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benchVecAdd.cpp
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Thu, Feb 20, 23:05
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Sat, Feb 22, 23:05 (2 d)
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rDLMA Diffusion limited mixed aggregation
benchVecAdd.cpp
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#include <iostream>
#include <Eigen/Core>
#include <bench/BenchTimer.h>
using namespace Eigen;
#ifndef SIZE
#define SIZE 50
#endif
#ifndef REPEAT
#define REPEAT 10000
#endif
typedef float Scalar;
__attribute__ ((noinline)) void benchVec(Scalar* a, Scalar* b, Scalar* c, int size);
__attribute__ ((noinline)) void benchVec(MatrixXf& a, MatrixXf& b, MatrixXf& c);
__attribute__ ((noinline)) void benchVec(VectorXf& a, VectorXf& b, VectorXf& c);
int main(int argc, char* argv[])
{
int size = SIZE * 8;
int size2 = size * size;
Scalar* a = internal::aligned_new<Scalar>(size2);
Scalar* b = internal::aligned_new<Scalar>(size2+4)+1;
Scalar* c = internal::aligned_new<Scalar>(size2);
for (int i=0; i<size; ++i)
{
a[i] = b[i] = c[i] = 0;
}
BenchTimer timer;
timer.reset();
for (int k=0; k<10; ++k)
{
timer.start();
benchVec(a, b, c, size2);
timer.stop();
}
std::cout << timer.value() << "s " << (double(size2*REPEAT)/timer.value())/(1024.*1024.*1024.) << " GFlops\n";
return 0;
for (int innersize = size; innersize>2 ; --innersize)
{
if (size2%innersize==0)
{
int outersize = size2/innersize;
MatrixXf ma = Map<MatrixXf>(a, innersize, outersize );
MatrixXf mb = Map<MatrixXf>(b, innersize, outersize );
MatrixXf mc = Map<MatrixXf>(c, innersize, outersize );
timer.reset();
for (int k=0; k<3; ++k)
{
timer.start();
benchVec(ma, mb, mc);
timer.stop();
}
std::cout << innersize << " x " << outersize << " " << timer.value() << "s " << (double(size2*REPEAT)/timer.value())/(1024.*1024.*1024.) << " GFlops\n";
}
}
VectorXf va = Map<VectorXf>(a, size2);
VectorXf vb = Map<VectorXf>(b, size2);
VectorXf vc = Map<VectorXf>(c, size2);
timer.reset();
for (int k=0; k<3; ++k)
{
timer.start();
benchVec(va, vb, vc);
timer.stop();
}
std::cout << timer.value() << "s " << (double(size2*REPEAT)/timer.value())/(1024.*1024.*1024.) << " GFlops\n";
return 0;
}
void benchVec(MatrixXf& a, MatrixXf& b, MatrixXf& c)
{
for (int k=0; k<REPEAT; ++k)
a = a + b;
}
void benchVec(VectorXf& a, VectorXf& b, VectorXf& c)
{
for (int k=0; k<REPEAT; ++k)
a = a + b;
}
void benchVec(Scalar* a, Scalar* b, Scalar* c, int size)
{
typedef internal::packet_traits<Scalar>::type PacketScalar;
const int PacketSize = internal::packet_traits<Scalar>::size;
PacketScalar a0, a1, a2, a3, b0, b1, b2, b3;
for (int k=0; k<REPEAT; ++k)
for (int i=0; i<size; i+=PacketSize*8)
{
// a0 = internal::pload(&a[i]);
// b0 = internal::pload(&b[i]);
// a1 = internal::pload(&a[i+1*PacketSize]);
// b1 = internal::pload(&b[i+1*PacketSize]);
// a2 = internal::pload(&a[i+2*PacketSize]);
// b2 = internal::pload(&b[i+2*PacketSize]);
// a3 = internal::pload(&a[i+3*PacketSize]);
// b3 = internal::pload(&b[i+3*PacketSize]);
// internal::pstore(&a[i], internal::padd(a0, b0));
// a0 = internal::pload(&a[i+4*PacketSize]);
// b0 = internal::pload(&b[i+4*PacketSize]);
//
// internal::pstore(&a[i+1*PacketSize], internal::padd(a1, b1));
// a1 = internal::pload(&a[i+5*PacketSize]);
// b1 = internal::pload(&b[i+5*PacketSize]);
//
// internal::pstore(&a[i+2*PacketSize], internal::padd(a2, b2));
// a2 = internal::pload(&a[i+6*PacketSize]);
// b2 = internal::pload(&b[i+6*PacketSize]);
//
// internal::pstore(&a[i+3*PacketSize], internal::padd(a3, b3));
// a3 = internal::pload(&a[i+7*PacketSize]);
// b3 = internal::pload(&b[i+7*PacketSize]);
//
// internal::pstore(&a[i+4*PacketSize], internal::padd(a0, b0));
// internal::pstore(&a[i+5*PacketSize], internal::padd(a1, b1));
// internal::pstore(&a[i+6*PacketSize], internal::padd(a2, b2));
// internal::pstore(&a[i+7*PacketSize], internal::padd(a3, b3));
internal::pstore(&a[i+2*PacketSize], internal::padd(internal::ploadu(&a[i+2*PacketSize]), internal::ploadu(&b[i+2*PacketSize])));
internal::pstore(&a[i+3*PacketSize], internal::padd(internal::ploadu(&a[i+3*PacketSize]), internal::ploadu(&b[i+3*PacketSize])));
internal::pstore(&a[i+4*PacketSize], internal::padd(internal::ploadu(&a[i+4*PacketSize]), internal::ploadu(&b[i+4*PacketSize])));
internal::pstore(&a[i+5*PacketSize], internal::padd(internal::ploadu(&a[i+5*PacketSize]), internal::ploadu(&b[i+5*PacketSize])));
internal::pstore(&a[i+6*PacketSize], internal::padd(internal::ploadu(&a[i+6*PacketSize]), internal::ploadu(&b[i+6*PacketSize])));
internal::pstore(&a[i+7*PacketSize], internal::padd(internal::ploadu(&a[i+7*PacketSize]), internal::ploadu(&b[i+7*PacketSize])));
}
}
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