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xtensor/benchmark/benchmark_view_access.cpp at stable · xtensor-stack/xtensor · GitHub
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/*
**************************************************************************
* Copyright (c) 2016, Johan Mabille, Sylvain Corlay and Wolf Vollprecht *
* *
* Distributed under the terms of the BSD 3-Clause License. *
* *
* The full license is in the file LICENSE, distributed with this software. *
***************************************************************************
*/
#
include
<
benchmark/benchmark.h
>
//
#include "xtensor/xshape.hpp"
#
include
"
xtensor/xadapt.hpp
"
#
include
"
xtensor/xnoalias.hpp
"
#
include
"
xtensor/xrandom.hpp
"
#
include
"
xtensor/xbuilder.hpp
"
#
include
"
xtensor/xstorage.hpp
"
#
include
"
xtensor/xutils.hpp
"
#
include
"
xtensor/xview.hpp
"
namespace
xt
{
template
<
class
T
, std::
size_t
N>
class
simple_array
{
public:
using
self_type = simple_array<T, N>;
using
shape_type = std::array<
ptrdiff_t
, N>;
simple_array
() =
default
;
explicit
simple_array
(
const
std::array<
ptrdiff_t
, N>& shape)
: m_shape(shape)
{
ptrdiff_t
data_size =
1
;
m_strides[N -
1
] =
1
;
for
(std::
ptrdiff_t
i = N -
1
; i >
0
; --i)
{
data_size *=
static_cast
<
ptrdiff_t
>(shape[i]);
m_strides[i -
1
] = data_size;
}
data_size *= shape[
0
];
memory.
resize
(data_size);
}
template
<
class
E
>
self_type&
operator
=(
const
xexpression<E>& e)
{
const
E& de = e.
derived_cast
();
std::copy
(de.
cbegin
(), de.
cend
(), memory.
begin
());
return
*
this
;
}
void
fill
(T val)
{
std::fill
(memory.
begin
(), memory.
end
(), val);
}
template
<
class
... Args>
T&
operator
()(Args... args)
{
std::array<
ptrdiff_t
,
sizeof
...(Args)>
idx
({
static_cast
<
long
>(args)...});
static_assert
(
sizeof
...(Args) == N,
"
too few or too many indices!
"
);
ptrdiff_t
offset =
0
;
for
(std::
size_t
i =
0
; i < N; ++i)
{
offset += m_strides[i] * idx[i];
}
return
memory[offset];
}
xt::uvector<T> memory;
std::array<
ptrdiff_t
, N> m_shape, m_strides;
};
void
xview_access_calc
(benchmark::State &state) {
xt::xtensor<
double
,
4
> A = xt::random::rand<
double
>({
100
,
100
,
4
,
4
});
xt::xtensor<
double
,
3
> elemvec = xt::random::rand<
double
>({
100
,
4
,
4
});
xt::xtensor<
double
,
2
> eps = xt::empty<
double
>({
2
,
2
});
for
(
auto
_ : state)
{
for
(
size_t
e =
0
; e <
100
; ++e)
{
//
alias element vector (e.g. nodal displacements)
auto
u =
xt::view
(elemvec, e,
xt::all
(),
xt::all
());
for
(
size_t
k =
0
; k <
100
; ++k)
{
auto
dNx =
xt::view
(A, e, k,
xt::all
(),
xt::all
());
//
- evaluate symmetrized dyadic product (loops unrolled for efficiency)
//
grad(i,j) += dNx(m,i) * u(m,j)
//
eps (j,i) = 0.5 * ( grad(i,j) + grad(j,i) )
eps
(
0
,
0
) =
dNx
(
0
,
0
) *
u
(
0
,
0
) +
dNx
(
1
,
0
) *
u
(
1
,
0
) +
dNx
(
2
,
0
) *
u
(
2
,
0
) +
dNx
(
3
,
0
) *
u
(
3
,
0
);
eps
(
1
,
1
) =
dNx
(
0
,
1
) *
u
(
0
,
1
) +
dNx
(
1
,
1
) *
u
(
1
,
1
) +
dNx
(
2
,
1
) *
u
(
2
,
1
) +
dNx
(
3
,
1
) *
u
(
3
,
1
);
eps
(
0
,
1
) =
(
dNx
(
0
,
1
) *
u
(
0
,
0
) +
dNx
(
1
,
1
) *
u
(
1
,
0
) +
dNx
(
2
,
1
) *
u
(
2
,
0
) +
dNx
(
3
,
1
) *
u
(
3
,
0
) +
dNx
(
0
,
0
) *
u
(
0
,
1
) +
dNx
(
1
,
0
) *
u
(
1
,
1
) +
dNx
(
2
,
0
) *
u
(
2
,
1
) +
dNx
(
3
,
0
) *
u
(
3
,
1
)) /
2
.;
eps
(
1
,
0
) =
eps
(
0
,
1
);
benchmark::DoNotOptimize
(eps.
