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eigen/bench/btl/libs/hand_vec/hand_vec_interface.hh

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resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
//=====================================================
// File : hand_vec_interface.hh
// Copyright (C) 2008 Gael Guennebaud <g.gael@free.fr>
//=====================================================
//
// This program is free software; you can redistribute it and/or
// modify it under the terms of the GNU General Public License
// as published by the Free Software Foundation; either version 2
// of the License, or (at your option) any later version.
//
// This program is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
// You should have received a copy of the GNU General Public License
// along with this program; if not, write to the Free Software
// Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
//
#ifndef HAND_VEC_INTERFACE_HH
#define HAND_VEC_INTERFACE_HH
#include <Eigen/Core>
#include "f77_interface.hh"
using namespace Eigen;
template<class real>
class hand_vec_interface : public f77_interface_base<real> {
public :
typedef typename ei_packet_traits<real>::type Packet;
static const int PacketSize = ei_packet_traits<real>::size;
typedef typename f77_interface_base<real>::stl_matrix stl_matrix;
typedef typename f77_interface_base<real>::stl_vector stl_vector;
typedef typename f77_interface_base<real>::gene_matrix gene_matrix;
typedef typename f77_interface_base<real>::gene_vector gene_vector;
static void free_matrix(gene_matrix & A, int N){
the big memory changes. the most important changes are: ei_aligned_malloc now really behaves like a malloc (untyped, doesn't call ctor) ei_aligned_new is the typed variant calling ctor EIGEN_MAKE_ALIGNED_OPERATOR_NEW now takes the class name as parameter
2009-01-08 23:20:21 +08:00
ei_aligned_delete(A);
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
}
static void free_vector(gene_vector & B){
the big memory changes. the most important changes are: ei_aligned_malloc now really behaves like a malloc (untyped, doesn't call ctor) ei_aligned_new is the typed variant calling ctor EIGEN_MAKE_ALIGNED_OPERATOR_NEW now takes the class name as parameter
2009-01-08 23:20:21 +08:00
ei_aligned_delete(B);
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
}
static inline void matrix_from_stl(gene_matrix & A, stl_matrix & A_stl){
int N = A_stl.size();
the big memory changes. the most important changes are: ei_aligned_malloc now really behaves like a malloc (untyped, doesn't call ctor) ei_aligned_new is the typed variant calling ctor EIGEN_MAKE_ALIGNED_OPERATOR_NEW now takes the class name as parameter
2009-01-08 23:20:21 +08:00
A = ei_aligned_new<real>(N*N);
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
for (int j=0;j<N;j++)
for (int i=0;i<N;i++)
A[i+N*j] = A_stl[j][i];
}
static inline void vector_from_stl(gene_vector & B, stl_vector & B_stl){
int N = B_stl.size();
the big memory changes. the most important changes are: ei_aligned_malloc now really behaves like a malloc (untyped, doesn't call ctor) ei_aligned_new is the typed variant calling ctor EIGEN_MAKE_ALIGNED_OPERATOR_NEW now takes the class name as parameter
2009-01-08 23:20:21 +08:00
B = ei_aligned_new<real>(N);
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
for (int i=0;i<N;i++)
B[i] = B_stl[i];
}
static inline std::string name() {
#ifdef PEELING
return "hand_vectorized_peeling";
#else
return "hand_vectorized";
#endif
}
* updated benchmark files according to recent renamings * various improvements in BTL including trisolver and cholesky bench
2008-07-27 19:39:47 +08:00
static inline void matrix_vector_product(const gene_matrix & A, const gene_vector & B, gene_vector & X, int N)
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
{
various minor updates in the benchmark suite like non inlining of some functions as well as the experimental C code used to design efficient eigen's matrix vector products.
2008-07-12 20:14:08 +08:00
asm("#begin matrix_vector_product");
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
int AN = (N/PacketSize)*PacketSize;
* updated benchmark files according to recent renamings * various improvements in BTL including trisolver and cholesky bench
2008-07-27 19:39:47 +08:00
int ANP = (AN/(2*PacketSize))*2*PacketSize;
various minor updates in the benchmark suite like non inlining of some functions as well as the experimental C code used to design efficient eigen's matrix vector products.
2008-07-12 20:14:08 +08:00
int bound = (N/4)*4;
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
for (int i=0;i<N;i++)
X[i] = 0;
various minor updates in the benchmark suite like non inlining of some functions as well as the experimental C code used to design efficient eigen's matrix vector products.
