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add bench_reverse, draft of a reverse vectorization for AltiVec, make
global Scaling function static
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@ -111,7 +111,7 @@ template<> inline v4i ei_pmul(const v4i& a, const v4i& b)
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USE_CONST_v1i;
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USE_CONST_v16i_;
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// Get the absolute values
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// Get the absolute values
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a1 = vec_abs(a);
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b1 = vec_abs(b);
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@ -146,7 +146,7 @@ template<> inline v4f ei_pdiv(const v4f& a, const v4f& b) {
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// Altivec does not offer a divide instruction, we have to do a reciprocal approximation
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y_0 = vec_re(b);
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// Do one Newton-Raphson iteration to get the needed accuracy
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t = vec_nmsub(y_0, b, v1f);
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y_1 = vec_madd(y_0, t, y_0);
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@ -260,6 +260,17 @@ template<> inline int ei_pfirst(const v4i& a)
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return ai[0];
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}
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template<> EIGEN_STRONG_INLINE v4f ei_preverse(const v4f& a)
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{
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static const unsigned char __attribute__(aligned(16)) reverse_mask = {12,13,14,15, 8,9,10,11, 4,5,6,7, 0,1,2,3};
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return (v4f)vec_perm((__vector unsigned char)a,(__vector unsigned char)a,reverse_mask);
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}
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template<> EIGEN_STRONG_INLINE v4i ei_preverse(const v4i& a)
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{
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static const unsigned char __attribute__(aligned(16)) reverse_mask = {12,13,14,15, 8,9,10,11, 4,5,6,7, 0,1,2,3};
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return (v4i)vec_perm((__vector unsigned char)a,(__vector unsigned char)a,reverse_mask);
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}
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inline v4f ei_preduxp(const v4f* vecs)
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{
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v4f v[4], sum[4];
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@ -120,28 +120,28 @@ public:
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};
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/** Constructs a uniform scaling from scale factor \a s */
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inline UniformScaling<float> Scaling(float s) { return UniformScaling<float>(s); }
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static inline UniformScaling<float> Scaling(float s) { return UniformScaling<float>(s); }
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/** Constructs a uniform scaling from scale factor \a s */
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inline UniformScaling<double> Scaling(double s) { return UniformScaling<double>(s); }
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static inline UniformScaling<double> Scaling(double s) { return UniformScaling<double>(s); }
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/** Constructs a uniform scaling from scale factor \a s */
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template<typename RealScalar> inline UniformScaling<std::complex<RealScalar> >
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Scaling(const std::complex<RealScalar>& s)
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template<typename RealScalar>
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static inline UniformScaling<std::complex<RealScalar> > Scaling(const std::complex<RealScalar>& s)
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{ return UniformScaling<std::complex<RealScalar> >(s); }
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/** Constructs a 2D axis aligned scaling */
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template<typename Scalar> inline DiagonalMatrix<Scalar,2>
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Scaling(Scalar sx, Scalar sy)
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template<typename Scalar>
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static inline DiagonalMatrix<Scalar,2> Scaling(Scalar sx, Scalar sy)
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{ return DiagonalMatrix<Scalar,2>(sx, sy); }
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/** Constructs a 3D axis aligned scaling */
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template<typename Scalar> inline DiagonalMatrix<Scalar,3>
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Scaling(Scalar sx, Scalar sy, Scalar sz)
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template<typename Scalar>
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static inline DiagonalMatrix<Scalar,3> Scaling(Scalar sx, Scalar sy, Scalar sz)
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{ return DiagonalMatrix<Scalar,3>(sx, sy, sz); }
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/** Constructs an axis aligned scaling expression from vector expression \a coeffs
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* This is an alias for coeffs.asDiagonal()
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*/
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template<typename Derived>
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inline const DiagonalMatrixWrapper<Derived> Scaling(const MatrixBase<Derived>& coeffs)
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static inline const DiagonalMatrixWrapper<Derived> Scaling(const MatrixBase<Derived>& coeffs)
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{ return coeffs.asDiagonal(); }
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/** \addtogroup Geometry_Module */
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83
bench/bench_reverse.cpp
Normal file
83
bench/bench_reverse.cpp
Normal file
@ -0,0 +1,83 @@
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#include <Eigen/Array>
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#include <bench/BenchUtil.h>
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using namespace Eigen;
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#ifndef REPEAT
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#define REPEAT 100000
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#endif
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#ifndef TRIES
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#define TRIES 20
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#endif
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typedef double Scalar;
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template <typename MatrixType>
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__attribute__ ((noinline)) void bench_reverse(const MatrixType& m)
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{
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int rows = m.rows();
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int cols = m.cols();
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int size = m.size();
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int repeats = (REPEAT*1000)/size;
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MatrixType a = MatrixType::Random(rows,cols);
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MatrixType b = MatrixType::Random(rows,cols);
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BenchTimer timerB, timerH, timerV;
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Scalar acc = 0;
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int r = ei_random<int>(0,rows-1);
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int c = ei_random<int>(0,cols-1);
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for (int t=0; t<TRIES; ++t)
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{
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timerB.start();
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for (int k=0; k<repeats; ++k)
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{
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asm("#begin foo");
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b = a.reverse();
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asm("#end foo");
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acc += b.coeff(r,c);
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}
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timerB.stop();
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}
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if (MatrixType::RowsAtCompileTime==Dynamic)
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std::cout << "dyn ";
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else
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std::cout << "fixed ";
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std::cout << rows << " x " << cols << " \t"
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<< (timerB.value() * REPEAT) / repeats << "s "
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<< "(" << 1e-6 * size*repeats/timerB.value() << " MFLOPS)\t";
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std::cout << "\n";
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// make sure the compiler does not optimize too much
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if (acc==123)
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std::cout << acc;
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}
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int main(int argc, char* argv[])
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{
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const int dynsizes[] = {4,6,8,16,24,32,49,64,128,256,512,900,0};
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std::cout << "size no sqrt standard";
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// #ifdef BENCH_GSL
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// std::cout << " GSL (standard + double + ATLAS) ";
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// #endif
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std::cout << "\n";
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for (uint i=0; dynsizes[i]>0; ++i)
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{
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bench_reverse(Matrix<Scalar,Dynamic,Dynamic>(dynsizes[i],dynsizes[i]));
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bench_reverse(Matrix<Scalar,Dynamic,1>(dynsizes[i]*dynsizes[i]));
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}
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// bench_reverse(Matrix<Scalar,2,2>());
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// bench_reverse(Matrix<Scalar,3,3>());
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// bench_reverse(Matrix<Scalar,4,4>());
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// bench_reverse(Matrix<Scalar,5,5>());
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// bench_reverse(Matrix<Scalar,6,6>());
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// bench_reverse(Matrix<Scalar,7,7>());
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// bench_reverse(Matrix<Scalar,8,8>());
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// bench_reverse(Matrix<Scalar,12,12>());
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// bench_reverse(Matrix<Scalar,16,16>());
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return 0;
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}
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