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dscal

Multiply a vector x by a constant alpha.

Installation

npm install @stdlib/blas-base-dscal-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 dscal = require( '@stdlib/blas-base-dscal-wasm' );

dscal.main( N, alpha, x, strideX )

Multiplies a vector x by a constant alpha.

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

var x = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );

dscal.main( x.length, 5.0, x, 1 );
// x => <Float64Array>[ 5.0, 10.0, 15.0, 20.0, 25.0 ]

The function has the following parameters:

• N: number of indexed elements.
• alpha: scalar constant.
• x: input Float64Array.
• 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 multiply every other value in x by alpha in reverse order,

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

var x = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );

dscal.main( 5, 5.0, x, -1 );
// x => <Float64Array>[ 5.0, 10.0, 15.0, 20.0, 25.0 ]

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

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

// Initial array:
var x0 = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );

// Create a typed array view:
var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element

dscal.main( 3, 5.0, x1, -2 );
// x0 => <Float64Array>[ 1.0, 10.0, 3.0, 20.0, 5.0, 30.0 ]

dscal.ndarray( N, alpha, x, strideX, offsetX )

Multiplies a vector x by a constant alpha using alternative indexing semantics.

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

var x = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );

dscal.ndarray( x.length, 5.0, x, 1, 0 );
// x => <Float64Array>[ 5.0, 10.0, 15.0, 20.0, 25.0 ]

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 multiply every other value in x by a constant alpha starting from the second element,

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

var x = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );

dscal.ndarray( 3, 5.0, x, 2, 1 );
// x => <Float64Array>[ 1.0, 10.0, 3.0, 20.0, 5.0, 30.0 ]

---

Module

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

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

dscal.Module.prototype.main( N, α, xp, sx )

Multiplies a vector x by a constant α.

var Memory = require( '@stdlib/wasm-memory' );
var oneTo = require( '@stdlib/array-one-to' );
var ones = require( '@stdlib/array-ones' );
var zeros = require( '@stdlib/array-zeros' );
var bytesPerElement = require( '@stdlib/ndarray-base-bytes-per-element' );

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

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

// Define a vector data type:
var dtype = 'float64';

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

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

// Write vector values to module memory:
mod.write( xptr, oneTo( N, dtype ) );

// Perform computation:
mod.main( N, 5.0, xptr, 1 );

// Read out the results:
var view = zeros( N, dtype );
mod.read( xptr, view );

console.log( view );
// => <Float64Array>[ 5.0, 10.0, 15.0, 20.0, 25.0 ]

The function has the following parameters:

• N: number of indexed elements.
• α: scalar constant.
• xp: input Float64Array pointer (i.e., byte offset).
• sx: index increment for x.

dscal.Module.prototype.ndarray( N, α, xp, sx, ox )

Multiplies a vector x by a constant α using alternative indexing semantics.

var Memory = require( '@stdlib/wasm-memory' );
var oneTo = require( '@stdlib/array-one-to' );
var ones = require( '@stdlib/array-ones' );
var zeros = require( '@stdlib/array-zeros' );
var bytesPerElement = require( '@stdlib/ndarray-base-bytes-per-element' );

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

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

// Define a vector data type:
var dtype = 'float64';

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

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

// Write vector values to module memory:
mod.write( xptr, oneTo( N, dtype ) );

// Perform computation:
mod.ndarray( N, 5.0, xptr, 1, 0 );

// Read out the results:
var view = zeros( N, dtype );
mod.read( xptr, view );

console.log( view );
// => <Float64Array>[ 5.0, 10.0, 15.0, 20.0, 25.0 ]

The function has the following additional parameters:

• ox: starting index for x.

---

Notes

• If N <= 0, x is left unchanged.
• This package implements routines using WebAssembly. When provided arrays which are not allocated on a dscal 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/dscal. 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/dscal. 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.
• dscal() corresponds to the BLAS level 1 function dscal.

---

Examples

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

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

dscal.ndarray( x.length, 5.0, x, 1, 0 );
console.log( x );

---

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.