feat: add C implementation to blas/base/ssyr

PR-URL: #7127
Ref: #2039
Co-authored-by: Athan Reines <[email protected]>
Reviewed-by: Athan Reines <[email protected]>

Author: ShabiShett07

lib/node_modules/@stdlib/blas/base/ssyr/README.md

@@ -2,7 +2,7 @@
 
 @license Apache-2.0
 
-Copyright (c) 2025 The Stdlib Authors.
+Copyright (c) 2024 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.
@@ -20,7 +20,7 @@ limitations under the License.
 
 # ssyr
 
-> Perform the symmetric rank 1 operation `A = α*x*x**T + A`.
+> Perform the symmetric rank 1 operation `A = α*x*x^T + A`.
 
 <section class="usage">
 
@@ -32,16 +32,16 @@ var ssyr = require( '@stdlib/blas/base/ssyr' );
 
 #### ssyr( order, uplo, N, α, x, sx, A, LDA )
 
-Performs the symmetric rank 1 operation `A = α*x*x**T + A` where `α` is a scalar, `x` is an `N` element vector, and `A` is an `N` by `N` symmetric matrix.
+Performs the symmetric rank 1 operation `A = α*x*x^T + A` where `α` is a scalar, `x` is an `N` element vector, and `A` is an `N` by `N` symmetric matrix.
 
 ```javascript
 var Float32Array = require( '@stdlib/array/float32' );
 
-var A = new Float32Array( [ 1.0, 2.0, 3.0, 0.0, 1.0, 2.0, 0.0, 0.0, 1.0 ] );
+var A = new Float32Array( [ 1.0, 2.0, 3.0, 2.0, 1.0, 2.0, 3.0, 2.0, 1.0 ] );
 var x = new Float32Array( [ 1.0, 2.0, 3.0 ] );
 
 ssyr( 'row-major', 'upper', 3, 1.0, x, 1, A, 3 );
-// A => <Float32Array>[ 2.0, 4.0, 6.0, 0.0, 5.0, 8.0, 0.0, 0.0, 10.0 ]
+// A => <Float32Array>[ 2.0, 4.0, 6.0, 2.0, 5.0, 8.0, 3.0, 2.0, 10.0 ]
 ```
 
 The function has the following parameters:
@@ -51,20 +51,20 @@ The function has the following parameters:
 -   **N**: number of elements along each dimension of `A`.
 -   **α**: scalar constant.
 -   **x**: input [`Float32Array`][mdn-float32array].
--   **sx**: index increment for `x`.
+-   **sx**: stride length for `x`.
 -   **A**: input matrix stored in linear memory as a [`Float32Array`][mdn-float32array].
--   **lda**: stride of the first dimension of `A` (a.k.a., leading dimension of the matrix `A`).
+-   **LDA**: stride of the first dimension of `A` (a.k.a., leading dimension of the matrix `A`).
 
-The stride parameters determine how elements in the input arrays are accessed at runtime. For example, to iterate over every other element of `x` in reverse order,
+The stride parameters determine how elements in the input arrays are accessed at runtime. For example, to iterate over the elements of `x` in reverse order,
 
 ```javascript
 var Float32Array = require( '@stdlib/array/float32' );
 
-var A = new Float32Array( [ 1.0, 2.0, 3.0, 0.0, 1.0, 2.0, 0.0, 0.0, 1.0 ] );
-var x = new Float32Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
+var A = new Float32Array( [ 1.0, 2.0, 3.0, 2.0, 1.0, 2.0, 3.0, 2.0, 1.0 ] );
+var x = new Float32Array( [ 3.0, 2.0, 1.0 ] );
 
-ssyr( 'row-major', 'upper', 3, 1.0, x, -2, A, 3 );
-// A => <Float32Array>[ 26.0, 17.0, 8.0, 0.0, 10.0, 5.0, 0.0, 0.0, 2.0 ]
+ssyr( 'row-major', 'upper', 3, 1.0, x, -1, A, 3 );
+// A => <Float32Array>[ 2.0, 4.0, 6.0, 2.0, 5.0, 8.0, 3.0, 2.0, 10.0 ]
 ```
 
 Note that indexing is relative to the first index. To introduce an offset, use [`typed array`][mdn-typed-array] views.
@@ -75,14 +75,14 @@ Note that indexing is relative to the first index. To introduce an offset, use [
 var Float32Array = require( '@stdlib/array/float32' );
 