2008-07-12 20:14:08 +08:00
for (int i=0;i<bound;i+=4)
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
{
* updated benchmark files according to recent renamings * various improvements in BTL including trisolver and cholesky bench
2008-07-27 19:39:47 +08:00
register real* __restrict__ A0 = A + i*N;
register real* __restrict__ A1 = A + (i+1)*N;
register real* __restrict__ A2 = A + (i+2)*N;
register real* __restrict__ A3 = A + (i+3)*N;
Packet ptmp0 = ei_pset1(B[i]);
Packet ptmp1 = ei_pset1(B[i+1]);
Packet ptmp2 = ei_pset1(B[i+2]);
Packet ptmp3 = ei_pset1(B[i+3]);
// register Packet ptmp0, ptmp1, ptmp2, ptmp3;
// asm(
//
// "movss (%[B],%[j],4), %[ptmp0] \n\t"
// "shufps $0,%[ptmp0],%[ptmp0] \n\t"
// "movss 4(%[B],%[j],4), %[ptmp1] \n\t"
// "shufps $0,%[ptmp1],%[ptmp1] \n\t"
// "movss 8(%[B],%[j],4), %[ptmp2] \n\t"
// "shufps $0,%[ptmp2],%[ptmp2] \n\t"
// "movss 12(%[B],%[j],4), %[ptmp3] \n\t"
// "shufps $0,%[ptmp3],%[ptmp3] \n\t"
// : [ptmp0] "=x" (ptmp0),
// [ptmp1] "=x" (ptmp1),
// [ptmp2] "=x" (ptmp2),
// [ptmp3] "=x" (ptmp3)
// : [B] "r" (B),
// [j] "r" (size_t(i))
// : );
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
if (AN>0)
{
* updated benchmark files according to recent renamings * various improvements in BTL including trisolver and cholesky bench
2008-07-27 19:39:47 +08:00
// for (size_t j = 0;j<ANP;j+=8)
// {
// asm(
//
// "movaps (%[A0],%[j],4), %%xmm8 \n\t"
// "movaps 16(%[A0],%[j],4), %%xmm12 \n\t"
// "movups (%[A3],%[j],4), %%xmm11 \n\t"
// "movups 16(%[A3],%[j],4), %%xmm15 \n\t"
// "movups (%[A2],%[j],4), %%xmm10 \n\t"
// "movups 16(%[A2],%[j],4), %%xmm14 \n\t"
// "movups (%[A1],%[j],4), %%xmm9 \n\t"
// "movups 16(%[A1],%[j],4), %%xmm13 \n\t"
//
// "mulps %[ptmp0], %%xmm8 \n\t"
// "addps (%[res0],%[j],4), %%xmm8 \n\t"
// "mulps %[ptmp3], %%xmm11 \n\t"
// "addps %%xmm11, %%xmm8 \n\t"
// "mulps %[ptmp2], %%xmm10 \n\t"
// "addps %%xmm10, %%xmm8 \n\t"
// "mulps %[ptmp1], %%xmm9 \n\t"
// "addps %%xmm9, %%xmm8 \n\t"
// "movaps %%xmm8, (%[res0],%[j],4) \n\t"
//
// "mulps %[ptmp0], %%xmm12 \n\t"
// "addps 16(%[res0],%[j],4), %%xmm12 \n\t"
// "mulps %[ptmp3], %%xmm15 \n\t"
// "addps %%xmm15, %%xmm12 \n\t"
// "mulps %[ptmp2], %%xmm14 \n\t"
// "addps %%xmm14, %%xmm12 \n\t"
// "mulps %[ptmp1], %%xmm13 \n\t"
// "addps %%xmm13, %%xmm12 \n\t"
// "movaps %%xmm12, 16(%[res0],%[j],4) \n\t"
// :
// : [res0] "r" (X), [j] "r" (j),[A0] "r" (A0),
// [A1] "r" (A1),
// [A2] "r" (A2),
// [A3] "r" (A3),
// [ptmp0] "x" (ptmp0),
// [ptmp1] "x" (ptmp1),
// [ptmp2] "x" (ptmp2),
// [ptmp3] "x" (ptmp3)
// : "%xmm8", "%xmm9", "%xmm10", "%xmm11", "%xmm12", "%xmm13", "%xmm14", "%xmm15", "%r14");
// }
register Packet A00;
register Packet A01;
register Packet A02;
register Packet A03;
register Packet A10;
register Packet A11;
register Packet A12;
register Packet A13;
for (int j = 0;j<ANP;j+=2*PacketSize)
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
{
* updated benchmark files according to recent renamings * various improvements in BTL including trisolver and cholesky bench
2008-07-27 19:39:47 +08:00
// A00 = ei_pload(&A0[j]);
// A01 = ei_ploadu(&A1[j]);
// A02 = ei_ploadu(&A2[j]);
// A03 = ei_ploadu(&A3[j]);
// A10 = ei_pload(&A0[j+PacketSize]);
// A11 = ei_ploadu(&A1[j+PacketSize]);
// A12 = ei_ploadu(&A2[j+PacketSize]);
// A13 = ei_ploadu(&A3[j+PacketSize]);
//
// A00 = ei_pmul(ptmp0, A00);
// A01 = ei_pmul(ptmp1, A01);
// A02 = ei_pmul(ptmp2, A02);
// A03 = ei_pmul(ptmp3, A03);
// A10 = ei_pmul(ptmp0, A10);
// A11 = ei_pmul(ptmp1, A11);
// A12 = ei_pmul(ptmp2, A12);
// A13 = ei_pmul(ptmp3, A13);
//
// A00 = ei_padd(A00,A01);
// A02 = ei_padd(A02,A03);
// A00 = ei_padd(A00,ei_pload(&X[j]));
// A00 = ei_padd(A00,A02);
// ei_pstore(&X[j],A00);
//
// A10 = ei_padd(A10,A11);
// A12 = ei_padd(A12,A13);
// A10 = ei_padd(A10,ei_pload(&X[j+PacketSize]));
// A10 = ei_padd(A10,A12);
// ei_pstore(&X[j+PacketSize],A10);
various minor updates in the benchmark suite like non inlining of some functions as well as the experimental C code used to design efficient eigen's matrix vector products.
2008-07-12 20:14:08 +08:00
ei_pstore(&X[j],
ei_padd(ei_pload(&X[j]),
ei_padd(
* updated benchmark files according to recent renamings * various improvements in BTL including trisolver and cholesky bench
2008-07-27 19:39:47 +08:00
ei_padd(ei_pmul(ptmp0,ei_pload(&A0[j])),ei_pmul(ptmp1,ei_ploadu(&A1[j]))),
ei_padd(ei_pmul(ptmp2,ei_ploadu(&A2[j])),ei_pmul(ptmp3,ei_ploadu(&A3[j]))) )));
various minor updates in the benchmark suite like non inlining of some functions as well as the experimental C code used to design efficient eigen's matrix vector products.
