snrm2
Calculate the L2-norm of a single-precision floating-point vector.
Usage
var snrm2 = require( '@stdlib/blas/base/snrm2-wasm' );
snrm2.main( N, x, strideX )
Calculates the L2-norm of a single-precision floating-point vector.
var Float32Array = require( '@stdlib/array/float32' );
var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );
var z = snrm2.main( 3, x, 1 );
// returns 3.0
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 L2-norm of every other element in x
,
var Float32Array = require( '@stdlib/array/float32' );
var x = new Float32Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ] );
var z = snrm2.main( 4, x, 2 );
// returns 5.0
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( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
// Create a typed array view:
var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var z = snrm2.main( 4, x1, 2 );
// returns 5.0
snrm2.ndarray( N, x, strideX, offsetX )
Calculates the L2-norm of a single-precision floating-point vector using alternative indexing semantics.
var Float32Array = require( '@stdlib/array/float32' );
var x = new Float32Array( [ 1.0, -2.0, 2.0 ] );
var z = snrm2.ndarray( 3, x, 1, 0 );
// returns 3.0
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 calculate the L2-norm for every other value in x
starting from the second value,
var Float32Array = require( '@stdlib/array/float32' );
var x = new Float32Array( [ 2.0, 1.0, 2.0, -2.0, -2.0, 2.0, 3.0, 4.0 ] );
var z = snrm2.ndarray( 4, x, 2, 1 );
// returns 5.0
Module
snrm2.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 snrm2.Module( mem );
// returns <Module>
// Initialize the routine:
mod.initializeSync();
snrm2.Module.prototype.main( N, xp, sx )
Computes the L2-norm of a single-precision floating-point vector.
var Memory = require( '@stdlib/wasm/memory' );
var oneTo = require( '@stdlib/array/one-to' );
// 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 snrm2.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 the input vector:
var xptr = 0;
// Write vector values to module memory:
mod.write( xptr, oneTo( N, dtype ) );
// Perform computation:
var out = mod.main( N, xptr, 1 );
// returns ~7.42
The function has the following parameters:
- N: number of indexed elements.
- xp: input
Float32Array
pointer (i.e., byte offset). - sx: index increment for
x
.
snrm2.Module.prototype.ndarray( N, xp, sx, ox )
Computes the L2-norm of a single-precision floating-point vector using alternative indexing semantics.
var Memory = require( '@stdlib/wasm/memory' );
var oneTo = require( '@stdlib/array/one-to' );
// 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 snrm2.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 the input vector:
var xptr = 0;
// Write vector values to module memory:
mod.write( xptr, oneTo( N, dtype ) );
// Perform computation:
var out = mod.ndarray( N, xptr, 1, 0 );
// returns ~7.42
The function has the following additional parameters:
- ox: starting index for
x
.
Notes
- If
N <= 0
, bothmain
andndarray
methods return0.0
. - This package implements routines using WebAssembly. When provided arrays which are not allocated on a
snrm2
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/snrm2
. 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/snrm2
. 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. snrm2()
corresponds to the BLAS level 1 functionsnrm2
.
Examples
var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
var snrm2 = require( '@stdlib/blas/base/snrm2-wasm' );
var opts = {
'dtype': 'float32'
};
var x = discreteUniform( 10, 0, 100, opts );
console.log( x );
var out = snrm2.ndarray( x.length, x, 1, 0 );
console.log( out );