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feat: add blas/base/dsyrk
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‎lib/node_modules/@stdlib/blas/base/dsyrk/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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# dsyrk
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> Perform one of the symmetric rank `K` operations `C = α*A*A**T + β*C` or `C = α*A**T*A + β*C` where `α` and `β` are scalars, `C` is an `N` by `N` symmetric matrix and `A` is an `N` by `K` matrix in the first case and a `K` by `N` matrix in the second case.
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<section class="usage">
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## Usage
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```javascript
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var dsyrk = require( '@stdlib/blas/base/dsyrk' );
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```
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#### dsyrk( ord, uplo, trans, N, K, α, A, lda, β, C, ldc )
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Performs one of the symmetric rank `K` operations `C = α*A*A**T + β*C` or `C = α*A**T*A + β*C` where `α` and `β` are scalars, `C` is an `N` by `N` symmetric matrix and `A` is an `N` by `K` matrix in the first case and a `K` by `N` matrix in the second case.
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```javascript
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var Float64Array = require( '@stdlib/array/float64' );
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var A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 ] );
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var C = new Float64Array( [ 1.0, 2.0, 3.0, 0.0, 4.0, 5.0, 0.0, 0.0, 6.0 ] );
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dsyrk( 'row-major', 'upper', 'no-transpose', 3, 3, 1.0, A, 3, 1.0, C, 3 );
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// C => <Float64Array>[ 15.0, 34.0, 53.0, 0.0, 81.0, 127.0, 0.0, 0.0, 200.0 ]
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```
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The function has the following parameters:
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- **ord**: storage layout.
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- **uplo**: specifies whether the upper or lower triangular part of the symmetric matrix `C` is supplied.
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- **trans**: specifies whether `C` should be transposed, conjugate-transposed, or not transposed.
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- **N**: order of the matrix `C`.
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- **K**: number of columns or number of rows of the matrix `A`.
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- **α**: scalar constant.
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- **A**: first input matrix stored in linear memory as a [`Float64Array`][mdn-float64array].
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- **lda**: stride of the first dimension of `A` (leading dimension of `A`).
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- **β**: scalar constant.
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- **C**: second input matrix stored in linear memory as a [`Float64Array`][mdn-float64array].
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- **ldc**: stride of the first dimension of `C` (leading dimension of `C`).
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The stride parameters determine how elements in the input arrays are accessed at runtime. For example, to perform matrix multiplication of two subarrays
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<!-- lint disable maximum-len -->
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```javascript
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var Float64Array = require( '@stdlib/array/float64' );
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var A = new Float64Array( [ 1.0, 2.0, 3.0, 0.0, 0.0, 0.0, 4.0, 5.0, 6.0, 0.0, 0.0, 0.0, 7.0, 8.0, 9.0 ] );
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var C = new Float64Array( [ 1.0, 2.0, 3.0, 0.0, 4.0, 5.0, 0.0, 0.0, 6.0 ] );
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dsyrk( 'row-major', 'upper', 'no-transpose', 3, 3, 1.0, A, 6, 1.0, C, 3 );
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// C => <Float64Array>[ 15.0, 34.0, 53.0, 0.0, 81.0, 127.0, 0.0, 0.0, 200.0 ]
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```
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<!-- lint disable maximum-heading-length -->
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#### dsyrk.ndarray( uplo, trans, N, K, α, A, sa1, sa2, oa, β, C, sc1, sc2, oc )
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Performs one of the symmetric rank `K` operations `C = α*A*A**T + β*C` or `C = α*A**T*A + β*C`, using alternative indexing semantics and where `α` and `β` are scalars, `C` is an `N` by `N` symmetric matrix and `A` is an `N` by `K` matrix in the first case and a `K` by `N` matrix in the second case.