 // Initial arrays...
-var x0 = new Float32Array( [ 1.0, 1.0, 1.0, 1.0 ] );
-var A = new Float32Array( [ 1.0, 2.0, 3.0, 0.0, 1.0, 2.0, 0.0, 0.0, 1.0 ] );
+var x0 = new Float32Array( [ 0.0, 3.0, 2.0, 1.0 ] );
+var A = new Float32Array( [ 1.0, 2.0, 3.0, 2.0, 1.0, 2.0, 3.0, 2.0, 1.0 ] );
 
 // Create offset views...
 var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
 
 ssyr( 'row-major', 'upper', 3, 1.0, x1, -1, A, 3 );
-// A => <Float32Array>[ 2.0, 3.0, 4.0, 0.0, 2.0, 3.0, 0.0, 0.0, 2.0 ]
+// A => <Float32Array>[ 2.0, 4.0, 6.0, 2.0, 5.0, 8.0, 3.0, 2.0, 10.0 ]
 ```
 
 #### ssyr.ndarray( uplo, N, α, x, sx, ox, A, sa1, sa2, oa )
@@ -92,11 +92,11 @@ Performs the symmetric rank 1 operation `A = α*x*x^T + A`, using alternative in
 ```javascript
 var Float32Array = require( '@stdlib/array/float32' );
 
-var A = new Float32Array( [ 1.0, 2.0, 3.0, 0.0, 1.0, 2.0, 0.0, 0.0, 1.0 ] );
+var A = new Float32Array( [ 1.0, 2.0, 3.0, 2.0, 1.0, 2.0, 3.0, 2.0, 1.0 ] );
 var x = new Float32Array( [ 1.0, 2.0, 3.0 ] );
 
 ssyr.ndarray( 'upper', 3, 1.0, x, 1, 0, A, 3, 1, 0 );
-// A => <Float32Array>[ 2.0, 4.0, 6.0, 0.0, 5.0, 8.0, 0.0, 0.0, 10.0 ]
+// A => <Float32Array>[ 2.0, 4.0, 6.0, 2.0, 5.0, 8.0, 3.0, 2.0, 10.0 ]
 ```
 
 The function has the following additional parameters:
@@ -111,11 +111,11 @@ While [`typed array`][mdn-typed-array] views mandate a view offset based on the
 ```javascript
 var Float32Array = require( '@stdlib/array/float32' );
 
-var A = new Float32Array( [ 1.0, 2.0, 3.0, 0.0, 1.0, 2.0, 0.0, 0.0, 1.0 ] );
+var A = new Float32Array( [ 1.0, 2.0, 3.0, 2.0, 1.0, 2.0, 3.0, 2.0, 1.0 ] );
 var x = new Float32Array( [ 1.0, 2.0, 3.0, 4.0, 5.0 ] );
 
 ssyr.ndarray( 'upper', 3, 1.0, x, -2, 4, A, 3, 1, 0 );
-// A => <Float32Array>[ 26.0, 17.0, 8.0, 0.0, 10.0, 5.0, 0.0, 0.0, 2.0 ]
+// A => <Float32Array>[ 26.0, 17.0, 8.0, 2.0, 10.0, 5.0, 3.0, 2.0, 2.0 ]
 ```
 
 </section>
@@ -149,11 +149,18 @@ var opts = {
 
 var N = 3;
 
-var A = ones( N*N, opts.dtype );
+// Create N-by-N symmetric matrices:
+var A1 = ones( N*N, opts.dtype );
+var A2 = ones( N*N, opts.dtype );
+
+// Create a random vector:
 var x = discreteUniform( N, -10.0, 10.0, opts );
 
-ssyr( 'row-major', 'upper', 3, 1.0, x, 1, A, 3 );
-console.log( A );
+ssyr( 'row-major', 'upper', 3, 1.0, x, 1, A1, 3 );
+console.log( A1 );
+
+ssyr.ndarray( 'upper', 3, 1.0, x, 1, 0, A2, 3, 1, 0 );
+console.log( A2 );
 ```
 
 </section>
@@ -186,62 +193,62 @@ console.log( A );
 #include "stdlib/blas/base/ssyr.h"
 ```
 
-#### c_ssyr( order, uplo, N, alpha, \*X, strideX, \*A, LDA )
+#### c_ssyr( layout, uplo, N, alpha, \*X, sx, \*A, LDA )
 
-Performs the symmetric rank 1 operation `A = α*x*x^T + A`.
+Performs the symmetric rank 1 operation `A = α*x*x^T + A` where `α` is a scalar, `x` is an `N` element vector, and `A` is an `N` by `N` symmetric matrix.
 
