feat: add blas/dscal

lib/node_modules/@stdlib/blas/dscal/README.md

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+<!--
+
+@license Apache-2.0
+
+Copyright (c) 2026 The Stdlib Authors.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+-->
+
+# dscal
+
+> Multiply a double-precision floating-point vector by a scalar `alpha`.
+
+<section class="intro">
+
+</section>
+
+<!-- /.intro -->
+
+<section class="usage">
+
+## Usage
+
+```javascript
+var dscal = require( '@stdlib/blas/dscal' );
+```
+
+#### dscal( alpha, x\[, dim] )
+
+Multiplies a double-precision floating-point vector `x` by a scalar `alpha`.
+
+```javascript
+var Float64Array = require( '@stdlib/array/float64' );
+var array = require( '@stdlib/ndarray/array' );
+
+var x = array( new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] ) );
+
+dscal( 5.0, x );
+
+var xbuf = x.data;
+// returns <Float64Array>[ 5.0, 10.0, 15.0, 20.0, 25.0 ]
+```
+
+The function has the following parameters:
+
+-   **alpha**: scalar constant.
+-   **x**: a non-zero-dimensional [`ndarray`][@stdlib/ndarray/ctor] whose underlying data type is `float64`. Must not be read-only.
+-   **dim**: dimension along which to scale vectors. Must be a negative integer. Negative indices are resolved relative to the last array dimension, with the last dimension corresponding to `-1`. Default: `-1`.
+
+For multi-dimensional input [`ndarrays`][@stdlib/ndarray/ctor], the function performs batched computation, such that the function scales each vector in `x` according to the specified dimension index.
+
+```javascript
+var Float64Array = require( '@stdlib/array/float64' );
+var array = require( '@stdlib/ndarray/array' );
+
+var opts = {
+    'shape': [ 2, 3 ]
+};
+var x = array( new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] ), opts );
+
+var v = x.get( 0, 0 );
+// returns 1.0
+
+dscal( 5.0, x );
+
+v = x.get( 0, 0 );
+// returns 5.0
+
+v = x.get( 1, 2 );
+// returns 30.0
+```
+
+</section>
+
+<!-- /.usage -->
+
+<section class="notes">
+
+## Notes
+
+-   Negative indices are resolved relative to the last [`ndarray`][@stdlib/ndarray/ctor] dimension, with the last dimension corresponding to `-1`.
+-   For multi-dimensional [`ndarrays`][@stdlib/ndarray/ctor], batched computation effectively means scaling all elements of `x`; however, the choice of `dim` will significantly affect performance. For best performance, specify a `dim` which best aligns with the [memory layout][@stdlib/ndarray/orders] of provided [`ndarrays`][@stdlib/ndarray/ctor].
+-   `dscal()` provides a higher-level interface to the [BLAS][blas] level 1 function [`dscal`][@stdlib/blas/base/dscal].
+
+</section>
+
+<!-- /.notes -->
+
+<section class="examples">
+
+## Examples
+
+<!-- eslint no-undef: "error" -->
+
+```javascript
+var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
+var ndarray2array = require( '@stdlib/ndarray/to-array' );
+var array = require( '@stdlib/ndarray/array' );
+var dscal = require( '@stdlib/blas/dscal' );
+
+var opts = {
+    'dtype': 'float64'
+};
+
+var x = array( discreteUniform( 10, 0, 100, opts ), {
+    'shape': [ 5, 2 ]
+});
+console.log( ndarray2array( x ) );
+
+dscal( 5.0, x );
+console.log( ndarray2array( x ) );
+```
+
+</section>
+
+<!-- /.examples -->
+
+<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
+
+<section class="related">
+
+</section>
+
+<!-- /.related -->
+
+<!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
+
+<section class="links">
+
+[blas]: http://www.netlib.org/blas
+
+[@stdlib/ndarray/ctor]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/ctor
+
+[@stdlib/ndarray/orders]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/orders
+
+[@stdlib/blas/base/dscal]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/blas/base/dscal
+
+<!-- <related-links> -->
+
+<!-- </related-links> -->
+
+</section>
+
+<!-- /.links -->

