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sasum

Compute the sum of absolute values.

Installation

npm install @stdlib/blas-base-sasum-wasm

Alternatively,

• To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm branch (see README).
• If you are using Deno, visit the deno branch (see README for usage intructions).
• For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the umd branch (see README).

The branches.md 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

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

sasum.main( N, x, stride )

Computes the sum of absolute values.

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

var x = new Float32Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );

var sum = sasum.main( x.length, x, 1 );
// returns 15.0

The function has the following parameters:

• N: number of indexed elements.
• x: input Float32Array.
• strideX: index increment for x.

The N and stride parameters determine which elements in the input strided array are accessed at runtime. For example, to compute the sum of every other value,

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

var x = new Float32Array( [ -2.0, 1.0, 3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ] );

var sum = sasum.main( 4, x, 2 );
// returns 10.0

Note that indexing is relative to the first index. To introduce an offset, use typed array views.

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

// Initial array:
var x0 = new Float32Array( [ 1.0, -2.0, 3.0, -4.0, 5.0, -6.0 ] );

// Create a typed array view:
var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

var sum = sasum.main( 3, x1, 2 );
// returns 12.0

sasum.ndarray( N, x, strideX, offsetX )

Computes the sum of absolute values using alternative indexing semantics.

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

var x = new Float32Array( [ -2.0, 1.0, 3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ] );

var sum = sasum.ndarray( x.length, x, 1, 0 );
// returns 19.0

The function has the following additional parameters:

• offsetX: 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 compute the sum of last three elements,

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

var x = new Float32Array( [ 1.0, -2.0, 3.0, -4.0, 5.0, -6.0 ] );

var sum = sasum.ndarray( 3, x, 1, x.length-3 );
// returns 15.0

// Using a negative stride to sum from the last element:
sum = sasum.ndarray( 3, x, -1, x.length-1 );
// returns 15.0

---

Module

sasum.Module( memory )

Returns a new WebAssembly module wrapper instance which uses the provided WebAssembly memory instance as its underlying memory.

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 sasum.Module( mem );
// returns <Module>

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

sasum.Module.prototype.main( N, xp, sx )

Computes the sum of absolute values.

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 sasum.Module( mem );
// returns <Module>

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

// Define a vector data type:
var dtype = 'float32';

// Specify a vector length:
var N = 5;

// Define pointer (i.e., byte offsets) for storing the input vectors:
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 15.0

The function has the following parameters:

• N: number of indexed elements.
• xp: input Float32Array pointer (i.e., byte offset).
• sx: index increment for x.

sasum.Module.prototype.ndarray( N, xp, sx, ox )

Computes the sum of absolute values using alternative indexing semantics.

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 sasum.Module( mem );
// returns <Module>

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

// Define a vector data type:
var dtype = 'float32';

// Specify a vector length:
var N = 5;

// Define pointer (i.e., byte offsets) 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 15.0

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 sasum 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-base/sasum. 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/base/sasum. 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.
• sasum() corresponds to the BLAS level 1 function sasum.

---

Examples

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

var opts = {
    'dtype': 'float32'
};
var x = discreteUniform( 10, 0, 100, opts );
console.log( x );

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

---

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

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License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.