 ```c
 #include "stdlib/blas/base/shared.h"
 
-float A[] = { 1.0f, 0.0f, 0.0f, 2.0f, 1.0f, 0.0f, 3.0f, 2.0f, 1.0f };
-const float x[] = { 1.0f, 2.0f, 3.0f };
+float A[] = { 1.0, 2.0, 3.0, 2.0, 1.0, 2.0, 3.0, 2.0, 1.0 };
+const float x[] = { 1.0, 2.0, 3.0 };
 
-c_ssyr( CblasColMajor, CblasUpper, 3, 1.0f, x, 1, A, 3 );
+c_ssyr( CblasColMajor, CblasUpper, 3, 1.0, x, 1, A, 3 );
 ```
 
 The function accepts the following arguments:
 
--   **order**: `[in] CBLAS_LAYOUT` storage layout.
+-   **layout**: `[in] CBLAS_LAYOUT` storage layout.
 -   **uplo**: `[in] CBLAS_UPLO` specifies whether the upper or lower triangular part of the symmetric matrix `A` should be referenced.
 -   **N**: `[in] CBLAS_INT` number of elements along each dimension of `A`.
--   **alpha**: `[in] float` scalar.
+-   **alpha**: `[in] float` scalar constant.
 -   **X**: `[in] float*` input array.
--   **strideX**: `[in] CBLAS_INT` index increment for `X`.
+-   **sx**: `[in] CBLAS_INT` stride length for `X`.
 -   **A**: `[inout] float*` input matrix.
 -   **LDA**: `[in] CBLAS_INT` stride of the first dimension of `A` (a.k.a., leading dimension of the matrix `A`).
 
 ```c
-void c_ssyr( const CBLAS_LAYOUT order, const CBLAS_UPLO uplo, const CBLAS_INT N, const float alpha, const float *X, const CBLAS_INT strideX, float *A, const CBLAS_INT LDA )
+void c_ssyr( const CBLAS_LAYOUT layout, const CBLAS_UPLO uplo, const CBLAS_INT N, const float alpha, const float *X, const CBLAS_INT strideX, float *A, const CBLAS_INT LDA )
 ```
 
-#### c_ssyr_ndarray( uplo, N, alpha, \*X, strideX, offsetX, \*A, sa1, sa2, oa )
+#### c_ssyr_ndarray( uplo, N, alpha, \*X, sx, ox, \*A, sa1, sa2, oa )
 
-Performs the symmetric rank 1 operation `A = α*x*x^T + A` using alternative indexing semantics.
+Performs the symmetric rank 1 operation `A = α*x*x^T + A`, using alternative indexing semantics and where `α` is a scalar, `x` is an `N` element vector, and `A` is an `N` by `N` symmetric matrix.
 
 ```c
 #include "stdlib/blas/base/shared.h"
 
-float A[] = { 1.0f, 2.0f, 3.0f, 0.0f, 1.0f, 2.0f, 0.0f, 0.0f, 1.0f };
-const float x[] = { 1.0f, 2.0f, 3.0f };
+float A[] = { 1.0, 2.0, 3.0, 2.0, 1.0, 2.0, 3.0, 2.0, 1.0 };
+const float x[] = { 1.0, 2.0, 3.0 };
 
-c_ssyr_ndarray( CblasUpper, 3, 1.0f, x, 1, 0, A, 3, 1, 0 );
+c_ssyr_ndarray( CblasUpper, 3, 1.0, x, 1, 0, A, 3, 1, 0 );
 ```
 
 The function accepts the following arguments:
 
 -   **uplo**: `[in] CBLAS_UPLO` specifies whether the upper or lower triangular part of the symmetric matrix `A` should be referenced.
 -   **N**: `[in] CBLAS_INT` number of elements along each dimension of `A`.
--   **alpha**: `[in] float` scalar.
+-   **alpha**: `[in] float` scalar constant.
 -   **X**: `[in] float*` input array.
--   **strideX**: `[in] CBLAS_INT` index increment for `X`.
--   **offsetX**: `[in] CBLAS_INT` starting index for `X`.
+-   **sx**: `[in] CBLAS_INT` stride length for `X`.
+-   **ox**: `[in] CBLAS_INT` starting index for `X`.
 -   **A**: `[inout] float*` input matrix.
 -   **sa1**: `[in] CBLAS_INT` stride of the first dimension of `A`.
 -   **sa2**: `[in] CBLAS_INT` stride of the second dimension of `A`.
 -   **oa**: `[in] CBLAS_INT` starting index for `A`.
 
