blob: fb53b85af02b782a41c8e5e2d08a856f370ff566 [file] [log] [blame]
#include <typeinfo>
#include <iostream>
#include <Eigen/Core>
#include "BenchTimer.h"
using namespace Eigen;
using namespace std;
template <typename T>
EIGEN_DONT_INLINE typename T::Scalar sqsumNorm(T& v) {
return v.norm();
}
template <typename T>
EIGEN_DONT_INLINE typename T::Scalar stableNorm(T& v) {
return v.stableNorm();
}
template <typename T>
EIGEN_DONT_INLINE typename T::Scalar hypotNorm(T& v) {
return v.hypotNorm();
}
template <typename T>
EIGEN_DONT_INLINE typename T::Scalar blueNorm(T& v) {
return v.blueNorm();
}
template <typename T>
EIGEN_DONT_INLINE typename T::Scalar lapackNorm(T& v) {
typedef typename T::Scalar Scalar;
int n = v.size();
Scalar scale = 0;
Scalar ssq = 1;
for (int i = 0; i < n; ++i) {
Scalar ax = std::abs(v.coeff(i));
if (scale >= ax) {
ssq += numext::abs2(ax / scale);
} else {
ssq = Scalar(1) + ssq * numext::abs2(scale / ax);
scale = ax;
}
}
return scale * std::sqrt(ssq);
}
template <typename T>
EIGEN_DONT_INLINE typename T::Scalar twopassNorm(T& v) {
typedef typename T::Scalar Scalar;
Scalar s = v.array().abs().maxCoeff();
return s * (v / s).norm();
}
template <typename T>
EIGEN_DONT_INLINE typename T::Scalar bl2passNorm(T& v) {
return v.stableNorm();
}
template <typename T>
EIGEN_DONT_INLINE typename T::Scalar divacNorm(T& v) {
int n = v.size() / 2;
for (int i = 0; i < n; ++i) v(i) = v(2 * i) * v(2 * i) + v(2 * i + 1) * v(2 * i + 1);
n = n / 2;
while (n > 0) {
for (int i = 0; i < n; ++i) v(i) = v(2 * i) + v(2 * i + 1);
n = n / 2;
}
return std::sqrt(v(0));
}
namespace Eigen {
namespace internal {
#ifdef EIGEN_VECTORIZE
Packet4f plt(const Packet4f& a, Packet4f& b) { return _mm_cmplt_ps(a, b); }
Packet2d plt(const Packet2d& a, Packet2d& b) { return _mm_cmplt_pd(a, b); }
Packet4f pandnot(const Packet4f& a, Packet4f& b) { return _mm_andnot_ps(a, b); }
Packet2d pandnot(const Packet2d& a, Packet2d& b) { return _mm_andnot_pd(a, b); }
#endif
} // namespace internal
} // namespace Eigen
template <typename T>
EIGEN_DONT_INLINE typename T::Scalar pblueNorm(const T& v) {
#ifndef EIGEN_VECTORIZE
return v.blueNorm();
#else
typedef typename T::Scalar Scalar;
static int nmax = 0;
static Scalar b1, b2, s1m, s2m, overfl, rbig, relerr;
int n;
if (nmax <= 0) {
int nbig, ibeta, it, iemin, iemax, iexp;
Scalar abig, eps;
nbig = NumTraits<int>::highest(); // largest integer
ibeta = std::numeric_limits<Scalar>::radix; // NumTraits<Scalar>::Base; // base for
// floating-point numbers
it = NumTraits<Scalar>::digits(); // NumTraits<Scalar>::Mantissa; // number of base-beta digits in
// mantissa
iemin = NumTraits<Scalar>::min_exponent(); // minimum exponent
iemax = NumTraits<Scalar>::max_exponent(); // maximum exponent
rbig = NumTraits<Scalar>::highest(); // largest floating-point number
// Check the basic machine-dependent constants.
