sdsdot

Calculate the dot product of two single-precision floating-point vectors with extended accumulation.

The dot product (or scalar product) is defined as

Usage

var sdsdot = require( '@stdlib/blas/base/sdsdot' );

sdsdot( N, scalar, x, strideX, y, strideY )

Calculates the dot product of vectors x and y with extended accumulation.

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 = sdsdot( x.length, 0.0, x, 1, y, 1 );
// returns -5.0

The function has the following parameters:

  • N: number of indexed elements.
  • scalar: scalar constant added to the dot product.
  • x: input Float32Array.
  • strideX: index increment for x.
  • y: 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 = sdsdot( 3, 0.0, 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 = sdsdot( 3, 0.0, x1, -2, y1, 1 );
// returns 128.0

sdsdot.ndarray( N, x, strideX, offsetX, y, strideY, offsetY )

Calculates the dot product of vectors x and y with extended accumulation and 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 = sdsdot.ndarray( x.length, 0.0, 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 = sdsdot.ndarray( 3, 0.0, x, 2, 1, y, -1, y.length-1 );
// returns 128.0

Notes

  • If N <= 0, both functions return scalar.
  • sdsdot() corresponds to the BLAS level 1 function sdsdot.

Examples

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

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 out = sdsdot( x.length, 0.0, x, 1, y, -1 );
console.log( out );

C APIs

Usage

#include "stdlib/blas/base/sdsdot.h"

c_sdsdot( N, scalar, *X, strideX, *Y, strideY )

Calculates the dot product of vectors x and y with extended accumulation.

const float x[] = { 1.0f, -2.0f, 3.0f, -4.0f, 5.0f, -6.0f, 7.0f, -8.0f };
const float y[] = { 1.0f, -2.0f, 3.0f, -4.0f, 5.0f, -6.0f, 7.0f, -8.0f };

float v = c_sdsdot( 5, 0.0f, x, 1, y, -1 );
// returns -120.0f

The function accepts the following arguments:

  • N: [in] CBLAS_INT number of indexed elements.
  • scalar: [in] float scalar constant to add to dot product.
  • X: [in] float* first input array.
  • strideX: [in] CBLAS_INT index increment for X.
  • Y: [in] float* second input array.
  • strideY: [in] CBLAS_INT index increment for Y.
float c_sdsdot( const CBLAS_INT N, const float scalar, const float *X, const CBLAS_INT strideX, const float *Y, const CBLAS_INT strideY );

c_sdsdot_ndarray( N, scalar, *X, strideX, offsetX, *Y, strideY, offsetY )

Calculates the dot product of vectors x and y with extended accumulation using alternative indexing semantics.

const float x[] = { 1.0f, -2.0f, 3.0f, -4.0f, 5.0f, -6.0f, 7.0f, -8.0f };
const float y[] = { 1.0f, -2.0f, 3.0f, -4.0f, 5.0f, -6.0f, 7.0f, -8.0f };

float v = c_sdsdot_ndarray( 5, 0.0f, x, 1, 0, y, -1, 7 );
// returns -80.0f

The function accepts the following arguments:

  • N: [in] CBLAS_INT number of indexed elements.
  • scalar: [in] float scalar constant to add to dot product.
  • X: [in] float* first input array.
  • strideX: [in] CBLAS_INT index increment for X.
  • offsetX: [in] CBLAS_INT starting index for X.
  • Y: [in] float* second input array.
  • strideY: [in] CBLAS_INT index increment for Y.
  • offsetY: [in] CBLAS_INT starting index for Y.
float c_sdsdot_ndarray( const CBLAS_INT N, const float scalar, const float *X, const CBLAS_INT strideX, const CBLAS_INT offsetX, const float *Y, const CBLAS_INT strideY, const CBLAS_INT offsetY );

Examples

#include "stdlib/blas/base/sdsdot.h"
#include <stdio.h>

int main( void ) {
    // Create strided arrays:
    const float x[] = { 1.0f, -2.0f, 3.0f, -4.0f, 5.0f, -6.0f, 7.0f, -8.0f };
    const float y[] = { 1.0f, -2.0f, 3.0f, -4.0f, 5.0f, -6.0f, 7.0f, -8.0f };

    // Specify the number of indexed elements:
    const int N = 8;

    // Specify strides:
    const int strideX = 1;
    const int strideY = -1;

    // Compute the dot product:
    float d = c_sdsdot( N, 0.0f, x, strideX, y, strideY );

    // Print the result:
    printf( "dot product: %f\n", d );

    // Compute the dot product:
    d = c_sdsdot_ndarray( N, 0.0f, x, strideX, 0, y, strideY, 7 );

    // Print the result:
    printf( "dot product: %f\n", d );
}

References

  • Lawson, Charles L., Richard J. Hanson, Fred T. Krogh, and David Ronald Kincaid. 1979. "Algorithm 539: Basic Linear Algebra Subprograms for Fortran Usage [F1]." ACM Transactions on Mathematical Software 5 (3). New York, NY, USA: Association for Computing Machinery: 324–25. doi:10.1145/355841.355848.
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