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feat: add BLAS Level 2 routine for sger
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‎lib/node_modules/@stdlib/blas/base/sger/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) 2018 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 isnanf = require( '@stdlib/math/base/assert/is-nanf' );
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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 sger = require( './../lib/sger.js' );
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// VARIABLES //
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var options = {
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'dtype': 'float32'
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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 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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/**
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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 = sger( 'row-major', N, N, 1, 0, x, 1, y, 1, A, N );
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if ( isnanf( 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 ( isnanf( 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 = 6; // 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();

‎lib/node_modules/@stdlib/blas/base/sger/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) 2018 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 isnanf = require( '@stdlib/math/base/assert/is-nanf' );
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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 sger = require( './../lib/ndarray.js' );
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// VARIABLES //
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var options = {
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'dtype': 'float32'
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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 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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/**
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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 = sger( N, N, 1.0, x, 1, 0, y, 1, 0, A, N, 1, 0 );
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if ( isnanf( 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 ( isnanf( 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 = 6; // 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+':ndarray:size='+(len*len), f );
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}
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}
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main();

‎lib/node_modules/@stdlib/blas/base/sger/docs/repl.txt‎

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{{alias}}( ord, 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`
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is an `M` element vector, `y` is an `N` element vector and `A` is an `M` by
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`N` matrix.
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Indexing is relative to the first index. To introduce an offset, use typed
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array views.
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If `M` or `N` or `α` is equal to `0`, the function returns `A` unchanged.
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Parameters
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----------
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ord: string
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Row-major (C-style) or column-major (Fortran-style) order.
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M: integer
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Number of rows in `A`.
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N: integer
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Number of columns in `A`.
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α: number
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Scalar constant.
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x: Float32Array
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First input vector.
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sx: integer
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Index increment for `x`.
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y: Float32Array
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Second input vector.
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sy: integer
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Index increment for `y`.
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A: Float32Array
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Input Matrix.
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lda: integer
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Stride of the first dimension of `A` (a.k.a., leading dimension of the
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matrix `A`).
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Returns
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-------
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A: Float32Array
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Input Matrix.
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Examples
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--------
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// Standard usage:
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> var x = new {{alias:@stdlib/array/float32}}( [ 1.0, 1.0 ] );
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> var y = new {{alias:@stdlib/array/float32}}( [ 1.0, 1.0 ] );
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> var A = new {{alias:@stdlib/array/float32}}( [ 1.0, 2.0, 3.0, 4.0 ] );
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> var ord = 'row-major';
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> {{alias}}( ord, 2, 2, 1.0, x, 1, y, 1, A, 2 )
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<Float32Array>[ 2.0, 3.0, 4.0, 5.0 ]
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// Advanced indexing:
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> x = new {{alias:@stdlib/array/float32}}( [ 1.0, 1.0 ] );
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> y = new {{alias:@stdlib/array/float32}}( [ 1.0, 1.0 ] );
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> A = new {{alias:@stdlib/array/float32}}( [ 1.0, 2.0, 3.0, 4.0 ] );
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> {{alias}}( ord, 2, 2, 1.0, x, -1, y, -1, A, 2 )
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<Float32Array>[ 2.0, 3.0, 4.0, 5.0 ]
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{{alias}}.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
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semantics and where `α` is a scalar, `x` is an `M` element vector, `y` is an
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`N` element vector and `A` is an `M` by `N` matrix.
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While typed array views mandate a view offset based on the underlying
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buffer, the offset parameters support indexing semantics based on starting
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indices.
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Parameters
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----------
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M: integer
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Number of rows in `A`.
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N: integer
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Number of columns in `A`.
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α: number
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Scalar constant.
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x: Float32Array
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First input vector.
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sx: integer
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Index increment for `x`.
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ox: integer
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Starting index for `x`.
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y: Float32Array
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Second input vector.
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sy: integer
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Index increment for `y`.
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oy: integer
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Starting index for `y`.
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A: Float32Array
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Input Matrix.
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sa1: integer
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Stride of the first dimension of `A`.
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sa2: integer
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Stride of the second dimension of `A`.
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oa: integer
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Starting index for `A`.
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Returns
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-------
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A: Float32Array
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Input Matrix.
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Examples
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--------
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> var x = new {{alias:@stdlib/array/float32}}( [ 1.0, 1.0 ] );
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> var y = new {{alias:@stdlib/array/float32}}( [ 1.0, 1.0 ] );
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> var A = new {{alias:@stdlib/array/float32}}( [ 1.0, 2.0, 3.0, 4.0 ] );
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> {{alias}}.ndarray( 2, 2, 1.0, x, 1, 0, y, 1, 0, A, 2, 1, 0 )
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<Float32Array>[ 2.0, 3.0, 4.0, 5.0 ]
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See Also
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--------

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