2008-07-12 20:14:08 +08:00
ei_pstore(&X[j+PacketSize],
ei_padd(ei_pload(&X[j+PacketSize]),
ei_padd(
* updated benchmark files according to recent renamings * various improvements in BTL including trisolver and cholesky bench
2008-07-27 19:39:47 +08:00
ei_padd(ei_pmul(ptmp0,ei_pload(&A0[j+PacketSize])),ei_pmul(ptmp1,ei_ploadu(&A1[j+PacketSize]))),
ei_padd(ei_pmul(ptmp2,ei_ploadu(&A2[j+PacketSize])),ei_pmul(ptmp3,ei_ploadu(&A3[j+PacketSize]))) )));
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
}
for (int j = ANP;j<AN;j+=PacketSize)
various minor updates in the benchmark suite like non inlining of some functions as well as the experimental C code used to design efficient eigen's matrix vector products.
2008-07-12 20:14:08 +08:00
ei_pstore(&X[j],
ei_padd(ei_pload(&X[j]),
ei_padd(
* updated benchmark files according to recent renamings * various improvements in BTL including trisolver and cholesky bench
2008-07-27 19:39:47 +08:00
ei_padd(ei_pmul(ptmp0,ei_pload(&A0[j])),ei_pmul(ptmp1,ei_ploadu(&A1[j]))),
ei_padd(ei_pmul(ptmp2,ei_ploadu(&A2[j])),ei_pmul(ptmp3,ei_ploadu(&A3[j]))) )));
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
}
// process remaining scalars
for (int j=AN;j<N;j++)
* updated benchmark files according to recent renamings * various improvements in BTL including trisolver and cholesky bench
2008-07-27 19:39:47 +08:00
X[j] += ei_pfirst(ptmp0) * A0[j] + ei_pfirst(ptmp1) * A1[j] + ei_pfirst(ptmp2) * A2[j] + ei_pfirst(ptmp3) * A3[j];
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
}
various minor updates in the benchmark suite like non inlining of some functions as well as the experimental C code used to design efficient eigen's matrix vector products.
2008-07-12 20:14:08 +08:00
for (int i=bound;i<N;i++)
{
real tmp0 = B[i];
Packet ptmp0 = ei_pset1(tmp0);
int iN0 = i*N;
if (AN>0)
{
bool aligned0 = (iN0 % PacketSize) == 0;
if (aligned0)
for (int j = 0;j<AN;j+=PacketSize)
ei_pstore(&X[j], ei_padd(ei_pmul(ptmp0,ei_pload(&A[j+iN0])),ei_pload(&X[j])));
else
for (int j = 0;j<AN;j+=PacketSize)
ei_pstore(&X[j], ei_padd(ei_pmul(ptmp0,ei_ploadu(&A[j+iN0])),ei_pload(&X[j])));
}
// process remaining scalars
for (int j=AN;j<N;j++)
X[j] += tmp0 * A[j+iN0];
}
asm("#end matrix_vector_product");
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
}
* updated benchmark files according to recent renamings * various improvements in BTL including trisolver and cholesky bench
2008-07-27 19:39:47 +08:00
// static inline void matrix_vector_product(const gene_matrix & A, const gene_vector & B, gene_vector & X, int N)
// {
// asm("#begin matrix_vector_product");
// int AN = (N/PacketSize)*PacketSize;
// int ANP = (AN/(2*PacketSize))*2*PacketSize;
// int bound = (N/4)*4;
// for (int i=0;i<N;i++)
// X[i] = 0;
//
// for (int i=0;i<bound;i+=4)
// {
// real tmp0 = B[i];
// Packet ptmp0 = ei_pset1(tmp0);
// real tmp1 = B[i+1];
// Packet ptmp1 = ei_pset1(tmp1);
// real tmp2 = B[i+2];
// Packet ptmp2 = ei_pset1(tmp2);
// real tmp3 = B[i+3];
// Packet ptmp3 = ei_pset1(tmp3);
// int iN0 = i*N;
// int iN1 = (i+1)*N;
// int iN2 = (i+2)*N;
// int iN3 = (i+3)*N;
// if (AN>0)
// {
// // int aligned0 = (iN0 % PacketSize);
// int aligned1 = (iN1 % PacketSize);
//
// if (aligned1==0)
// {
// for (int j = 0;j<AN;j+=PacketSize)
// {
// ei_pstore(&X[j],
// ei_padd(ei_pload(&X[j]),
// ei_padd(
// ei_padd(ei_pmul(ptmp0,ei_pload(&A[j+iN0])),ei_pmul(ptmp1,ei_pload(&A[j+iN1]))),
// ei_padd(ei_pmul(ptmp2,ei_pload(&A[j+iN2])),ei_pmul(ptmp3,ei_pload(&A[j+iN3]))) )));
// }
// }
// else if (aligned1==2)
// {
// for (int j = 0;j<AN;j+=PacketSize)
// {
// ei_pstore(&X[j],
// ei_padd(ei_pload(&X[j]),
// ei_padd(
// ei_padd(ei_pmul(ptmp0,ei_pload(&A[j+iN0])),ei_pmul(ptmp1,ei_ploadu(&A[j+iN1]))),
// ei_padd(ei_pmul(ptmp2,ei_pload(&A[j+iN2])),ei_pmul(ptmp3,ei_ploadu(&A[j+iN3]))) )));
// }
// }
// else
// {
// for (int j = 0;j<ANP;j+=2*PacketSize)
// {
// ei_pstore(&X[j],
// ei_padd(ei_pload(&X[j]),
// ei_padd(
// ei_padd(ei_pmul(ptmp0,ei_pload(&A[j+iN0])),ei_pmul(ptmp1,ei_ploadu(&A[j+iN1]))),
