About stdlib...
We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.
The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.
When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.
To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!
Compute the dot product of
x
andy
.
npm install @stdlib/blas-base-sdot-wasm
Alternatively,
- To load the package in a website via a
script
tag without installation and bundlers, use the ES Module available on theesm
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.
var sdot = require( '@stdlib/blas-base-sdot-wasm' );
Computes the dot product of x
and y
.
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 4.0, 2.0, -3.0, 5.0, -1.0 ] );
var y = new Float32Array( [ 2.0, 6.0, -1.0, -4.0, 8.0 ] );
var z = sdot.main( x.length, x, 1, y, 1 );
// returns -5.0
The function has the following parameters:
- N: number of indexed elements.
- x: first input
Float32Array
. - strideX: index increment for
x
. - y: second input
Float32Array
. - strideY: index increment for
y
.
The N
and stride parameters determine which elements in the strided arrays are accessed at runtime. For example, to calculate the dot product of every other value in x
and the first N
elements of y
in reverse order,
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var y = new Float32Array( [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] );
var z = sdot.main( 3, x, 2, y, -1 );
// returns 9.0
Note that indexing is relative to the first index. To introduce an offset, use typed array
views.
var Float32Array = require( '@stdlib/array-float32' );
// Initial arrays...
var x0 = new Float32Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var y0 = new Float32Array( [ 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
// Create offset views...
var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var y1 = new Float32Array( y0.buffer, y0.BYTES_PER_ELEMENT*3 ); // start at 4th element
var z = sdot.main( 3, x1, -2, y1, 1 );
// returns 128.0
Computes the dot product of x
and y
using alternative indexing semantics.
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 4.0, 2.0, -3.0, 5.0, -1.0 ] );
var y = new Float32Array( [ 2.0, 6.0, -1.0, -4.0, 8.0 ] );
var z = sdot.ndarray( x.length, x, 1, 0, y, 1, 0 );
// returns -5.0
The function has the following additional parameters:
- offsetX: starting index for
x
. - offsetY: starting index for
y
.
While typed array
views mandate a view offset based on the underlying buffer, the offset parameters support indexing semantics based on starting indices. For example, to calculate the dot product of every other value in x
starting from the second value with the last 3 elements in y
in reverse order
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var y = new Float32Array( [ 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );
var z = sdot.ndarray( 3, x, 2, 1, y, -1, y.length-1 );
// returns 128.0
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 sdot.Module( mem );
// returns <Module>
// Initialize the routine:
mod.initializeSync();
Computes the dot product of x
and y
.
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 sdot.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 pointers (i.e., byte offsets) for storing two vectors:
var xptr = 0;
var yptr = N * bytesPerElement( dtype );
// Write vector values to module memory:
mod.write( xptr, oneTo( N, dtype ) );
mod.write( yptr, ones( N, dtype ) );
// Perform computation:
var z = mod.main( N, xptr, 1, yptr, 1 );
console.log( z );
The function has the following parameters:
- N: number of indexed elements.
- xp: first input
Float32Array
pointer (i.e., byte offset). - sx: index increment for
x
. - yp: second input
Float32Array
pointer (i.e., byte offset). - sy: index increment for
y
.
Computes the dot product of x
and y
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 sdot.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 pointers (i.e., byte offsets) for storing two vectors:
var xptr = 0;
var yptr = N * bytesPerElement( dtype );
// Write vector values to module memory:
mod.write( xptr, oneTo( N, dtype ) );
mod.write( yptr, ones( N, dtype ) );
// Perform computation:
var z = mod.ndarray( N, xptr, 1, 0, yptr, 1, 0 );
console.log( z );
The function has the following additional parameters:
- ox: starting index for
x
. - oy: starting index for
y
.
- If
N <= 0
, bothmain
andndarray
methods return0.0
. - This package implements routines using WebAssembly. When provided arrays which are not allocated on a
sdot
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/sdot
. 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/sdot
. 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. sdot()
corresponds to the BLAS level 1 functionsdot
.
var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var sdot = require( '@stdlib/blas-base-sdot-wasm' );
var opts = {
'dtype': 'float32'
};
var x = discreteUniform( 10, 0, 100, opts );
console.log( x );
var y = discreteUniform( x.length, 0, 10, opts );
console.log( y );
var z = sdot.ndarray( x.length, x, 1, 0, y, -1, y.length-1 );
console.log( z );
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.
See LICENSE.
Copyright © 2016-2024. The Stdlib Authors.