dasum

Usage

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

dasum.main( N, x, stride )

Computes the sum of absolute values.

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

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

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

The function has the following parameters:

• N: number of indexed elements.
• 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 compute the sum of every other value,

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

var x = new Float64Array( [ -2.0, 1.0, 3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ] );

var sum = dasum.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 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

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

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

Computes the sum of absolute values using alternative indexing semantics.

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

var x = new Float64Array( [ -2.0, 1.0, 3.0, -5.0, 4.0, 0.0, -1.0, -3.0 ] );

var sum = dasum.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 Float64Array = require( '@stdlib/array/float64' );

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

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

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

Module

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

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

dasum.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 dasum.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 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 sum = mod.main( N, xptr, 1 );
// returns 15.0

The function has the following parameters:

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

dasum.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 dasum.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 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 sum = mod.ndarray( N, xptr, 1, 0 );
// returns 15.0

The function has the following additional parameters:

• ox: starting index for x.