// ei_padd(ei_pmul(ptmp2,ei_ploadu(&A[j+iN2])),ei_pmul(ptmp3,ei_ploadu(&A[j+iN3]))) )));
//
// ei_pstore(&X[j+PacketSize],
// ei_padd(ei_pload(&X[j+PacketSize]),
// ei_padd(
// ei_padd(ei_pmul(ptmp0,ei_pload(&A[j+PacketSize+iN0])),ei_pmul(ptmp1,ei_ploadu(&A[j+PacketSize+iN1]))),
// ei_padd(ei_pmul(ptmp2,ei_ploadu(&A[j+PacketSize+iN2])),ei_pmul(ptmp3,ei_ploadu(&A[j+PacketSize+iN3]))) )));
//
// // ei_pstore(&X[j+2*PacketSize],
// // ei_padd(ei_pload(&X[j+2*PacketSize]),
// // ei_padd(
// // ei_padd(ei_pmul(ptmp0,ei_pload(&A[j+2*PacketSize+iN0])),ei_pmul(ptmp1,ei_ploadu(&A[j+2*PacketSize+iN1]))),
// // ei_padd(ei_pmul(ptmp2,ei_ploadu(&A[j+2*PacketSize+iN2])),ei_pmul(ptmp3,ei_ploadu(&A[j+2*PacketSize+iN3]))) )));
// //
// // ei_pstore(&X[j+3*PacketSize],
// // ei_padd(ei_pload(&X[j+3*PacketSize]),
// // ei_padd(
// // ei_padd(ei_pmul(ptmp0,ei_pload(&A[j+3*PacketSize+iN0])),ei_pmul(ptmp1,ei_ploadu(&A[j+3*PacketSize+iN1]))),
// // ei_padd(ei_pmul(ptmp2,ei_ploadu(&A[j+3*PacketSize+iN2])),ei_pmul(ptmp3,ei_ploadu(&A[j+3*PacketSize+iN3]))) )));
//
// }
// for (int j = ANP;j<AN;j+=PacketSize)
// ei_pstore(&X[j],
// ei_padd(ei_pload(&X[j]),
// ei_padd(
// ei_padd(ei_pmul(ptmp0,ei_ploadu(&A[j+iN0])),ei_pmul(ptmp1,ei_ploadu(&A[j+iN1]))),
// ei_padd(ei_pmul(ptmp2,ei_ploadu(&A[j+iN2])),ei_pmul(ptmp3,ei_ploadu(&A[j+iN3]))) )));
// }
// }
// // process remaining scalars
// for (int j=AN;j<N;j++)
// X[j] += tmp0 * A[j+iN0] + tmp1 * A[j+iN1] + tmp2 * A[j+iN2] + tmp3 * A[j+iN3];
// }
// for (int i=bound;i<N;i++)
// {
// real tmp0 = B[i];
// Packet ptmp0 = ei_pset1(tmp0);
// int iN0 = i*N;
// if (AN>0)
// {
// bool aligned0 = (iN0 % PacketSize) == 0;
// if (aligned0)
// for (int j = 0;j<AN;j+=PacketSize)
// ei_pstore(&X[j], ei_padd(ei_pmul(ptmp0,ei_pload(&A[j+iN0])),ei_pload(&X[j])));
// else
// for (int j = 0;j<AN;j+=PacketSize)
// ei_pstore(&X[j], ei_padd(ei_pmul(ptmp0,ei_ploadu(&A[j+iN0])),ei_pload(&X[j])));
// }
// // process remaining scalars
// for (int j=AN;j<N;j++)
// X[j] += tmp0 * A[j+iN0];
// }
// asm("#end matrix_vector_product");
// }
various minor updates in the benchmark suite like non inlining of some functions as well as the experimental C code used to design efficient eigen's matrix vector products.
2008-07-12 20:14:08 +08:00
// static inline void matrix_vector_product(const gene_matrix & A, const gene_vector & B, gene_vector & X, int N)
// {
// asm("#begin matrix_vector_product");
// int AN = (N/PacketSize)*PacketSize;
// for (int i=0;i<N;i++)
// X[i] = 0;
//
// for (int i=0;i<N;i+=2)
// {
// real tmp0 = B[i];
// Packet ptmp0 = ei_pset1(tmp0);
// real tmp1 = B[i+1];
// Packet ptmp1 = ei_pset1(tmp1);
// int iN0 = i*N;
// int iN1 = (i+1)*N;
// if (AN>0)
// {
// bool aligned0 = (iN0 % PacketSize) == 0;
// bool aligned1 = (iN1 % PacketSize) == 0;
//
// if (aligned0 && aligned1)
// {
// for (int j = 0;j<AN;j+=PacketSize)
// {
// ei_pstore(&X[j],
// ei_padd(ei_pmul(ptmp0,ei_pload(&A[j+iN0])),
// ei_padd(ei_pmul(ptmp1,ei_pload(&A[j+iN1])),ei_pload(&X[j]))));
// }
// }
// else if (aligned0)
// {
// for (int j = 0;j<AN;j+=PacketSize)
// {
// ei_pstore(&X[j],
// ei_padd(ei_pmul(ptmp0,ei_pload(&A[j+iN0])),
// ei_padd(ei_pmul(ptmp1,ei_ploadu(&A[j+iN1])),ei_pload(&X[j]))));
// }
// }
// else if (aligned1)
// {
// for (int j = 0;j<AN;j+=PacketSize)
// {
// ei_pstore(&X[j],
// ei_padd(ei_pmul(ptmp0,ei_ploadu(&A[j+iN0])),
// ei_padd(ei_pmul(ptmp1,ei_pload(&A[j+iN1])),ei_pload(&X[j]))));
// }
// }
// else
// {
// int ANP = (AN/(4*PacketSize))*4*PacketSize;
// for (int j = 0;j<ANP;j+=4*PacketSize)
// {
// ei_pstore(&X[j],
// ei_padd(ei_pmul(ptmp0,ei_ploadu(&A[j+iN0])),
// ei_padd(ei_pmul(ptmp1,ei_ploadu(&A[j+iN1])),ei_pload(&X[j]))));
//
// ei_pstore(&X[j+PacketSize],
// ei_padd(ei_pmul(ptmp0,ei_ploadu(&A[j+PacketSize+iN0])),
// ei_padd(ei_pmul(ptmp1,ei_ploadu(&A[j+PacketSize+iN1])),ei_pload(&X[j+PacketSize]))));
//
// ei_pstore(&X[j+2*PacketSize],
// ei_padd(ei_pmul(ptmp0,ei_ploadu(&A[j+2*PacketSize+iN0])),