 ```c
-void c_ssyr_ndarray( const CBLAS_UPLO uplo, const CBLAS_INT N, const float alpha, const float *X, const CBLAS_INT strideX, const CBLAS_INT offsetX, float *A, const CBLAS_INT sa1, const CBLAS_INT sa2, const CBLAS_INT oa )
+void c_ssyr_ndarray( const CBLAS_UPLO uplo, const CBLAS_INT N, const float alpha, const float *X, const CBLAS_INT strideX, const CBLAS_INT offsetX, float *A, const CBLAS_INT strideA1, const CBLAS_INT strideA2, const CBLAS_INT offsetA )
 ```
 
 </section>
@@ -268,27 +275,39 @@ void c_ssyr_ndarray( const CBLAS_UPLO uplo, const CBLAS_INT N, const float alpha
 #include <stdio.h>
 
 int main( void ) {
-    // Create strided arrays:
-    float A[] = { 1.0f, 0.0f, 0.0f, 2.0f, 1.0f, 0.0f, 3.0f, 2.0f, 1.0f };
-    const float x[] = { 1.0f, 2.0f, 3.0f };
-
-    // Specify the number of elements along each dimension of `A`:
+    // Define 3x3 symmetric matrices stored in row-major layout:
+    float A1[ 3*3 ] = {
+        1.0f, 2.0f, 3.0f,
+        2.0f, 1.0f, 2.0f,
+        3.0f, 2.0f, 1.0f
+    };
+
+    float A2[ 3*3 ] = {
+        1.0f, 2.0f, 3.0f,
+        2.0f, 1.0f, 2.0f,
+        3.0f, 2.0f, 1.0f
+    };
+
+    // Define a vector:
+    const float x[ 3 ] = { 1.0f, 2.0f, 3.0f };
+
+    // Specify the number of elements along each dimension of `A1` and `A2`:
     const int N = 3;
 
     // Perform the symmetric rank 1 operation `A = α*x*x^T + A`:
-    c_ssyr( CblasColMajor, CblasUpper, N, 1.0f, x, 1, A, N );
+    c_ssyr( CblasColMajor, CblasUpper, N, 1.0f, x, 1, A1, N );
 
     // Print the result:
     for ( int i = 0; i < N*N; i++ ) {
-        printf( "A[ %i ] = %f\n", i, A[ i ] );
+        printf( "A1[ %i ] = %f\n", i, A1[ i ] );
     }
 
-    // Perform the symmetric rank 1 operation `A = α*x*x^T + A`:
-    c_ssyr_ndarray( CblasUpper, N, 1.0f, x, 1, 0, A, N, 1, 0 );
+    // Perform the symmetric rank 1 operation `A = α*x*x^T + A` using alternative indexing semantics:
+    c_ssyr_ndarray( CblasUpper, N, 1.0, x, 1, 0, A2, N, 1, 0 );
 
     // Print the result:
     for ( int i = 0; i < N*N; i++ ) {
-        printf( "A[ %i ] = %f\n", i, A[ i ] );
+        printf( "A2[ %i ] = %f\n", i, A[ i ] );
     }
 }
 ```
@@ -315,7 +334,7 @@ int main( void ) {
 
 [blas]: http://www.netlib.org/blas
 
-[blas-ssyr]: https://www.netlib.org/lapack/explore-html/dc/d82/group__her_gad7585662770cdd3001ed08c7a864cd21.html#gad7585662770cdd3001ed08c7a864cd21
+[blas-ssyr]: https://www.netlib.org/lapack/explore-html-3.6.1/d6/d30/group__single__blas__level2_ga7b8a99048765ed2bf7c1e770bff0b622.html
 
 [mdn-float32array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Float32Array
 

lib/node_modules/@stdlib/blas/base/ssyr/benchmark/c/benchmark.c

@@ -83,7 +83,7 @@ static double tic( void ) {
 * Runs a benchmark.
 *
 * @param iterations   number of iterations
-* @param N            number of elements along each dimension
+* @param N            array dimension size
 * @return             elapsed time in seconds
 */
 static double benchmark1( int iterations, int N ) {
@@ -93,18 +93,18 @@ static double benchmark1( int iterations, int N ) {
 	double t;
 	int i;
 
