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intrinsics |
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The stdlib_intrinsics module provides replacements for some of the well known intrinsic functions found in Fortran compilers for which either a faster and/or more accurate implementation is found which has also proven of interest to the Fortran community.
The stdlib_sum function can replace the intrinsic sum for real or complex arrays. It follows a chunked implementation which maximizes vectorization potential as well as reducing the round-off error. This procedure is recommended when summing large arrays, for repetitive summation of smaller arrays consider the classical sum.
res = [[stdlib_intrinsics(module):stdlib_sum(interface)]] (x [,mask] )
res = [[stdlib_intrinsics(module):stdlib_sum(interface)]] (x, dim [,mask] )
Experimental
Pure function.
x: N-D array of either real, complex or integer type. This argument is intent(in).
dim (optional): scalar of type integer with a value in the range from 1 to n, where n equals the rank of x.
mask (optional): N-D array of logical values, with the same shape as x. This argument is intent(in).
If dim is absent, the output is a scalar of the same type and kind as to that of x. Otherwise, an array of rank n-1, where n equals the rank of x, and a shape similar to that of x with dimension dim dropped is returned.
The stdlib_sum_kahan function can replace the intrinsic sum for real, complex or integer arrays. It follows a chunked implementation which maximizes vectorization potential complemented by an elemental kernel based on the kahan summation strategy to reduce the round-off error:
elemental subroutine kahan_kernel_<kind>(a,s,c)
type(<kind>), intent(in) :: a
type(<kind>), intent(inout) :: s
type(<kind>), intent(inout) :: c
type(<kind>) :: t, y
y = a - c
t = s + y
c = (t - s) - y
s = t
end subroutineres = [[stdlib_intrinsics(module):stdlib_sum_kahan(interface)]] (x [,mask] )
res = [[stdlib_intrinsics(module):stdlib_sum_kahan(interface)]] (x, dim [,mask] )
Experimental
Pure function.
x: 1D array of either real, complex or integer type. This argument is intent(in).
dim (optional): scalar of type integer with a value in the range from 1 to n, where n equals the rank of x.
mask (optional): N-D array of logical values, with the same shape as x. This argument is intent(in).
If dim is absent, the output is a scalar of the same type and kind as to that of x. Otherwise, an array of rank n-1, where n equals the rank of x, and a shape similar to that of x with dimension dim dropped is returned.
{!example/intrinsics/example_sum.f90!}The stdlib_dot_product function can replace the intrinsic dot_product for 1D real or complex arrays. It follows a chunked implementation which maximizes vectorization potential as well as reducing the round-off error. This procedure is recommended when crunching large arrays, for repetitive products of smaller arrays consider the classical dot_product.
res = [[stdlib_intrinsics(module):stdlib_dot_product(interface)]] (x, y)
Experimental
Pure function.
x: 1D array of either real, complex or integer type. This argument is intent(in).
y: 1D array of the same type and kind as x. This argument is intent(in).
The output is a scalar of type and kind same as to that of x and y.
The stdlib_dot_product_kahan function can replace the intrinsic dot_product for 1D real or complex arrays. It follows a chunked implementation which maximizes vectorization potential , complemented by the same elemental kernel based on the kahan summation used for stdlib_sum to reduce the round-off error.
res = [[stdlib_intrinsics(module):stdlib_dot_product_kahan(interface)]] (x, y)
Experimental
Pure function.
x: 1D array of either real or complex type. This argument is intent(in).
y: 1D array of the same type and kind as x. This argument is intent(in).
The output is a scalar of type and kind same as to that of x and y.
{!example/intrinsics/example_dot_product.f90!}