// ei_padd(ei_pmul(ptmp1,ei_ploadu(&A[j+2*PacketSize+iN1])),ei_pload(&X[j+2*PacketSize]))));
//
// ei_pstore(&X[j+3*PacketSize],
// ei_padd(ei_pmul(ptmp0,ei_ploadu(&A[j+3*PacketSize+iN0])),
// ei_padd(ei_pmul(ptmp1,ei_ploadu(&A[j+3*PacketSize+iN1])),ei_pload(&X[j+3*PacketSize]))));
// }
// for (int j = ANP;j<AN;j+=PacketSize)
// ei_pstore(&X[j],
// ei_padd(ei_pmul(ptmp0,ei_ploadu(&A[j+iN0])),
// ei_padd(ei_pmul(ptmp1,ei_ploadu(&A[j+iN1])),ei_pload(&X[j]))));
// }
// }
// // process remaining scalars
// for (int j=AN;j<N;j++)
// X[j] += tmp0 * A[j+iN0] + tmp1 * A[j+iN1];
// }
// int remaining = (N/2)*2;
// for (int i=remaining;i<N;i++)
// {
// real tmp0 = B[i];
// Packet ptmp0 = ei_pset1(tmp0);
// int iN0 = i*N;
// if (AN>0)
// {
// bool aligned0 = (iN0 % PacketSize) == 0;
// if (aligned0)
// for (int j = 0;j<AN;j+=PacketSize)
// ei_pstore(&X[j], ei_padd(ei_pmul(ptmp0,ei_pload(&A[j+iN0])),ei_pload(&X[j])));
// else
// for (int j = 0;j<AN;j+=PacketSize)
// ei_pstore(&X[j], ei_padd(ei_pmul(ptmp0,ei_ploadu(&A[j+iN0])),ei_pload(&X[j])));
// }
// // process remaining scalars
// for (int j=AN;j<N;j++)
// X[j] += tmp0 * A[j+iN0];
// }
// asm("#end matrix_vector_product");
// }
// static inline void matrix_vector_product(const gene_matrix & A, const gene_vector & B, gene_vector & X, int N)
// {
// asm("#begin matrix_vector_product");
// int AN = (N/PacketSize)*PacketSize;
// for (int i=0;i<N;i++)
// X[i] = 0;
// for (int i=0;i<N;i++)
// {
// real tmp = B[i];
// Packet ptmp = ei_pset1(tmp);
// int iN = i*N;
// if (AN>0)
// {
// bool aligned = (iN % PacketSize) == 0;
// if (aligned)
// {
// #ifdef PEELING
// Packet A0, A1, A2, X0, X1, X2;
// int ANP = (AN/(8*PacketSize))*8*PacketSize;
// for (int j = 0;j<ANP;j+=PacketSize*8)
// {
// A0 = ei_pload(&A[j+iN]);
// X0 = ei_pload(&X[j]);
// A1 = ei_pload(&A[j+PacketSize+iN]);
// X1 = ei_pload(&X[j+PacketSize]);
// A2 = ei_pload(&A[j+2*PacketSize+iN]);
// X2 = ei_pload(&X[j+2*PacketSize]);
// ei_pstore(&X[j], ei_padd(X0, ei_pmul(ptmp,A0)));
// A0 = ei_pload(&A[j+3*PacketSize+iN]);
// X0 = ei_pload(&X[j+3*PacketSize]);
// ei_pstore(&X[j+PacketSize], ei_padd(ei_pload(&X1), ei_pmul(ptmp,A1)));
// A1 = ei_pload(&A[j+4*PacketSize+iN]);
// X1 = ei_pload(&X[j+4*PacketSize]);
// ei_pstore(&X[j+2*PacketSize], ei_padd(ei_pload(&X2), ei_pmul(ptmp,A2)));
// A2 = ei_pload(&A[j+5*PacketSize+iN]);
// X2 = ei_pload(&X[j+5*PacketSize]);
// ei_pstore(&X[j+3*PacketSize], ei_padd(ei_pload(&X0), ei_pmul(ptmp,A0)));
// A0 = ei_pload(&A[j+6*PacketSize+iN]);
// X0 = ei_pload(&X[j+6*PacketSize]);
// ei_pstore(&X[j+4*PacketSize], ei_padd(ei_pload(&X1), ei_pmul(ptmp,A1)));
// A1 = ei_pload(&A[j+7*PacketSize+iN]);
// X1 = ei_pload(&X[j+7*PacketSize]);
// ei_pstore(&X[j+5*PacketSize], ei_padd(ei_pload(&X2), ei_pmul(ptmp,A2)));
// ei_pstore(&X[j+6*PacketSize], ei_padd(ei_pload(&X0), ei_pmul(ptmp,A0)));
// ei_pstore(&X[j+7*PacketSize], ei_padd(ei_pload(&X1), ei_pmul(ptmp,A1)));
// //
// // ei_pstore(&X[j], ei_padd(ei_pload(&X[j]), ei_pmul(ptmp,ei_pload(&A[j+iN]))));
// // ei_pstore(&X[j+PacketSize], ei_padd(ei_pload(&X[j+PacketSize]), ei_pmul(ptmp,ei_pload(&A[j+PacketSize+iN]))));
// // ei_pstore(&X[j+2*PacketSize], ei_padd(ei_pload(&X[j+2*PacketSize]), ei_pmul(ptmp,ei_pload(&A[j+2*PacketSize+iN]))));
// // ei_pstore(&X[j+3*PacketSize], ei_padd(ei_pload(&X[j+3*PacketSize]), ei_pmul(ptmp,ei_pload(&A[j+3*PacketSize+iN]))));
// // ei_pstore(&X[j+4*PacketSize], ei_padd(ei_pload(&X[j+4*PacketSize]), ei_pmul(ptmp,ei_pload(&A[j+4*PacketSize+iN]))));
// // ei_pstore(&X[j+5*PacketSize], ei_padd(ei_pload(&X[j+5*PacketSize]), ei_pmul(ptmp,ei_pload(&A[j+5*PacketSize+iN]))));
// // ei_pstore(&X[j+6*PacketSize], ei_padd(ei_pload(&X[j+6*PacketSize]), ei_pmul(ptmp,ei_pload(&A[j+6*PacketSize+iN]))));
// // ei_pstore(&X[j+7*PacketSize], ei_padd(ei_pload(&X[j+7*PacketSize]), ei_pmul(ptmp,ei_pload(&A[j+7*PacketSize+iN]))));
// }
// for (int j = ANP;j<AN;j+=PacketSize)
// ei_pstore(&X[j], ei_padd(ei_pload(&X[j]), ei_pmul(ptmp,ei_pload(&A[j+iN]))));
// #else
