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Calculate the dot product of two single-precision floating-point vectors with extended accumulation.
The dot product (or scalar product) is defined as
npm install @stdlib/blas-base-sdsdot
Alternatively,
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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 sdsdot = require( '@stdlib/blas-base-sdsdot' );
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
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
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 );
#include "stdlib/blas/base/sdsdot.h"
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 forX
. - Y:
[in] float*
second input array. - strideY:
[in] CBLAS_INT
index increment forY
.
float c_sdsdot( const CBLAS_INT N, const float scalar, const float *X, const CBLAS_INT strideX, const float *Y, const CBLAS_INT strideY );
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 forX
. - offsetX:
[in] CBLAS_INT
starting index forX
. - Y:
[in] float*
second input array. - strideY:
[in] CBLAS_INT
index increment forY
. - offsetY:
[in] CBLAS_INT
starting index forY
.
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 );
#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 );
}
- 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.
@stdlib/blas-base/ddot
: calculate the dot product of two double-precision floating-point vectors.@stdlib/blas-base/dsdot
: calculate the dot product with extended accumulation and result of two single-precision floating-point vectors.@stdlib/blas-base/sdot
: calculate the dot product of two single-precision floating-point vectors.
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