lib/node_modules/@stdlib/blas/dscal/benchmark/benchmark.js

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+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2026 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+// MODULES //
+
+var bench = require( '@stdlib/bench' );
+var isnan = require( '@stdlib/math/base/assert/is-nan' );
+var pow = require( '@stdlib/math/base/special/pow' );
+var uniform = require( '@stdlib/random/array/uniform' );
+var array = require( '@stdlib/ndarray/array' );
+var format = require( '@stdlib/string/format' );
+var pkg = require( './../package.json' ).name;
+var dscal = require( './../lib/main.js' );
+
+
+// VARIABLES //
+
+var opts = {
+	'dtype': 'float64'
+};
+
+
+// FUNCTIONS //
+
+/**
+* Creates a benchmark function.
+*
+* @private
+* @param {PositiveInteger} len - array length
+* @returns {Function} benchmark function
+*/
+function createBenchmark( len ) {
+	var x = array( uniform( len, -100.0, 100.0, opts ) );
+	return benchmark;
+
+	/**
+	* Benchmark function.
+	*
+	* @private
+	* @param {Benchmark} b - benchmark instance
+	*/
+	function benchmark( b ) {
+		var d;
+		var i;
+
+		b.tic();
+		for ( i = 0; i < b.iterations; i++ ) {
+			d = dscal( 1.0, x );
+			if ( typeof d !== 'object' ) {
+				b.fail( 'should return an ndarray' );
+			}
+		}
+		b.toc();
+		if ( isnan( d.get( i%len ) ) ) {
+			b.fail( 'should not return NaN' );
+		}
+		b.pass( 'benchmark finished' );
+		b.end();
+	}
+}
+
+
+// MAIN //
+
+/**
+* Main execution sequence.
+*
+* @private
+*/
+function main() {
+	var len;
+	var min;
+	var max;
+	var f;
+	var i;
+
+	min = 1; // 10^min
+	max = 6; // 10^max
+
+	for ( i = min; i <= max; i++ ) {
+		len = pow( 10, i );
+		f = createBenchmark( len );
+		bench( format( '%s:len=%d', pkg, len ), f );
+	}
+}
+
+main();

lib/node_modules/@stdlib/blas/dscal/benchmark/benchmark.stacks.js

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+/**
+* @license Apache-2.0
+*
+* Copyright (c) 2026 The Stdlib Authors.
+*
+* Licensed under the Apache License, Version 2.0 (the "License");
+* you may not use this file except in compliance with the License.
+* You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+'use strict';
+
+// MODULES //
+
+var bench = require( '@stdlib/bench' );
+var isnan = require( '@stdlib/math/base/assert/is-nan' );
+var pow = require( '@stdlib/math/base/special/pow' );
+var uniform = require( '@stdlib/random/array/uniform' );
+var numel = require( '@stdlib/ndarray/base/numel' );
+var array = require( '@stdlib/ndarray/array' );
+var format = require( '@stdlib/string/format' );
+var pkg = require( './../package.json' ).name;
+var dscal = require( './../lib/main.js' );
+
+
+// VARIABLES //
+
+var OPTS = {
+	'dtype': 'float64'
+};
+
+
+// FUNCTIONS //
+
+/**
+* Creates a benchmark function.
+*
+* @private
+* @param {PositiveIntegerArray} shape - array shape
+* @returns {Function} benchmark function
+*/
+function createBenchmark( shape ) {
+	var x;
+	var N;
+	var o;
+
+	N = numel( shape );
+	o = {
+		'shape': shape
+	};
+	x = array( uniform( N, -100.0, 100.0, OPTS ), o );
+
+	return benchmark;
+
+	/**
+	* Benchmark function.
+	*
+	* @private
+	* @param {Benchmark} b - benchmark instance
+	*/
+	function benchmark( b ) {
+		var d;
+		var i;
+
+		b.tic();
+		for ( i = 0; i < b.iterations; i++ ) {
+			d = dscal( 1.0, x );
+			if ( typeof d !== 'object' ) {
+				b.fail( 'should return an ndarray' );
+			}
+		}
+		b.toc();
+		if ( isnan( d.iget( i%N ) ) ) {
+			b.fail( 'should not return NaN' );
+		}
+		b.pass( 'benchmark finished' );
+		b.end();
+	}
+}
+
+
+// MAIN //
+
+/**
+* Main execution sequence.
+*
+* @private
+*/
+function main() {
+	var shape;
+	var min;
+	var max;
+	var N;
+	var f;
+	var i;
+
+	min = 1; // 10^min
+	max = 6; // 10^max
+
+	for ( i = min; i <= max; i++ ) {
+		N = pow( 10, i );
+
+		shape = [ 2, N/2 ];
+		f = createBenchmark( shape );
+		bench( format( '%s::stacks:size=%d,ndims=%d,shape=(%s)', pkg, N, shape.length, shape.join( ',' ) ), f );
+
+		shape = [ N/2, 2 ];
+		f = createBenchmark( shape );
+		bench( format( '%s::stacks:size=%d,ndims=%d,shape=(%s)', pkg, N, shape.length, shape.join( ',' ) ), f );
+	}
+}
+
+main();