if (iemin > 1 - 2 * it || 1 + it > iemax || (it == 2 && ibeta < 5) || (it <= 4 && ibeta <= 3) || it < 2) {
eigen_assert(false && "the algorithm cannot be guaranteed on this computer");
}
iexp = -((1 - iemin) / 2);
b1 = std::pow(ibeta, iexp); // lower boundary of midrange
iexp = (iemax + 1 - it) / 2;
b2 = std::pow(ibeta, iexp); // upper boundary of midrange
iexp = (2 - iemin) / 2;
s1m = std::pow(ibeta, iexp); // scaling factor for lower range
iexp = -((iemax + it) / 2);
s2m = std::pow(ibeta, iexp); // scaling factor for upper range
overfl = rbig * s2m; // overflow boundary for abig
eps = std::pow(ibeta, 1 - it);
relerr = std::sqrt(eps); // tolerance for neglecting asml
abig = 1.0 / eps - 1.0;
if (Scalar(nbig) > abig)
nmax = abig; // largest safe n
else
nmax = nbig;
}
typedef typename internal::packet_traits<Scalar>::type Packet;
const int ps = internal::packet_traits<Scalar>::size;
Packet pasml = internal::pset1<Packet>(Scalar(0));
Packet pamed = internal::pset1<Packet>(Scalar(0));
Packet pabig = internal::pset1<Packet>(Scalar(0));
Packet ps2m = internal::pset1<Packet>(s2m);
Packet ps1m = internal::pset1<Packet>(s1m);
Packet pb2 = internal::pset1<Packet>(b2);
Packet pb1 = internal::pset1<Packet>(b1);
for (int j = 0; j < v.size(); j += ps) {
Packet ax = internal::pabs(v.template packet<Aligned>(j));
Packet ax_s2m = internal::pmul(ax, ps2m);
Packet ax_s1m = internal::pmul(ax, ps1m);
Packet maskBig = internal::plt(pb2, ax);
Packet maskSml = internal::plt(ax, pb1);
// Packet maskMed = internal::pand(maskSml,maskBig);
// Packet scale = internal::pset1(Scalar(0));
// scale = internal::por(scale, internal::pand(maskBig,ps2m));
// scale = internal::por(scale, internal::pand(maskSml,ps1m));
// scale = internal::por(scale, internal::pandnot(internal::pset1(Scalar(1)),maskMed));
// ax = internal::pmul(ax,scale);
// ax = internal::pmul(ax,ax);
// pabig = internal::padd(pabig, internal::pand(maskBig, ax));
// pasml = internal::padd(pasml, internal::pand(maskSml, ax));
// pamed = internal::padd(pamed, internal::pandnot(ax,maskMed));
pabig = internal::padd(pabig, internal::pand(maskBig, internal::pmul(ax_s2m, ax_s2m)));
pasml = internal::padd(pasml, internal::pand(maskSml, internal::pmul(ax_s1m, ax_s1m)));
pamed = internal::padd(pamed, internal::pandnot(internal::pmul(ax, ax), internal::pand(maskSml, maskBig)));
}
Scalar abig = internal::predux(pabig);
Scalar asml = internal::predux(pasml);
Scalar amed = internal::predux(pamed);
if (abig > Scalar(0)) {
abig = std::sqrt(abig);
if (abig > overfl) {
eigen_assert(false && "overflow");
return rbig;
}
if (amed > Scalar(0)) {
abig = abig / s2m;
amed = std::sqrt(amed);
} else {
return abig / s2m;
}
} else if (asml > Scalar(0)) {
if (amed > Scalar(0)) {
abig = std::sqrt(amed);
amed = std::sqrt(asml) / s1m;
} else {
return std::sqrt(asml) / s1m;
}
} else {
return std::sqrt(amed);
}
asml = std::min(abig, amed);
abig = std::max(abig, amed);
if (asml <= abig * relerr)
return abig;
else
return abig * std::sqrt(Scalar(1) + numext::abs2(asml / abig));
#endif
}
#define BENCH_PERF(NRM) \
{ \
float af = 0; \
double ad = 0; \
std::complex<float> ac = 0; \
Eigen::BenchTimer tf, td, tcf; \
tf.reset(); \
td.reset(); \
tcf.reset(); \
for (int k = 0; k < tries; ++k) { \
tf.start(); \
for (int i = 0; i < iters; ++i) { \
af += NRM(vf); \
} \
tf.stop(); \
} \
for (int k = 0; k < tries; ++k) { \
td.start(); \
for (int i = 0; i < iters; ++i) { \
ad += NRM(vd); \
} \
td.stop(); \
} \
/*for (int k=0; k<std::max(1,tries/3); ++k) { \
tcf.start(); \
for (int i=0; i<iters; ++i) { ac += NRM(vcf); } \
tcf.stop(); \
} */ \
std::cout << #NRM << "\t" << tf.value() << " " << td.value() << " " << tcf.value() << "\n"; \
}
void check_accuracy(double basef, double based, int s) {
double yf = basef * std::abs(internal::random<double>());
double yd = based * std::abs(internal::random<double>());
VectorXf vf = VectorXf::Ones(s) * yf;
VectorXd vd = VectorXd::Ones(s) * yd;
std::cout << "reference\t" << std::sqrt(double(s)) * yf << "\t" << std::sqrt(double(s)) * yd << "\n";
