitly copied to module memory prior to computation. Data movement may entail a performance cost, and, thus, if you are using arrays external to module memory, you should prefer using [`@stdlib/blas-ext/base/dnansumkbn2`][@stdlib/blas/ext/base/dnansumkbn2]. However, if working with arrays which are allocated and explicitly managed on module memory, you can achieve better performance when compared to the pure JavaScript implementations found in [`@stdlib/blas/ext/base/dnansumkbn2`][@stdlib/blas/ext/base/dnansumkbn2]. Beware that such performance gains may come at the cost of additional complexity when having to perform manual memory management. Choosing between implementations depends heavily on the particular needs and constraints of your application, with no one choice universally better than the other.

</section>

<!-- /.notes -->

<section class="examples">

* * *

## Examples

<!-- eslint no-undef: "error" -->

```javascript
var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var dnansumkbn2 = require( '@stdlib/blas-ext-base-wasm-dnansumkbn2' );

var opts = {
    'dtype': 'float64'
};
var x = discreteUniform( 10, 0, 100, opts );
console.log( x );

var sum = dnansumkbn2.ndarray( x.length, x, 1, 0 );
console.log( sum );
```

</section>

<!-- /.examples -->

<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->

<section class="related">

</section>

<!-- /.related -->

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<section class="main-repo" >

* * *

## Notice

This package is part of [stdlib][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][stdlib], see the main project [repository][stdlib].

#### Community

[![Chat][chat-image]][chat-url]

---

## License

See [LICENSE][stdlib-license].


## Copyright

Copyright &copy; 2016-2026. The Stdlib [Authors][stdlib-authors].

</section>

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