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feat: add blas/base/gger
PR-URL: #7629 Ref: #2039 Co-authored-by: Athan Reines <[email protected]> Reviewed-by: Athan Reines <[email protected]> Co-authored-by: stdlib-bot <[email protected]>
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‎lib/node_modules/@stdlib/blas/base/gger/README.md

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<!--
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@license Apache-2.0
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Copyright (c) 2025 The Stdlib Authors.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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-->
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# gger
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> Perform the rank 1 operation `A = α*x*y^T + A`.
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<section class="usage">
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## Usage
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```javascript
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var gger = require( '@stdlib/blas/base/gger' );
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```
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#### gger( order, M, N, α, x, sx, y, sy, A, lda )
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Performs the rank 1 operation `A = α*x*y^T + A`, where `α` is a scalar, `x` is an `M` element vector, `y` is an `N` element vector, and `A` is an `M` by `N` matrix.
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```javascript
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var A = [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ];
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var x = [ 1.0, 1.0 ];
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var y = [ 1.0, 1.0, 1.0 ];
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gger( 'row-major', 2, 3, 1.0, x, 1, y, 1, A, 3 );
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// A => [ 2.0, 3.0, 4.0, 5.0, 6.0, 7.0 ]
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```
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The function has the following parameters:
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- **order**: storage layout.
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- **M**: number of rows in the matrix `A`.
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- **N**: number of columns in the matrix `A`.
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- **α**: scalar constant.
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- **x**: an `M` element input array.
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- **sx**: stride length for `x`.
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- **y**: an `N` element input array.
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- **sy**: stride length for `y`.
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- **A**: input matrix stored in linear memory.
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- **lda**: stride of the first dimension of `A` (a.k.a., leading dimension of the matrix `A`).
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The stride parameters determine which elements in the strided arrays are accessed at runtime. For example, to iterate over every other element in `x` and `y`,
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```javascript
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var A = [ 1.0, 4.0, 2.0, 5.0, 3.0, 6.0 ];
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var x = [ 1.0, 0.0, 1.0, 0.0 ];
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var y = [ 1.0, 0.0, 1.0, 0.0, 1.0, 0.0 ];
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gger( 'column-major', 2, 3, 1.0, x, 2, y, 2, A, 2 );
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// A => [ 2.0, 5.0, 3.0, 6.0, 4.0, 7.0 ]
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```
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Note that indexing is relative to the first index. To introduce an offset, use [`typed array`][mdn-typed-array] views.
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<!-- eslint-disable stdlib/capitalized-comments -->
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```javascript
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var Float64Array = require( '@stdlib/array/float64' );
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// Initial arrays...
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var x0 = new Float64Array( [ 0.0, 1.0, 1.0 ] );
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var y0 = new Float64Array( [ 0.0, 1.0, 1.0, 1.0 ] );
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var A = new Float64Array( [ 1.0, 4.0, 2.0, 5.0, 3.0, 6.0 ] );
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// Create offset views...
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var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
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var y1 = new Float64Array( y0.buffer, y0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
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gger( 'column-major', 2, 3, 1.0, x1, -1, y1, -1, A, 2 );
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// A => <Float64Array>[ 2.0, 5.0, 3.0, 6.0, 4.0, 7.0 ]
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```
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#### gger.ndarray( M, N, α, x, sx, ox, y, sy, oy, A, sa1, sa2, oa )
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Performs the rank 1 operation `A = α*x*y^T + A`, using alternative indexing semantics and where `α` is a scalar, `x` is an `M` element vector, `y` is an `N` element vector, and `A` is an `M` by `N` matrix.
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```javascript
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var A = [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ];
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var x = [ 1.0, 1.0 ];
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var y = [ 1.0, 1.0, 1.0 ];
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gger.ndarray( 2, 3, 1.0, x, 1, 0, y, 1, 0, A, 3, 1, 0 );
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// A => [ 2.0, 3.0, 4.0, 5.0, 6.0, 7.0 ]
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```
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The function has the following additional parameters:
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- **sa1**: stride of the first dimension of `A`.
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- **sa2**: stride of the second dimension of `A`.
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- **oa**: starting index for `A`.
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- **ox**: starting index for `x`.
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- **oy**: starting index for `y`.
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While [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying buffer, the offset parameters support indexing semantics based on starting indices. For example,
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```javascript
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var Float64Array = require( '@stdlib/array/float64' );
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var A = [ 0.0, 0.0, 1.0, 4.0, 2.0, 5.0, 3.0, 6.0 ];
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var x = [ 0.0, 1.0, 0.0, 1.0, 0.0 ];
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var y = [ 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0 ];
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gger.ndarray( 2, 3, 1.0, x, 2, 1, y, 2, 1, A, 1, 2, 2 );
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// A => [ 0.0, 0.0, 2.0, 5.0, 3.0, 6.0, 4.0, 7.0 ]
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```
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</section>
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<!-- /.usage -->
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<section class="notes">
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## Notes
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- `gger()` corresponds to the [BLAS][blas] level 2 function [`dger`][dger] with the exception that this implementation works with any array type, not just Float64Arrays. Depending on the environment, the typed versions ([`dger`][@stdlib/blas/base/dger], [`sger`][@stdlib/blas/base/sger], etc.) are likely to be significantly more performant.
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- Both functions support array-like objects having getter and setter accessors for array element access (e.g., [`@stdlib/array/base/accessor`][@stdlib/array/base/accessor]).
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</section>
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<!-- /.notes -->
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<section class="examples">
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## Examples
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<!-- eslint no-undef: "error" -->
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```javascript
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var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
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var gger = require( '@stdlib/blas/base/gger' );
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var opts = {
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'dtype': 'generic'
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};
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var M = 3;
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var N = 5;
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var A = discreteUniform( M*N, 0, 255, opts );
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var x = discreteUniform( M, 0, 255, opts );
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var y = discreteUniform( N, 0, 255, opts );
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gger( 'row-major', M, N, 1.0, x, 1, y, 1, A, N );
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console.log( A );
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gger.ndarray( M, N, 1.0, x, 1, 0, y, 1, 0, A, 1, M, 0 );
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console.log( A );
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```
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</section>
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<!-- /.examples -->
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<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
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<section class="related">
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</section>
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<!-- /.related -->
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<!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
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<section class="links">
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[blas]: http://www.netlib.org/blas
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[dger]: https://www.netlib.org/lapack/explore-html-3.6.1/d7/d15/group__double__blas__level2_ga458222e01b4d348e9b52b9343d52f828.html
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[mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray
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[@stdlib/blas/base/dger]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/blas/base/dger
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[@stdlib/blas/base/sger]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/blas/base/sger
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[@stdlib/array/base/accessor]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/array/base/accessor
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<!-- <related-links> -->
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<!-- </related-links> -->
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</section>
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<!-- /.links -->