std::cout << "sqsumNorm\t" << sqsumNorm(vf) << "\t" << sqsumNorm(vd) << "\n";
std::cout << "hypotNorm\t" << hypotNorm(vf) << "\t" << hypotNorm(vd) << "\n";
std::cout << "blueNorm\t" << blueNorm(vf) << "\t" << blueNorm(vd) << "\n";
std::cout << "pblueNorm\t" << pblueNorm(vf) << "\t" << pblueNorm(vd) << "\n";
std::cout << "lapackNorm\t" << lapackNorm(vf) << "\t" << lapackNorm(vd) << "\n";
std::cout << "twopassNorm\t" << twopassNorm(vf) << "\t" << twopassNorm(vd) << "\n";
std::cout << "bl2passNorm\t" << bl2passNorm(vf) << "\t" << bl2passNorm(vd) << "\n";
}
void check_accuracy_var(int ef0, int ef1, int ed0, int ed1, int s) {
VectorXf vf(s);
VectorXd vd(s);
for (int i = 0; i < s; ++i) {
vf[i] = std::abs(internal::random<double>()) * std::pow(double(10), internal::random<int>(ef0, ef1));
vd[i] = std::abs(internal::random<double>()) * std::pow(double(10), internal::random<int>(ed0, ed1));
}
// std::cout << "reference\t" << internal::sqrt(double(s))*yf << "\t" << internal::sqrt(double(s))*yd << "\n";
std::cout << "sqsumNorm\t" << sqsumNorm(vf) << "\t" << sqsumNorm(vd) << "\t" << sqsumNorm(vf.cast<long double>())
<< "\t" << sqsumNorm(vd.cast<long double>()) << "\n";
std::cout << "hypotNorm\t" << hypotNorm(vf) << "\t" << hypotNorm(vd) << "\t" << hypotNorm(vf.cast<long double>())
<< "\t" << hypotNorm(vd.cast<long double>()) << "\n";
std::cout << "blueNorm\t" << blueNorm(vf) << "\t" << blueNorm(vd) << "\t" << blueNorm(vf.cast<long double>()) << "\t"
<< blueNorm(vd.cast<long double>()) << "\n";
std::cout << "pblueNorm\t" << pblueNorm(vf) << "\t" << pblueNorm(vd) << "\t" << blueNorm(vf.cast<long double>())
<< "\t" << blueNorm(vd.cast<long double>()) << "\n";
std::cout << "lapackNorm\t" << lapackNorm(vf) << "\t" << lapackNorm(vd) << "\t" << lapackNorm(vf.cast<long double>())
<< "\t" << lapackNorm(vd.cast<long double>()) << "\n";
std::cout << "twopassNorm\t" << twopassNorm(vf) << "\t" << twopassNorm(vd) << "\t"
<< twopassNorm(vf.cast<long double>()) << "\t" << twopassNorm(vd.cast<long double>()) << "\n";
// std::cout << "bl2passNorm\t" << bl2passNorm(vf) << "\t" << bl2passNorm(vd) << "\t" << bl2passNorm(vf.cast<long
// double>()) << "\t" << bl2passNorm(vd.cast<long double>()) << "\n";
}
int main(int argc, char** argv) {
int tries = 10;
int iters = 100000;
double y = 1.1345743233455785456788e12 * internal::random<double>();
VectorXf v = VectorXf::Ones(1024) * y;
// return 0;
int s = 10000;
double basef_ok = 1.1345743233455785456788e15;
double based_ok = 1.1345743233455785456788e95;
double basef_under = 1.1345743233455785456788e-27;
double based_under = 1.1345743233455785456788e-303;
double basef_over = 1.1345743233455785456788e+27;
double based_over = 1.1345743233455785456788e+302;
std::cout.precision(20);
std::cerr << "\nNo under/overflow:\n";
check_accuracy(basef_ok, based_ok, s);
std::cerr << "\nUnderflow:\n";
check_accuracy(basef_under, based_under, s);
std::cerr << "\nOverflow:\n";
check_accuracy(basef_over, based_over, s);
std::cerr << "\nVarying (over):\n";
for (int k = 0; k < 1; ++k) {
check_accuracy_var(20, 27, 190, 302, s);
std::cout << "\n";
}
std::cerr << "\nVarying (under):\n";
for (int k = 0; k < 1; ++k) {
check_accuracy_var(-27, 20, -302, -190, s);
std::cout << "\n";
}
y = 1;
std::cout.precision(4);
int s1 = 1024 * 1024 * 32;
std::cerr << "Performance (out of cache, " << s1 << "):\n";
{
int iters = 1;
VectorXf vf = VectorXf::Random(s1) * y;
VectorXd vd = VectorXd::Random(s1) * y;
VectorXcf vcf = VectorXcf::Random(s1) * y;
BENCH_PERF(sqsumNorm);
BENCH_PERF(stableNorm);
BENCH_PERF(blueNorm);
BENCH_PERF(pblueNorm);
BENCH_PERF(lapackNorm);
BENCH_PERF(hypotNorm);
BENCH_PERF(twopassNorm);
BENCH_PERF(bl2passNorm);
}
std::cerr << "\nPerformance (in cache, " << 512 << "):\n";
{
int iters = 100000;
VectorXf vf = VectorXf::Random(512) * y;
VectorXd vd = VectorXd::Random(512) * y;
VectorXcf vcf = VectorXcf::Random(512) * y;
BENCH_PERF(sqsumNorm);
BENCH_PERF(stableNorm);
BENCH_PERF(blueNorm);
BENCH_PERF(pblueNorm);
BENCH_PERF(lapackNorm);
BENCH_PERF(hypotNorm);
BENCH_PERF(twopassNorm);
BENCH_PERF(bl2passNorm);
}
}