// for (int j = 0;j<AN;j+=PacketSize)
// ei_pstore(&X[j], ei_padd(ei_pload(&X[j]), ei_pmul(ptmp,ei_pload(&A[j+iN]))));
// #endif
// }
// else
// {
// #ifdef PEELING
// int ANP = (AN/(8*PacketSize))*8*PacketSize;
// for (int j = 0;j<ANP;j+=PacketSize*8)
// {
// ei_pstore(&X[j], ei_padd(ei_pload(&X[j]), ei_pmul(ptmp,ei_ploadu(&A[j+iN]))));
// ei_pstore(&X[j+PacketSize], ei_padd(ei_pload(&X[j+PacketSize]), ei_pmul(ptmp,ei_ploadu(&A[j+PacketSize+iN]))));
// ei_pstore(&X[j+2*PacketSize], ei_padd(ei_pload(&X[j+2*PacketSize]), ei_pmul(ptmp,ei_ploadu(&A[j+2*PacketSize+iN]))));
// ei_pstore(&X[j+3*PacketSize], ei_padd(ei_pload(&X[j+3*PacketSize]), ei_pmul(ptmp,ei_ploadu(&A[j+3*PacketSize+iN]))));
// ei_pstore(&X[j+4*PacketSize], ei_padd(ei_pload(&X[j+4*PacketSize]), ei_pmul(ptmp,ei_ploadu(&A[j+4*PacketSize+iN]))));
// ei_pstore(&X[j+5*PacketSize], ei_padd(ei_pload(&X[j+5*PacketSize]), ei_pmul(ptmp,ei_ploadu(&A[j+5*PacketSize+iN]))));
// ei_pstore(&X[j+6*PacketSize], ei_padd(ei_pload(&X[j+6*PacketSize]), ei_pmul(ptmp,ei_ploadu(&A[j+6*PacketSize+iN]))));
// ei_pstore(&X[j+7*PacketSize], ei_padd(ei_pload(&X[j+7*PacketSize]), ei_pmul(ptmp,ei_ploadu(&A[j+7*PacketSize+iN]))));
// }
// for (int j = ANP;j<AN;j+=PacketSize)
// ei_pstore(&X[j], ei_padd(ei_pload(&X[j]), ei_pmul(ptmp,ei_ploadu(&A[j+iN]))));
// #else
// for (int j = 0;j<AN;j+=PacketSize)
// ei_pstore(&X[j], ei_padd(ei_pload(&X[j]), ei_pmul(ptmp,ei_ploadu(&A[j+iN]))));
// #endif
// }
// }
// // process remaining scalars
// for (int j=AN;j<N;j++)
// X[j] += tmp * A[j+iN];
// }
// asm("#end matrix_vector_product");
// }
static inline void atv_product(const gene_matrix & A, const gene_vector & B, gene_vector & X, int N)
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
{
int AN = (N/PacketSize)*PacketSize;
various minor updates in the benchmark suite like non inlining of some functions as well as the experimental C code used to design efficient eigen's matrix vector products.
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int bound = (N/4)*4;
for (int i=0;i<bound;i+=4)
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
{
various minor updates in the benchmark suite like non inlining of some functions as well as the experimental C code used to design efficient eigen's matrix vector products.
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real tmp0 = 0;
Packet ptmp0 = ei_pset1(real(0));
real tmp1 = 0;
Packet ptmp1 = ei_pset1(real(0));
real tmp2 = 0;
Packet ptmp2 = ei_pset1(real(0));
real tmp3 = 0;
Packet ptmp3 = ei_pset1(real(0));
int iN0 = i*N;
int iN1 = (i+1)*N;
int iN2 = (i+2)*N;
int iN3 = (i+3)*N;
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
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if (AN>0)
{
various minor updates in the benchmark suite like non inlining of some functions as well as the experimental C code used to design efficient eigen's matrix vector products.
2008-07-12 20:14:08 +08:00
int align1 = (iN1 % PacketSize);
if (align1==0)
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
{
various minor updates in the benchmark suite like non inlining of some functions as well as the experimental C code used to design efficient eigen's matrix vector products.
2008-07-12 20:14:08 +08:00
for (int j = 0;j<AN;j+=PacketSize)
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
{
various minor updates in the benchmark suite like non inlining of some functions as well as the experimental C code used to design efficient eigen's matrix vector products.
2008-07-12 20:14:08 +08:00
Packet b = ei_pload(&B[j]);
ptmp0 = ei_padd(ptmp0, ei_pmul(b, ei_pload(&A[j+iN0])));
ptmp1 = ei_padd(ptmp1, ei_pmul(b, ei_pload(&A[j+iN1])));
ptmp2 = ei_padd(ptmp2, ei_pmul(b, ei_pload(&A[j+iN2])));
ptmp3 = ei_padd(ptmp3, ei_pmul(b, ei_pload(&A[j+iN3])));
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
}
various minor updates in the benchmark suite like non inlining of some functions as well as the experimental C code used to design efficient eigen's matrix vector products.
