https://github.com/stdlib-js/blas-ext-base-wasm-dasumpw

Calculate the sum of absolute values (L1 norm) of double-precision floating-point strided array elements using pairwise summation.
https://github.com/stdlib-js/blas-ext-base-wasm-dasumpw

abs absolute blas extended javascript l1-norm l1norm manhattan math mathematics node node-js nodejs norm statistics stats stdlib sum taxicab total

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Calculate the sum of absolute values (L1 norm) of double-precision floating-point strided array elements using pairwise summation.

Host: GitHub
URL: https://github.com/stdlib-js/blas-ext-base-wasm-dasumpw
Owner: stdlib-js
License: apache-2.0
Created: 2025-03-15T06:59:27.000Z (about 2 months ago)
Default Branch: main
Last Pushed: 2025-03-17T00:42:05.000Z (about 2 months ago)
Last Synced: 2025-03-17T01:31:10.683Z (about 2 months ago)
Topics: abs, absolute, blas, extended, javascript, l1-norm, l1norm, manhattan, math, mathematics, node, node-js, nodejs, norm, statistics, stats, stdlib, sum, taxicab, total
Language: JavaScript
Homepage: https://github.com/stdlib-js/stdlib
Size: 482 KB
Stars: 0
Watchers: 2
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
- Security: SECURITY.md

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# dasumpw

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

> Calculate the sum of absolute values ([_L1_ norm][l1norm]) of double-precision floating-point strided array elements using pairwise summation.

The [_L1_ norm][l1norm] is defined as

```math

\|\mathbf{x}\|_1 = \sum_{i=0}^{n-1} \vert x_i \vert

```

## Installation

```bash

npm install @stdlib/blas-ext-base-wasm-dasumpw

```

Alternatively,

-   To load the package in a website via a `script` tag without installation and bundlers, use the [ES Module][es-module] available on the [`esm`][esm-url] branch (see [README][esm-readme]).

-   If you are using Deno, visit the [`deno`][deno-url] branch (see [README][deno-readme] for usage intructions).

-   For use in Observable, or in browser/node environments, use the [Universal Module Definition (UMD)][umd] build available on the [`umd`][umd-url] branch (see [README][umd-readme]).

The [branches.md][branches-url] file summarizes the available branches and displays a diagram illustrating their relationships.

To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.

## Usage

```javascript

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

```

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

Computes the sum of absolute values ([_L1_ norm][l1norm]) of double-precision floating-point strided array elements using pairwise summation.

```javascript

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

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

var sum = dasumpw.main( x.length, x, 1 );

// returns 5.0

```

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 array are accessed at runtime. For example, to access every other element in `x`,

```javascript

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

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

var sum = dasumpw.main( 4, x, 2 );

// returns 9.0

```

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

```javascript

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

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 sum = dasumpw.main( 4, x1, 2 );

// returns 9.0

```

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

Computes the sum of absolute values ([_L1_ norm][l1norm]) of double-precision floating-point strided array elements using pairwise summation and alternative indexing semantics.

```javascript

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

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

var sum = dasumpw.ndarray( x.length, x, 1, 0 );

// returns 5.0

```

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 access every other element starting from the second element:

```javascript

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

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

var v = dasumpw.ndarray( 4, x, 2, 1 );

// returns 9.0

```

* * *

### Module

#### dasumpw.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.

```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 dasumpw.Module( mem );

// returns 

// Initialize the routine:

mod.initializeSync();

```

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

Computes the sum of absolute values ([_L1_ norm][l1norm]) of double-precision floating-point strided array elements using pairwise summation.

```javascript

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

var oneTo = require( '@stdlib/array-one-to' );

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

// 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 dasumpw.Module( mem );

// returns 

// Initialize the routine:

mod.initializeSync();

// Define a vector data type:

var dtype = 'float64';

// Specify a vector length:

var N = 3;

// Define a pointer (i.e., byte offset) for storing the input vector:

var xptr = 0;

// Write vector values to module memory:

mod.write( xptr, oneTo( N, dtype ) );

// 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`.

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

Computes the sum of absolute values ([_L1_ norm][l1norm]) of double-precision floating-point strided array elements using pairwise summation and alternative indexing semantics.