storage
());
}
}
}
}
void
raw_access_calc
(benchmark::State &state) {
xt::xtensor<
double
,
4
> A = xt::random::rand<
double
>({
100
,
100
,
4
,
4
});
xt::xtensor<
double
,
3
> elemvec = xt::random::rand<
double
>({
100
,
4
,
4
});
xt::xtensor<
double
,
2
> eps = xt::empty<
double
>({
2
,
2
});
for
(
auto
_ : state)
{
for
(
size_t
e =
0
; e <
100
; ++e)
{
for
(
size_t
k =
0
; k <
100
; ++k)
{
//
- evaluate symmetrized dyadic product (loops unrolled for efficiency)
//
grad(i,j) += dNx(m,i) * u(m,j)
//
eps (j,i) = 0.5 * ( grad(i,j) + grad(j,i) )
eps
(
0
,
0
) =
A
(e, k,
0
,
0
) *
elemvec
(e,
0
,
0
) +
A
(e, k,
1
,
0
) *
elemvec
(e,
1
,
0
) +
A
(e, k,
2
,
0
) *
elemvec
(e,
2
,
0
) +
A
(e, k,
3
,
0
) *
elemvec
(e,
3
,
0
);
eps
(
1
,
1
) =
A
(e, k,
0
,
1
) *
elemvec
(e,
0
,
1
) +
A
(e, k,
1
,
1
) *
elemvec
(e,
1
,
1
) +
A
(e, k,
2
,
1
) *
elemvec
(e,
2
,
1
) +
A
(e, k,
3
,
1
) *
elemvec
(e,
3
,
1
);
eps
(
0
,
1
) = (
A
(e, k,
0
,
1
) *
elemvec
(e,
0
,
0
) +
A
(e, k,
1
,
1
) *
elemvec
(e,
1
,
0
) +
A
(e, k,
2
,
1
) *
elemvec
(e,
2
,
0
) +
A
(e, k,
3
,
1
) *
elemvec
(e,
3
,
0
) +
A
(e, k,
0
,
0
) *
elemvec
(e,
0
,
1
) +
A
(e, k,
1
,
0
) *
elemvec
(e,
1
,
1
) +
A
(e, k,
2
,
0
) *
elemvec
(e,
2
,
1
) +
A
(e, k,
3
,
0
) *
elemvec
(e,
3
,
1
)) /
2
.;
eps
(
1
,
0
) =
eps
(
0
,
1
);
benchmark::DoNotOptimize
(eps.
storage
());
}
}
}
}
void
unchecked_access_calc
(benchmark::State &state) {
xt::xtensor<
double
,
4
> A = xt::random::rand<
double
>({
100
,
100
,
4
,
4
});
xt::xtensor<
double
,
3
> elemvec = xt::random::rand<
double
>({
100
,
4
,
4
});
xt::xtensor<
double
,
2
> eps = xt::empty<
double
>({
2
,
2
});
for
(
auto
_ : state)
{
for
(
size_t
e =
0
; e <
100
; ++e)
{
for
(
size_t
k =
0
; k <
100
; ++k)
{
//
- evaluate symmetrized dyadic product (loops unrolled for efficiency)
//
grad(i,j) += dNx(m,i) * u(m,j)
//
eps (j,i) = 0.5 * ( grad(i,j) + grad(j,i) )
eps.
unchecked
(
0
,
0
) =
A.
unchecked
(e, k,
0
,
0
) * elemvec.
unchecked
(e,
0
,
0
) +
A.
unchecked
(e, k,
1
,
0
) * elemvec.
unchecked
(e,
1
,
0
) +
A.
unchecked
(e, k,
2
,
0
) * elemvec.
unchecked
(e,
2
,
0
) +
A.
unchecked
(e, k,
3
,
0
) * elemvec.
unchecked
(e,
3
,
0
);
eps.
unchecked
(
1
,
1
) =
A.
unchecked
(e, k,
0
,
1
) * elemvec.
unchecked
(e,
0
,
1
) +
A.
