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https://gitlab.com/libeigen/eigen.git
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136 lines
4.7 KiB
C++
136 lines
4.7 KiB
C++
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#include <iostream>
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#include <Eigen/Core>
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#include <bench/BenchTimer.h>
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using namespace Eigen;
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#ifndef SIZE
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#define SIZE 50
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#endif
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#ifndef REPEAT
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#define REPEAT 10000
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#endif
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typedef float Scalar;
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__attribute__ ((noinline)) void benchVec(Scalar* a, Scalar* b, Scalar* c, int size);
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__attribute__ ((noinline)) void benchVec(MatrixXf& a, MatrixXf& b, MatrixXf& c);
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__attribute__ ((noinline)) void benchVec(VectorXf& a, VectorXf& b, VectorXf& c);
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int main(int argc, char* argv[])
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{
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int size = SIZE * 8;
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int size2 = size * size;
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Scalar* a = ei_aligned_new<Scalar>(size2);
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Scalar* b = ei_aligned_new<Scalar>(size2+4)+1;
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Scalar* c = ei_aligned_new<Scalar>(size2);
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for (int i=0; i<size; ++i)
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{
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a[i] = b[i] = c[i] = 0;
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}
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BenchTimer timer;
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timer.reset();
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for (int k=0; k<10; ++k)
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{
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timer.start();
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benchVec(a, b, c, size2);
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timer.stop();
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}
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std::cout << timer.value() << "s " << (double(size2*REPEAT)/timer.value())/(1024.*1024.*1024.) << " GFlops\n";
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return 0;
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for (int innersize = size; innersize>2 ; --innersize)
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{
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if (size2%innersize==0)
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{
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int outersize = size2/innersize;
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MatrixXf ma = Map<MatrixXf>(a, innersize, outersize );
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MatrixXf mb = Map<MatrixXf>(b, innersize, outersize );
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MatrixXf mc = Map<MatrixXf>(c, innersize, outersize );
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timer.reset();
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for (int k=0; k<3; ++k)
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{
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timer.start();
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benchVec(ma, mb, mc);
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timer.stop();
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}
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std::cout << innersize << " x " << outersize << " " << timer.value() << "s " << (double(size2*REPEAT)/timer.value())/(1024.*1024.*1024.) << " GFlops\n";
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}
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}
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VectorXf va = Map<VectorXf>(a, size2);
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VectorXf vb = Map<VectorXf>(b, size2);
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VectorXf vc = Map<VectorXf>(c, size2);
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timer.reset();
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for (int k=0; k<3; ++k)
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{
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timer.start();
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benchVec(va, vb, vc);
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timer.stop();
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}
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std::cout << timer.value() << "s " << (double(size2*REPEAT)/timer.value())/(1024.*1024.*1024.) << " GFlops\n";
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return 0;
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}
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void benchVec(MatrixXf& a, MatrixXf& b, MatrixXf& c)
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{
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for (int k=0; k<REPEAT; ++k)
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a = a + b;
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}
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void benchVec(VectorXf& a, VectorXf& b, VectorXf& c)
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{
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for (int k=0; k<REPEAT; ++k)
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a = a + b;
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}
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void benchVec(Scalar* a, Scalar* b, Scalar* c, int size)
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{
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typedef ei_packet_traits<Scalar>::type PacketScalar;
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const int PacketSize = ei_packet_traits<Scalar>::size;
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PacketScalar a0, a1, a2, a3, b0, b1, b2, b3;
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for (int k=0; k<REPEAT; ++k)
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for (int i=0; i<size; i+=PacketSize*8)
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{
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// a0 = ei_pload(&a[i]);
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// b0 = ei_pload(&b[i]);
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// a1 = ei_pload(&a[i+1*PacketSize]);
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// b1 = ei_pload(&b[i+1*PacketSize]);
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// a2 = ei_pload(&a[i+2*PacketSize]);
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// b2 = ei_pload(&b[i+2*PacketSize]);
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// a3 = ei_pload(&a[i+3*PacketSize]);
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// b3 = ei_pload(&b[i+3*PacketSize]);
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// ei_pstore(&a[i], ei_padd(a0, b0));
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// a0 = ei_pload(&a[i+4*PacketSize]);
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// b0 = ei_pload(&b[i+4*PacketSize]);
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//
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// ei_pstore(&a[i+1*PacketSize], ei_padd(a1, b1));
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// a1 = ei_pload(&a[i+5*PacketSize]);
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// b1 = ei_pload(&b[i+5*PacketSize]);
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//
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// ei_pstore(&a[i+2*PacketSize], ei_padd(a2, b2));
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// a2 = ei_pload(&a[i+6*PacketSize]);
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// b2 = ei_pload(&b[i+6*PacketSize]);
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//
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// ei_pstore(&a[i+3*PacketSize], ei_padd(a3, b3));
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// a3 = ei_pload(&a[i+7*PacketSize]);
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// b3 = ei_pload(&b[i+7*PacketSize]);
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//
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// ei_pstore(&a[i+4*PacketSize], ei_padd(a0, b0));
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// ei_pstore(&a[i+5*PacketSize], ei_padd(a1, b1));
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// ei_pstore(&a[i+6*PacketSize], ei_padd(a2, b2));
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// ei_pstore(&a[i+7*PacketSize], ei_padd(a3, b3));
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ei_pstore(&a[i+2*PacketSize], ei_padd(ei_ploadu(&a[i+2*PacketSize]), ei_ploadu(&b[i+2*PacketSize])));
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ei_pstore(&a[i+3*PacketSize], ei_padd(ei_ploadu(&a[i+3*PacketSize]), ei_ploadu(&b[i+3*PacketSize])));
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ei_pstore(&a[i+4*PacketSize], ei_padd(ei_ploadu(&a[i+4*PacketSize]), ei_ploadu(&b[i+4*PacketSize])));
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ei_pstore(&a[i+5*PacketSize], ei_padd(ei_ploadu(&a[i+5*PacketSize]), ei_ploadu(&b[i+5*PacketSize])));
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ei_pstore(&a[i+6*PacketSize], ei_padd(ei_ploadu(&a[i+6*PacketSize]), ei_ploadu(&b[i+6*PacketSize])));
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ei_pstore(&a[i+7*PacketSize], ei_padd(ei_ploadu(&a[i+7*PacketSize]), ei_ploadu(&b[i+7*PacketSize])));
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}
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}
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