```javascript

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

var oneTo = require( '@stdlib/array-one-to' );

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

// 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 dasumpw.Module( mem );

// returns 

// Initialize the routine:

mod.initializeSync();

// Define a vector data type:

var dtype = 'float64';

// Specify a vector length:

var N = 3;

// Define a pointer (i.e., byte offset) for storing the input vector:

var xptr = 0;

// Write vector values to module memory:

mod.write( xptr, oneTo( N, dtype ) );

// 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`.

* * *

## 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 `dasumpw` 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/dasumpw`][@stdlib/blas/ext/base/dasumpw]. 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/dasumpw`][@stdlib/blas/ext/base/dasumpw]. 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.

* * *

## Examples

```javascript

var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );

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

var opts = {

    'dtype': 'float64'

};

var x = discreteUniform( 10, 0, 100, opts );

console.log( x );

var sum = dasumpw.ndarray( x.length, x, 1, 0 );

console.log( sum );

```

* * *

## 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 © 2016-2025. The Stdlib [Authors][stdlib-authors].

[npm-image]: http://img.shields.io/npm/v/@stdlib/blas-ext-base-wasm-dasumpw.svg

[npm-url]: https://npmjs.org/package/@stdlib/blas-ext-base-wasm-dasumpw

[test-image]: https://github.com/stdlib-js/blas-ext-base-wasm-dasumpw/actions/workflows/test.yml/badge.svg?branch=main

[test-url]: https://github.com/stdlib-js/blas-ext-base-wasm-dasumpw/actions/workflows/test.yml?query=branch:main

[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/blas-ext-base-wasm-dasumpw/main.svg

[coverage-url]: https://codecov.io/github/stdlib-js/blas-ext-base-wasm-dasumpw?branch=main

[chat-image]: https://img.shields.io/gitter/room/stdlib-js/stdlib.svg

[chat-url]: https://app.gitter.im/#/room/#stdlib-js_stdlib:gitter.im

[stdlib]: https://github.com/stdlib-js/stdlib

[stdlib-authors]: https://github.com/stdlib-js/stdlib/graphs/contributors

[umd]: https://github.com/umdjs/umd

[es-module]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules

[deno-url]: https://github.com/stdlib-js/blas-ext-base-wasm-dasumpw/tree/deno

[deno-readme]: https://github.com/stdlib-js/blas-ext-base-wasm-dasumpw/blob/deno/README.md

[umd-url]: https://github.com/stdlib-js/blas-ext-base-wasm-dasumpw/tree/umd

[umd-readme]: https://github.com/stdlib-js/blas-ext-base-wasm-dasumpw/blob/umd/README.md

[esm-url]: https://github.com/stdlib-js/blas-ext-base-wasm-dasumpw/tree/esm

[esm-readme]: https://github.com/stdlib-js/blas-ext-base-wasm-dasumpw/blob/esm/README.md

[branches-url]: https://github.com/stdlib-js/blas-ext-base-wasm-dasumpw/blob/main/branches.md

[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/blas-ext-base-wasm-dasumpw/main/LICENSE

[mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray

[@stdlib/array/float64]: https://github.com/stdlib-js/array-float64

[@stdlib/wasm/memory]: https://github.com/stdlib-js/wasm-memory

[@stdlib/wasm/module-wrapper]: https://github.com/stdlib-js/wasm-module-wrapper

[@stdlib/blas/ext/base/dasumpw]: https://github.com/stdlib-js/blas-ext-base-dasumpw

[l1norm]: https://en.wikipedia.org/wiki/Norm_%28mathematics%29

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