unchecked
(e, k,
1
,
1
) * elemvec.
unchecked
(e,
1
,
1
) +
A.
unchecked
(e, k,
2
,
1
) * elemvec.
unchecked
(e,
2
,
1
) +
A.
unchecked
(e, k,
3
,
1
) * elemvec.
unchecked
(e,
3
,
1
);
eps.
unchecked
(
0
,
1
) =
(A.
unchecked
(e, k,
0
,
1
) * elemvec.
unchecked
(e,
0
,
0
) +
A.
unchecked
(e, k,
1
,
1
) * elemvec.
unchecked
(e,
1
,
0
) +
A.
unchecked
(e, k,
2
,
1
) * elemvec.
unchecked
(e,
2
,
0
) +
A.
unchecked
(e, k,
3
,
1
) * elemvec.
unchecked
(e,
3
,
0
) +
A.
unchecked
(e, k,
0
,
0
) * elemvec.
unchecked
(e,
0
,
1
) +
A.
unchecked
(e, k,
1
,
0
) * elemvec.
unchecked
(e,
1
,
1
) +
A.
unchecked
(e, k,
2
,
0
) * elemvec.
unchecked
(e,
2
,
1
) +
A.
unchecked
(e, k,
3
,
0
) * elemvec.
unchecked
(e,
3
,
1
)) /
2
.;
eps.
unchecked
(
1
,
0
) = eps.
unchecked
(
0
,
1
);
benchmark::DoNotOptimize
(eps.
storage
());
}
}
}
}
void
simplearray_access_calc
(benchmark::State &state) {
simple_array<
double
,
4
>
A
(std::array<
ptrdiff_t
,
4
>{
100
,
100
,
4
,
2
});
simple_array<
double
,
3
>
elemvec
(std::array<
ptrdiff_t
,
3
>{
100
,
4
,
2
});
simple_array<
double
,
2
>
eps
(std::array<
ptrdiff_t
,
2
>{
2
,
2
});
for
(
auto
_ : state)
{
for
(
size_t
e =
0
; e <
100
; ++e)
{
for
(
size_t
k =
0
; k <
100
; ++k)
{
//
- evaluate sy mmetrized dyadic product (loops unrolled for efficiency)
//
grad(i,j) += dNx(m,i) * u(m,j)
//
eps (j,i) = 0.5 * ( grad(i,j) + grad(j,i) )
eps
(
0
,
0
) =
A
(e, k,
0
,
0
) *
elemvec
(e,
0
,
0
) +
A
(e, k,
1
,
0
) *
elemvec
(e,
1
,
0
) +
A
(e, k,
2
,
0
) *
elemvec
(e,
2
,
0
) +
A
(e, k,
3
,
0
) *
elemvec
(e,
3
,
0
);
eps
(
1
,
1
) =
A
(e, k,
0
,
1
) *
elemvec
(e,
0
,
1
) +
A
(e, k,
1
,
1
) *
elemvec
(e,
1
,
1
) +
A
(e, k,
2
,
1
) *
elemvec
(e,
2
,
1
) +
A
(e, k,
3
,
1
) *
elemvec
(e,
3
,
1
);
eps
(
0
,
1
) = (
A
(e, k,
0
,
1
) *
elemvec
(e,
0
,
0
) +
A
(e, k,
1
,
1
) *
elemvec
(e,
1
,
0
) +
A
(e, k,
2
,
1
) *
elemvec
(e,
2
,
0
) +
A
(e, k,
3
,
1
) *
elemvec
(e,
3
,
0
) +
A
(e, k,
0
,
0
) *
elemvec
(e,
0
,
1
) +
A
(e, k,
1
,
0
) *
elemvec
(e,
1
,
1
) +
A
(e, k,
2
,
0
) *
elemvec
(e,
2
,
1
) +
A
(e, k,
3
,
0
) *
elemvec
(e,
3
,
1
)) /
2
.;
eps
(
1
,
0
) =
eps
(
0
,
1
);
benchmark::DoNotOptimize
(eps.