-	stdlib_strided_sfill( N, 0.5f, x, 1 );
+	stdlib_strided_sfill( N, 1.0f, x, 1 );
 	stdlib_strided_sfill( N*N, 1.0f, A, 1 );
 	t = tic();
 	for ( i = 0; i < iterations; i++ ) {
-		c_ssyr( CblasRowMajor, CblasUpper, N, 1.0, x, 1, A, N );
-		if ( A[ 0 ] != A[ 0 ] ) {
+		c_ssyr( CblasRowMajor, CblasUpper, N, 1.0f, x, 1, A, N );
+		if ( A[ i%(N*2) ] != A[ i%(N*2) ] ) {
 			printf( "should not return NaN\n" );
 			break;
 		}
 	}
 	elapsed = tic() - t;
-	if ( A[ 0 ] != A[ 0 ] ) {
+	if ( A[ i%(N*2) ] != A[ i%(N*2) ] ) {
 		printf( "should not return NaN\n" );
 	}
 	return elapsed;
@@ -114,7 +114,7 @@ static double benchmark1( int iterations, int N ) {
 * Runs a benchmark.
 *
 * @param iterations   number of iterations
-* @param N            number of elements along each dimension
+* @param N            array dimension size
 * @return             elapsed time in seconds
 */
 static double benchmark2( int iterations, int N ) {
@@ -124,18 +124,18 @@ static double benchmark2( int iterations, int N ) {
 	double t;
 	int i;
 
-	stdlib_strided_sfill( N, 0.5f, x, 1 );
+	stdlib_strided_sfill( N, 1.0f, x, 1 );
 	stdlib_strided_sfill( N*N, 1.0f, A, 1 );
 	t = tic();
 	for ( i = 0; i < iterations; i++ ) {
-		c_ssyr_ndarray( CblasUpper, N, 1.0, x, 1, 0, A, N, 1, 0 );
-		if ( A[ 0 ] != A[ 0 ] ) {
+		c_ssyr_ndarray( CblasUpper, N, 1.0f, x, 1, 0, A, N, 1, 0 );
+		if ( A[ i%(N*2) ] != A[ i%(N*2) ] ) {
 			printf( "should not return NaN\n" );
 			break;
 		}
 	}
 	elapsed = tic() - t;
-	if ( A[ 0 ] != A[ 0 ] ) {
+	if ( A[ i%(N*2) ] != A[ i%(N*2) ] ) {
 		printf( "should not return NaN\n" );
 	}
 	return elapsed;

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

@@ -46,10 +46,24 @@
 
     Examples
     --------
+    // Standard usage:
     > var x = new {{alias:@stdlib/array/float32}}( [ 1.0, 1.0 ] );
-    > var A = new {{alias:@stdlib/array/float32}}( [ 1.0, 2.0, 0.0, 2.0 ] );
+    > var A = new {{alias:@stdlib/array/float32}}( [ 1.0, 2.0, 2.0, 1.0 ] );
     > {{alias}}( 'row-major', 'upper', 2, 1.0, x, 1, A, 2 )
-    <Float32Array>[ 2.0, 3.0, 0.0, 3.0 ]
+    <Float32Array>[ 2.0, 3.0, 2.0, 2.0 ]
+
+    // Advanced indexing:
+    > var x = new {{alias:@stdlib/array/float32}}( [ 1.0, 1.0 ] );
+    > var A = new {{alias:@stdlib/array/float32}}( [ 1.0, 2.0, 2.0, 1.0 ] );
+    > {{alias}}( 'row-major', 'upper', 2, 1.0, x, -1, A, 2 )
+    <Float32Array>[ 2.0, 3.0, 2.0, 2.0 ]
+
+    // Using typed array views:
+    > var x0 = new {{alias:@stdlib/array/float32}}( [ 0.0, 1.0, 1.0 ] );
+    > A = new {{alias:@stdlib/array/float32}}( [ 1.0, 2.0, 2.0, 1.0 ] );
+    > var x1 = new {{alias:@stdlib/array/float32}}( x0.buffer, x0.BYTES_PER_ELEMENT*1 );
+    > {{alias}}( 'row-major', 'upper', 2, 1.0, x, 1, A, 2 )
+    <Float32Array>[ 2.0, 3.0, 2.0, 2.0 ]
 
 
 {{alias}}.ndarray( uplo, N, α, x, sx, ox, A, sa1, sa2, oa )
@@ -102,9 +116,9 @@
     Examples
     --------
     > var x = new {{alias:@stdlib/array/float32}}( [ 1.0, 1.0 ] );
-    > var A = new {{alias:@stdlib/array/float32}}( [ 1.0, 2.0, 0.0, 2.0 ] );
+    > var A = new {{alias:@stdlib/array/float32}}( [ 1.0, 2.0, 2.0, 1.0 ] );
     > {{alias}}.ndarray( 'upper', 2, 1.0, x, 1, 0, A, 2, 1, 0 )
-    <Float32Array>[ 2.0, 3.0, 0.0, 3.0 ]
+    <Float32Array>[ 2.0, 3.0, 2.0, 2.0 ]
 
     See Also
     --------