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```javascript
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var Float64Array = require( '@stdlib/array/float64' );
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var A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 ] );
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var C = new Float64Array( [ 1.0, 2.0, 3.0, 0.0, 4.0, 5.0, 0.0, 0.0, 6.0 ] );
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dsyrk.ndarray( 'upper', 'no-transpose', 3, 3, 1.0, A, 3, 1, 0, 1.0, C, 3, 1, 0 );
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// C => <Float64Array>[ 15.0, 34.0, 53.0, 0.0, 81.0, 127.0, 0.0, 0.0, 200.0 ]
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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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- **sc1**: stride of the first dimension of `C`.
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- **sc2**: stride of the second dimension of `C`.
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- **oc**: starting index for `C`.
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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 = new Float64Array( [ 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 ] );
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var C = new Float64Array( [ 0.0, 0.0, 1.0, 2.0, 3.0, 0.0, 4.0, 5.0, 0.0, 0.0, 6.0 ] );
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dsyrk.ndarray( 'upper', 'no-transpose', 3, 3, 1.0, A, 3, 1, 1, 1.0, C, 3, 1, 2 );
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// C => <Float64Array>[ 0.0, 0.0, 15.0, 34.0, 53.0, 0.0, 81.0, 127.0, 0.0, 0.0, 200.0 ]
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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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- `dsyrk()` corresponds to the [BLAS][blas] level 3 function [`dsyrk`][blas-dsyrk].
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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 dsyrk = require( '@stdlib/blas/base/dsyrk' );
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var opts = {
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'dtype': 'float64'
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};
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var N = 4;
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var K = 3;
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var A = discreteUniform( N*N, 0, 10, opts ); // 4x4
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var C = discreteUniform( N*K, 0, 10, opts ); // 4x3
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dsyrk( 'row-major', 'lower', 'no-transpose', N, K, 1.0, A, N, 1.0, C, N );
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console.log( C );
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dsyrk.ndarray( 'lower', 'no-transpose', N, K, 1.0, A, N, 1, 0, 1.0, C, N, 1, 0 );
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console.log( C );
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```
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</section>
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<!-- /.examples -->
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<!-- C interface documentation. -->
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* * *
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<section class="c">
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## C APIs
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<!-- Section to include introductory text. Make sure to keep an empty line after the intro `section` element and another before the `/section` close. -->
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<section class="intro">
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</section>
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<!-- /.intro -->
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<!-- C usage documentation. -->
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<section class="usage">
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### Usage
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```c
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#include "stdlib/blas/base/dsyrk.h"
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```
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#### TODO
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TODO.
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```c
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TODO
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```
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TODO
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```c
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TODO
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```
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</section>
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<!-- /.usage -->
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<!-- C API usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
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<section class="notes">
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</section>
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<!-- /.notes -->
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<!-- C API usage examples. -->
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<section class="examples">
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### Examples
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```c
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TODO
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```
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</section>
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<!-- /.examples -->
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</section>
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<!-- /.c -->
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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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[blas-dsyrk]: https://www.netlib.org/lapack/explore-html-3.6.1/d1/d54/group__double__blas__level3_gae0ba56279ae3fa27c75fefbc4cc73ddf.html
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[mdn-float64array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Float64Array
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[mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray
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</section>
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<!-- /.links -->

‎lib/node_modules/@stdlib/blas/base/dsyrk/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 dsyrk = require( './../lib/dsyrk.js' );
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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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* Creates 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 A = uniform( N*N, -10.0, 10.0, options );
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var C = uniform( N*N, -10.0, 10.0, options );
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return benchmark;
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/**
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* Benchmark function.
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*
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* @private
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* @param {Benchmark} b - benchmark instance
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*/
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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 = dsyrk( 'row-major', 'lower', 'no-transpose', N, N, 1.0, A, N, 1.0, C, 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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/**
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* Main execution sequence.
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*
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* @private
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*/
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function main() {
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var len;
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var min;
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var max;
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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 = 4; // 10^max
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for ( i = min; i <= max; i++ ) {
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len = floor( pow( pow( 10, i ), 1.0/2.0 ) );
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f = createBenchmark( len );
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bench( pkg+':size='+(len*len), f );
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}
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}
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main();

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