2008-07-12 20:14:08 +08:00
}
else if (align1==2)
{
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
for (int j = 0;j<AN;j+=PacketSize)
various minor updates in the benchmark suite like non inlining of some functions as well as the experimental C code used to design efficient eigen's matrix vector products.
2008-07-12 20:14:08 +08:00
{
Packet b = ei_pload(&B[j]);
ptmp0 = ei_padd(ptmp0, ei_pmul(b, ei_pload(&A[j+iN0])));
ptmp1 = ei_padd(ptmp1, ei_pmul(b, ei_ploadu(&A[j+iN1])));
ptmp2 = ei_padd(ptmp2, ei_pmul(b, ei_pload(&A[j+iN2])));
ptmp3 = ei_padd(ptmp3, ei_pmul(b, ei_ploadu(&A[j+iN3])));
}
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
}
else
{
various minor updates in the benchmark suite like non inlining of some functions as well as the experimental C code used to design efficient eigen's matrix vector products.
2008-07-12 20:14:08 +08:00
for (int j = 0;j<AN;j+=PacketSize)
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
{
various minor updates in the benchmark suite like non inlining of some functions as well as the experimental C code used to design efficient eigen's matrix vector products.
2008-07-12 20:14:08 +08:00
Packet b = ei_pload(&B[j]);
ptmp0 = ei_padd(ptmp0, ei_pmul(b, ei_pload(&A[j+iN0])));
ptmp1 = ei_padd(ptmp1, ei_pmul(b, ei_ploadu(&A[j+iN1])));
ptmp2 = ei_padd(ptmp2, ei_pmul(b, ei_ploadu(&A[j+iN2])));
ptmp3 = ei_padd(ptmp3, ei_pmul(b, ei_ploadu(&A[j+iN3])));
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
}
}
various minor updates in the benchmark suite like non inlining of some functions as well as the experimental C code used to design efficient eigen's matrix vector products.
2008-07-12 20:14:08 +08:00
tmp0 = ei_predux(ptmp0);
tmp1 = ei_predux(ptmp1);
tmp2 = ei_predux(ptmp2);
tmp3 = ei_predux(ptmp3);
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
}
// process remaining scalars
for (int j=AN;j<N;j++)
various minor updates in the benchmark suite like non inlining of some functions as well as the experimental C code used to design efficient eigen's matrix vector products.
2008-07-12 20:14:08 +08:00
{
tmp0 += B[j] * A[j+iN0];
tmp1 += B[j] * A[j+iN1];
tmp2 += B[j] * A[j+iN2];
tmp3 += B[j] * A[j+iN3];
}
X[i+0] = tmp0;
X[i+1] = tmp1;
X[i+2] = tmp2;
X[i+3] = tmp3;
}
for (int i=bound;i<N;i++)
{
real tmp0 = 0;
Packet ptmp0 = ei_pset1(real(0));
int iN0 = i*N;
if (AN>0)
{
if (iN0 % PacketSize==0)
for (int j = 0;j<AN;j+=PacketSize)
ptmp0 = ei_padd(ptmp0, ei_pmul(ei_pload(&B[j]), ei_pload(&A[j+iN0])));
else
for (int j = 0;j<AN;j+=PacketSize)
ptmp0 = ei_padd(ptmp0, ei_pmul(ei_pload(&B[j]), ei_ploadu(&A[j+iN0])));
tmp0 = ei_predux(ptmp0);
}
// process remaining scalars
for (int j=AN;j<N;j++)
tmp0 += B[j] * A[j+iN0];
X[i+0] = tmp0;
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
}
}
various minor updates in the benchmark suite like non inlining of some functions as well as the experimental C code used to design efficient eigen's matrix vector products.
2008-07-12 20:14:08 +08:00
// static inline void atv_product(const gene_matrix & A, const gene_vector & B, gene_vector & X, int N)
// {
// int AN = (N/PacketSize)*PacketSize;
// for (int i=0;i<N;i++)
// X[i] = 0;
// for (int i=0;i<N;i++)
// {
// real tmp = 0;
// Packet ptmp = ei_pset1(real(0));
// int iN = i*N;
// if (AN>0)
// {
// bool aligned = (iN % PacketSize) == 0;
// if (aligned)
// {
// #ifdef PEELING
// int ANP = (AN/(8*PacketSize))*8*PacketSize;
// for (int j = 0;j<ANP;j+=PacketSize*8)
// {
// ptmp =
// ei_padd(ei_pmul(ei_pload(&B[j]), ei_pload(&A[j+iN])),
// ei_padd(ei_pmul(ei_pload(&B[j+PacketSize]), ei_pload(&A[j+PacketSize+iN])),
// ei_padd(ei_pmul(ei_pload(&B[j+2*PacketSize]), ei_pload(&A[j+2*PacketSize+iN])),
// ei_padd(ei_pmul(ei_pload(&B[j+3*PacketSize]), ei_pload(&A[j+3*PacketSize+iN])),
// ei_padd(ei_pmul(ei_pload(&B[j+4*PacketSize]), ei_pload(&A[j+4*PacketSize+iN])),
// ei_padd(ei_pmul(ei_pload(&B[j+5*PacketSize]), ei_pload(&A[j+5*PacketSize+iN])),
// ei_padd(ei_pmul(ei_pload(&B[j+6*PacketSize]), ei_pload(&A[j+6*PacketSize+iN])),
// ei_padd(ei_pmul(ei_pload(&B[j+7*PacketSize]), ei_pload(&A[j+7*PacketSize+iN])),
// ptmp))))))));
// }