memory
);
}
}
}
}
#
define
M_NELEM
3600
#
define
M_NNE
4
#
define
M_NDIM
2
template
<
class
X
, layout_type L>
class
jumping_random
{
public:
using
shape_type =
typename
X::shape_type;
jumping_random
()
: m_dofs(shape_type{
3721
,
2
}),
m_conn
(shape_type{
3600
,
4
})
{
m_dofs =
xt::clip
(
xt::reshape_view
(
xt::arange
(
2
*
3721
), {
3721
,
2
}),
0
,
7199
);
for
(std::
size_t
i =
0
; i <
3600
; ++i)
{
m_conn
(i,
0
) = i;
m_conn
(i,
1
) = i +
1
;
m_conn
(i,
2
) = i +
62
;
m_conn
(i,
3
) = i +
61
;
}
}
auto
calc_dofval
(xt::xtensor<
double
,
3
, L>& elemvec, xt::xtensor<
double
,
1
, L>& dofval)
{
dofval.
fill
(
0.0
);
for
(
size_t
e =
0
; e <
M_NELEM
; ++e)
for
(
size_t
m =
0
; m <
M_NNE
; ++m)
for
(
size_t
i =
0
; i <
M_NDIM
; ++i)
dofval
(
m_dofs
(
m_conn
(e, m), i)) +=
elemvec
(e, m, i);
}
auto
calc_dofval_simple
(simple_array<
double
,
3
>& elemvec, simple_array<
double
,
1
>& dofval)
{
dofval.
fill
(
0.0
);
for
(
size_t
e =
0
; e <
M_NELEM
; ++e)
for
(
size_t
m =
0
; m <
M_NNE
; ++m)
for
(
size_t
i =
0
; i <
M_NDIM
; ++i)
dofval
(
m_dofs
(
m_conn
(e, m), i)) +=
elemvec
(e, m, i);
}
auto
calc_unchecked_dofval
(xt::xtensor<
double
,
3
, L>& elemvec, xt::xtensor<
double
,
1
, L>& dofval)
{
dofval.
fill
(
0.0
);
for
(
size_t
e =
0
; e <
M_NELEM
; ++e)
for
(
size_t
m =
0
; m <
M_NNE
; ++m)
for
(
size_t
i =
0
; i <
M_NDIM
; ++i)
dofval.
unchecked
(m_dofs.
unchecked
(m_conn.
unchecked
(e, m), i)) += elemvec.
unchecked
(e, m, i);
}
X m_dofs, m_conn;
};
template
<layout_type L>
void
jumping_access
(benchmark::State& state)
{
auto
rx = jumping_random<xt::xtensor<std::
size_t
,
2
, L>, L>();
xt::xtensor<
double
,
3
, L> elemvec = xt::random::rand<
double
>({
M_NELEM
,
M_NNE
,
M_NDIM
});
xt::xtensor<
double
,
1
, L> dofval = xt::empty<
double
>({
7200
});
for
(
auto
_ : state)
{
rx.
calc_dofval
(elemvec, dofval);
benchmark::DoNotOptimize
(dofval.
data
());
}
}
template
<layout_type L>
void
jumping_access_unchecked
(benchmark::State& state)
{
auto
rx = jumping_random<xt::xtensor<std::
size_t
,
2
, L>, L>();
xt::xtensor<
double
,
3
, L> elemvec = xt::random::rand<
double
>({
M_NELEM
,
M_NNE
,
M_NDIM
});
xt::xtensor<
double
,
1
, L> dofval = xt::empty<
double
>({
7200
});
for
(
auto
_ : state)
{
rx.
calc_unchecked_dofval
(elemvec, dofval);
benchmark::DoNotOptimize
(dofval.
data
());
}
}
void
jumping_access_simplearray
(benchmark::State& state)
{
auto
rx = jumping_random<simple_array<std::
size_t
,
2
>, layout_type::row_major>();
simple_array<
double
,
3
>
elemvec
({
M_NELEM
,
M_NNE
,
M_NDIM
});
elemvec = xt::random::rand<
double
>({
M_NELEM
,
M_NNE
,
M_NDIM
});
simple_array<
double
,
1
>
dofval
({
7200
});
for
(
auto
_ : state)
{
rx.
calc_dofval_simple
(elemvec, dofval);
benchmark::DoNotOptimize
(dofval.
memory
);
}
}
BENCHMARK
(raw_access_calc);
BENCHMARK
(unchecked_access_calc);
BENCHMARK
(simplearray_access_calc);
BENCHMARK
(xview_access_calc);
BENCHMARK_TEMPLATE
(jumping_access, layout_type::row_major);
BENCHMARK_TEMPLATE
(jumping_access, layout_type::column_major);
BENCHMARK_TEMPLATE
(jumping_access_unchecked, layout_type::row_major);
BENCHMARK_TEMPLATE
(jumping_access_unchecked, layout_type::column_major);
BENCHMARK
(jumping_access_simplearray);
}
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