@stdlib/blas-base-wasm-sscal

v0.1.0

Published

8 days ago

Multiply a vector `x` by a scalar `alpha`.

sscal

Multiply a vector x by a constant alpha.

Installation

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

Usage

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

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

Multiplies a vector x by a constant alpha.

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

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

sscal.main( x.length, 5.0, x, 1 );
// x => <Float32Array>[ 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 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 multiply every other value in x by alpha in reverse order,

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

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

sscal.main( 5, 5.0, x, -1 );
// x => <Float32Array>[ 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 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

sscal.main( 3, 5.0, x1, -2 );
// x0 => <Float32Array>[ 1.0, 10.0, 3.0, 20.0, 5.0, 30.0 ]

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

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

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

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

sscal.ndarray( x.length, 5.0, x, 1, 0 );
// x => <Float32Array>[ 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 Float32Array = require( '@stdlib/array-float32' );

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

sscal.ndarray( 3, 5.0, x, 2, 1 );
// x => <Float32Array>[ 1.0, 10.0, 3.0, 20.0, 5.0, 30.0 ]

Module

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

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

sscal.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 sscal.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 a pointer (i.e., byte offset) for storing an 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 );
// => <Float32Array>[ 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 Float32Array pointer (i.e., byte offset).
sx: index increment for x.

sscal.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 sscal.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 a pointer (i.e., byte offset) for storing an 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 );
// => <Float32Array>[ 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 sscal 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/sscal. 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/sscal. 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.
sscal() corresponds to the BLAS level 1 function sscal.

Examples

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

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

sscal.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

License

See LICENSE.