// for (int j = ANP;j<AN;j+=PacketSize)
// ptmp = ei_padd(ptmp, ei_pmul(ei_pload(&B[j]), ei_pload(&A[j+iN])));
// #else
// for (int j = 0;j<AN;j+=PacketSize)
// ptmp = ei_padd(ptmp, ei_pmul(ei_pload(&B[j]), ei_pload(&A[j+iN])));
// #endif
// }
// else
// {
// #ifdef PEELING
// int ANP = (AN/(8*PacketSize))*8*PacketSize;
// for (int j = 0;j<ANP;j+=PacketSize*8)
// {
// ptmp =
// ei_padd(ei_pmul(ei_pload(&B[j]), ei_ploadu(&A[j+iN])),
// ei_padd(ei_pmul(ei_pload(&B[j+PacketSize]), ei_ploadu(&A[j+PacketSize+iN])),
// ei_padd(ei_pmul(ei_pload(&B[j+2*PacketSize]), ei_ploadu(&A[j+2*PacketSize+iN])),
// ei_padd(ei_pmul(ei_pload(&B[j+3*PacketSize]), ei_ploadu(&A[j+3*PacketSize+iN])),
// ei_padd(ei_pmul(ei_pload(&B[j+4*PacketSize]), ei_ploadu(&A[j+4*PacketSize+iN])),
// ei_padd(ei_pmul(ei_pload(&B[j+5*PacketSize]), ei_ploadu(&A[j+5*PacketSize+iN])),
// ei_padd(ei_pmul(ei_pload(&B[j+6*PacketSize]), ei_ploadu(&A[j+6*PacketSize+iN])),
// ei_padd(ei_pmul(ei_pload(&B[j+7*PacketSize]), ei_ploadu(&A[j+7*PacketSize+iN])),
// ptmp))))))));
// }
// for (int j = ANP;j<AN;j+=PacketSize)
// ptmp = ei_padd(ptmp, ei_pmul(ei_pload(&B[j]), ei_ploadu(&A[j+iN])));
// #else
// for (int j = 0;j<AN;j+=PacketSize)
// ptmp = ei_padd(ptmp, ei_pmul(ei_pload(&B[j]), ei_ploadu(&A[j+iN])));
// #endif
// }
// tmp = ei_predux(ptmp);
// }
// // process remaining scalars
// for (int j=AN;j<N;j++)
// tmp += B[j] * A[j+iN];
// X[i] = tmp;
// }
// }
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
static inline void axpy(real coef, const gene_vector & X, gene_vector & Y, int N){
int AN = (N/PacketSize)*PacketSize;
if (AN>0)
{
Packet pcoef = ei_pset1(coef);
#ifdef PEELING
Add a LU decomposition action in BTL and various cleaning in BTL. For instance all per plot settings have been moved to a single file, go_mean now takes an optional second argument "tiny" to generate plots for tiny matrices, and output of comparison information wrt to previous benchs (if any).
2008-08-05 07:12:48 +08:00
const int peelSize = 3;
int ANP = (AN/(peelSize*PacketSize))*peelSize*PacketSize;
float* X1 = X + PacketSize;
float* Y1 = Y + PacketSize;
float* X2 = X + 2*PacketSize;
float* Y2 = Y + 2*PacketSize;
Packet x0,x1,x2,y0,y1,y2;
for (int j = 0;j<ANP;j+=PacketSize*peelSize)
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
{
Add a LU decomposition action in BTL and various cleaning in BTL. For instance all per plot settings have been moved to a single file, go_mean now takes an optional second argument "tiny" to generate plots for tiny matrices, and output of comparison information wrt to previous benchs (if any).
2008-08-05 07:12:48 +08:00
x0 = ei_pload(X+j);
x1 = ei_pload(X1+j);
x2 = ei_pload(X2+j);
y0 = ei_pload(Y+j);
y1 = ei_pload(Y1+j);
y2 = ei_pload(Y2+j);
y0 = ei_pmadd(pcoef, x0, y0);
y1 = ei_pmadd(pcoef, x1, y1);
y2 = ei_pmadd(pcoef, x2, y2);
ei_pstore(Y+j, y0);
ei_pstore(Y1+j, y1);
ei_pstore(Y2+j, y2);
// ei_pstore(&Y[j+2*PacketSize], ei_padd(ei_pload(&Y[j+2*PacketSize]), ei_pmul(pcoef,ei_pload(&X[j+2*PacketSize]))));
// ei_pstore(&Y[j+3*PacketSize], ei_padd(ei_pload(&Y[j+3*PacketSize]), ei_pmul(pcoef,ei_pload(&X[j+3*PacketSize]))));
// ei_pstore(&Y[j+4*PacketSize], ei_padd(ei_pload(&Y[j+4*PacketSize]), ei_pmul(pcoef,ei_pload(&X[j+4*PacketSize]))));
// ei_pstore(&Y[j+5*PacketSize], ei_padd(ei_pload(&Y[j+5*PacketSize]), ei_pmul(pcoef,ei_pload(&X[j+5*PacketSize]))));
// ei_pstore(&Y[j+6*PacketSize], ei_padd(ei_pload(&Y[j+6*PacketSize]), ei_pmul(pcoef,ei_pload(&X[j+6*PacketSize]))));
// ei_pstore(&Y[j+7*PacketSize], ei_padd(ei_pload(&Y[j+7*PacketSize]), ei_pmul(pcoef,ei_pload(&X[j+7*PacketSize]))));
resurrected tvmet, added mt4, intel's MKL and handcoded vectorized backends in the benchmark suite
2008-07-11 02:28:50 +08:00
}
for (int j = ANP;j<AN;j+=PacketSize)
ei_pstore(&Y[j], ei_padd(ei_pload(&Y[j]), ei_pmul(pcoef,ei_pload(&X[j]))));
#else
for (int j = 0;j<AN;j+=PacketSize)
ei_pstore(&Y[j], ei_padd(ei_pload(&Y[j]), ei_pmul(pcoef,ei_pload(&X[j]))));
#endif
}
// process remaining scalars
for (int i=AN;i<N;i++)
Y[i] += coef * X[i];
}
};
#endif
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