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the Index types change.

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As discussed on the list (too long to explain here).
This commit is contained in:
Benoit Jacob
2010-05-30 16:00:58 -04:00
parent faa3ff3be6
commit aaaade4b3d
158 changed files with 3137 additions and 2878 deletions
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41
Eigen/src/Core/Redux.h
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@@ -176,15 +176,16 @@ template<typename Func, typename Derived>
struct ei_redux_impl<Func, Derived, DefaultTraversal, NoUnrolling>
{
typedef typename Derived::Scalar Scalar;
typedef typename Derived::Index Index;
static Scalar run(const Derived& mat, const Func& func)
{
ei_assert(mat.rows()>0 && mat.cols()>0 && "you are using a non initialized matrix");
Scalar res;
res = mat.coeffByOuterInner(0, 0);
for(int i = 1; i < mat.innerSize(); ++i)
for(Index i = 1; i < mat.innerSize(); ++i)
res = func(res, mat.coeffByOuterInner(0, i));
for(int i = 1; i < mat.outerSize(); ++i)
for(int j = 0; j < mat.innerSize(); ++j)
for(Index i = 1; i < mat.outerSize(); ++i)
for(Index j = 0; j < mat.innerSize(); ++j)
res = func(res, mat.coeffByOuterInner(i, j));
return res;
}
@@ -200,37 +201,38 @@ struct ei_redux_impl<Func, Derived, LinearVectorizedTraversal, NoUnrolling>
{
typedef typename Derived::Scalar Scalar;
typedef typename ei_packet_traits<Scalar>::type PacketScalar;
typedef typename Derived::Index Index;
static Scalar run(const Derived& mat, const Func& func)
{
const int size = mat.size();
const int packetSize = ei_packet_traits<Scalar>::size;
const int alignedStart = ei_first_aligned(mat);
const Index size = mat.size();
const Index packetSize = ei_packet_traits<Scalar>::size;
const Index alignedStart = ei_first_aligned(mat);
enum {
alignment = (Derived::Flags & DirectAccessBit) || (Derived::Flags & AlignedBit)
? Aligned : Unaligned
};
const int alignedSize = ((size-alignedStart)/packetSize)*packetSize;
const int alignedEnd = alignedStart + alignedSize;
const Index alignedSize = ((size-alignedStart)/packetSize)*packetSize;
const Index alignedEnd = alignedStart + alignedSize;
Scalar res;
if(alignedSize)
{
PacketScalar packet_res = mat.template packet<alignment>(alignedStart);
for(int index = alignedStart + packetSize; index < alignedEnd; index += packetSize)
for(Index index = alignedStart + packetSize; index < alignedEnd; index += packetSize)
packet_res = func.packetOp(packet_res, mat.template packet<alignment>(index));
res = func.predux(packet_res);
for(int index = 0; index < alignedStart; ++index)
for(Index index = 0; index < alignedStart; ++index)
res = func(res,mat.coeff(index));
for(int index = alignedEnd; index < size; ++index)
for(Index index = alignedEnd; index < size; ++index)
res = func(res,mat.coeff(index));
}
else // too small to vectorize anything.
// since this is dynamic-size hence inefficient anyway for such small sizes, don't try to optimize.
{
res = mat.coeff(0);
for(int index = 1; index < size; ++index)
for(Index index = 1; index < size; ++index)
res = func(res,mat.coeff(index));
}
@@ -243,26 +245,27 @@ struct ei_redux_impl<Func, Derived, SliceVectorizedTraversal, NoUnrolling>
{
typedef typename Derived::Scalar Scalar;
typedef typename ei_packet_traits<Scalar>::type PacketScalar;
typedef typename Derived::Index Index;
static Scalar run(const Derived& mat, const Func& func)
{
const int innerSize = mat.innerSize();
const int outerSize = mat.outerSize();
const Index innerSize = mat.innerSize();
const Index outerSize = mat.outerSize();
enum {
packetSize = ei_packet_traits<Scalar>::size
};
const int packetedInnerSize = ((innerSize)/packetSize)*packetSize;
const Index packetedInnerSize = ((innerSize)/packetSize)*packetSize;
Scalar res;
if(packetedInnerSize)
{
PacketScalar packet_res = mat.template packet<Unaligned>(0,0);
for(int j=0; j<outerSize; ++j)
for(int i=(j==0?packetSize:0); i<packetedInnerSize; i+=int(packetSize))
for(Index j=0; j<outerSize; ++j)
for(Index i=(j==0?packetSize:0); i<packetedInnerSize; i+=Index(packetSize))
packet_res = func.packetOp(packet_res, mat.template packetByOuterInner<Unaligned>(j,i));
res = func.predux(packet_res);
for(int j=0; j<outerSize; ++j)
for(int i=packetedInnerSize; i<innerSize; ++i)
for(Index j=0; j<outerSize; ++j)
for(Index i=packetedInnerSize; i<innerSize; ++i)
res = func(res, mat.coeffByOuterInner(j,i));
}
else // too small to vectorize anything.