‎lib/node_modules/@stdlib/blas/base/gger/benchmark/benchmark.js

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/**
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* @license Apache-2.0
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*
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* Copyright (c) 2025 The Stdlib Authors.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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'use strict';
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// MODULES //
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var bench = require( '@stdlib/bench' );
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var uniform = require( '@stdlib/random/array/uniform' );
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var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var pow = require( '@stdlib/math/base/special/pow' );
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var floor = require( '@stdlib/math/base/special/floor' );
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var pkg = require( './../package.json' ).name;
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var gger = require( './../lib' );
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// VARIABLES //
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var options = {
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'dtype': 'float64'
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};
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// FUNCTIONS //
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/**
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* Create a benchmark function.
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*
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* @private
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* @param {PositiveInteger} N - array dimension size
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* @returns {Function} benchmark function
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*/
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function createBenchmark( N ) {
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var x = uniform( N, -10.0, 10.0, options );
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var y = uniform( N, -10.0, 10.0, options );
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var A = uniform( N*N, -10.0, 10.0, options );
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return benchmark;
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function benchmark( b ) {
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var z;
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var i;
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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z = gger( 'row-major', N, N, 1.0, x, 1, y, 1, A, N );
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if ( isnan( z[ i%z.length ] ) ) {
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b.fail( 'should not return NaN' );
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}
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}
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b.toc();
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if ( isnan( z[ i%z.length ] ) ) {
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b.fail( 'should not return NaN' );
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}
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b.pass( 'benchmark finished' );
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b.end();
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}
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}
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// MAIN //
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function main() {
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var min;
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var max;
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var N;
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var f;
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var i;
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min = 1; // 10^min
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max = 6; // 10^max
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for ( i = min; i <= max; i++ ) {
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N = floor( pow( pow( 10, i ), 1.0/2.0 ) );
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f = createBenchmark( N );
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bench( pkg+':size='+(N*N), f );
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}
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}
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main();

‎lib/node_modules/@stdlib/blas/base/gger/benchmark/benchmark.ndarray.js

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/**
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* @license Apache-2.0
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*
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* Copyright (c) 2025 The Stdlib Authors.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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'use strict';
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// MODULES //
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var bench = require( '@stdlib/bench' );
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var uniform = require( '@stdlib/random/array/uniform' );
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var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var pow = require( '@stdlib/math/base/special/pow' );
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var floor = require( '@stdlib/math/base/special/floor' );
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var pkg = require( './../package.json' ).name;
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var gger = require( './../lib' ).ndarray;
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// VARIABLES //
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var options = {
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'dtype': 'float64'
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};
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// FUNCTIONS //
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/**
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* Create a benchmark function.
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*
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* @private
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* @param {PositiveInteger} N - array dimension size
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* @returns {Function} benchmark function
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*/
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function createBenchmark( N ) {
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var x = uniform( N, -10.0, 10.0, options );
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var y = uniform( N, -10.0, 10.0, options );
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var A = uniform( N*N, -10.0, 10.0, options );
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return benchmark;
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function benchmark( b ) {
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var z;
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var i;
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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z = gger( 'row-major', N, N, 1.0, x, 1, y, 1, A, N );
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if ( isnan( z[ i%z.length ] ) ) {
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b.fail( 'should not return NaN' );
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}
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}
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b.toc();
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if ( isnan( z[ i%z.length ] ) ) {
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b.fail( 'should not return NaN' );
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}
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b.pass( 'benchmark finished' );
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b.end();
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}
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}
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// MAIN //
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function main() {
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var min;
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var max;
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var N;
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var f;
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var i;
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min = 1; // 10^min
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max = 6; // 10^max
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for ( i = min; i <= max; i++ ) {
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N = floor( pow( pow( 10, i ), 1.0/2.0 ) );
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f = createBenchmark( N );
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bench( pkg+':ndarray:size='+(N*N), f );
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}
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}
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main();

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