Improved performance of updateAb in NoiseModel with GSL/ATLAS. Various other small optimizations were made.
Alex Cunningham
parent
2183e09c67
commit
ac746ccead
124
cpp/Matrix.cpp
124
cpp/Matrix.cpp
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@ -171,16 +171,19 @@ Vector column_(const Matrix& A, size_t j) {
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}
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/* ************************************************************************* */
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Vector row(const Matrix& A, size_t i) {
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Vector row_(const Matrix& A, size_t i) {
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if (i>=A.size1())
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throw invalid_argument("Row index out of bounds!");
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const double * Aptr = A.data().begin() + A.size2() * i;
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return Vector_(A.size2(), Aptr);
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// TODO: improve this
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size_t n = A.size2();
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Vector a(n);
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for (size_t j=0; j<n; ++j)
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a(j) = A(i,j);
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return a;
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// size_t n = A.size2();
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// Vector a(n);
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// for (size_t j=0; j<n; ++j)
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// a(j) = A(i,j);
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// return a;
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}
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/* ************************************************************************* */
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@ -322,8 +325,30 @@ void householder_update(Matrix &A, int j, double beta, const Vector& vjm) {
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// __attribute__ ((noinline)) // uncomment to prevent inlining when profiling
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static void updateAb(Matrix& A, Vector& b, int j, const Vector& a,
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const Vector& r, double d) {
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// TODO: do some optimization here if possible
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const size_t m = A.size1(), n = A.size2();
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#ifdef GSL
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// update A
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// A(0:m,j+1:end) = A(0:m,j+1:end) - a(0:m)*r(j+1:end)'
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// get a view for A
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// gsl_matrix_view Ag = gsl_matrix_view_array(A.data().begin(), m, n);
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// gsl_matrix_view Ag_view = gsl_matrix_submatrix (&(Ag.matrix), 0, j+1, m, n-j-1);
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// // get a view for r
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// gsl_vector_const_view rg = gsl_vector_const_view_array(r.data().begin()+j+1, n-j-1);
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// // get a view for a
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// gsl_vector_const_view ag = gsl_vector_const_view_array(a.data().begin(), m);
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//
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// // rank one update
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// gsl_blas_dger (-1.0, &(ag.vector), &(rg.vector), &(Ag_view.matrix));
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//
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// // update b
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// double * bptr = b.data().begin();
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// const double * aptr = a.data().begin();
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// for (size_t i = 0; i < m; i++) {
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// *(bptr+i) -= d* *(aptr+i);
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// }
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// original
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for (size_t i = 0; i < m; i++) { // update all rows
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double ai = a(i);
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b(i) -= ai * d;
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@ -333,6 +358,18 @@ static void updateAb(Matrix& A, Vector& b, int j, const Vector& a,
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for (size_t j2 = j + 1; j2 < n; j2++, Aij++, rptr++)
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*Aij -= ai * (*rptr);
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}
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#else
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for (size_t i = 0; i < m; i++) { // update all rows
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double ai = a(i);
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b(i) -= ai * d;
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double *Aij = A.data().begin() + i * n + j + 1;
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const double *rptr = r.data().begin() + j + 1;
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// A(i,j+1:end) -= ai*r(j+1:end)
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for (size_t j2 = j + 1; j2 < n; j2++, Aij++, rptr++)
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*Aij -= ai * (*rptr);
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}
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#endif
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}
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/* ************************************************************************* */
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@ -396,7 +433,54 @@ void householder_(Matrix &A, size_t k)
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{
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const size_t m = A.size1(), n = A.size2(), kprime = min(k,min(m,n));
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// Original version
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#ifdef GSL
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// loop over the kprime first columns
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for(size_t j=0; j < kprime; j++){
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// below, the indices r,c always refer to original A
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// copy column from matrix to xjm, i.e. x(j:m) = A(j:m,j)
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Vector xjm(m-j);
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for(size_t r = j ; r < m; r++)
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xjm(r-j) = A(r,j);
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// calculate the Householder vector
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// COPIED IN: boost::tie(beta,vjm) = house(xjm);
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const double x0 = xjm(0);
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const double x02 = x0*x0;
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const double sigma = inner_prod(trans(xjm),xjm) - x02;
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double beta = 0.0; Vector vjm(xjm); vjm(0) = 1.0;
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if( sigma == 0.0 )
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beta = 0.0;
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else {
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double mu = sqrt(x02 + sigma);
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if( x0 <= 0.0 )
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vjm(0) = x0 - mu;
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else
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vjm(0) = -sigma / (x0 + mu);
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const double v02 = vjm(0)*vjm(0);
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beta = 2.0 * v02 / (sigma + v02);
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vjm = vjm / vjm(0);
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}
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// do outer product update A = (I-beta vv')*A = A - v*(beta*A'*v)' = A - v*w'
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//householder_update(A, j, beta, vjm);
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// inlined use GSL version
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gsl_vector_const_view v = gsl_vector_const_view_array(vjm.data().begin(), m-j);
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gsl_matrix_view Ag = gsl_matrix_view_array(A.data().begin(), m, n);
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gsl_matrix_view Ag_view = gsl_matrix_submatrix (&(Ag.matrix), j, 0, m-j, n);
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gsl_linalg_householder_hm (beta, &(v.vector), &(Ag_view.matrix));
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// the Householder vector is copied in the zeroed out part
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for( size_t r = j+1 ; r < m ; r++ )
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A(r,j) = vjm(r-j);
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} // column j
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#else
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// loop over the kprime first columns
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for(size_t j=0; j < kprime; j++){
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// below, the indices r,c always refer to original A
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@ -411,13 +495,14 @@ void householder_(Matrix &A, size_t k)
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boost::tie(beta,vjm) = house(xjm);
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// do outer product update A = (I-beta vv')*A = A - v*(beta*A'*v)' = A - v*w'
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householder_update(A, j, beta, vjm) ;
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householder_update(A, j, beta, vjm);
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// the Householder vector is copied in the zeroed out part
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for( size_t r = j+1 ; r < m ; r++ )
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A(r,j) = vjm(r-j);
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} // column j
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#endif
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}
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/* ************************************************************************* */
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@ -530,27 +615,6 @@ Matrix collect(const std::vector<const Matrix *>& matrices, size_t m, size_t n)
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dimA2 += M->size2();
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}
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// original version
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// Matrix A(dimA1, dimA2);
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// size_t hindex = 0;
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// BOOST_FOREACH(const Matrix* M, matrices) {
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// for(size_t d1 = 0; d1 < M->size1(); d1++)
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// for(size_t d2 = 0; d2 < M->size2(); d2++)
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// A(d1, d2+hindex) = (*M)(d1, d2);
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// hindex += M->size2();
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// }
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// matrix_range version
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// Result: slower
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// Matrix A(dimA1, dimA2);
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// size_t hindex = 0;
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// BOOST_FOREACH(const Matrix* M, matrices) {
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// ublas::matrix_range<Matrix> mr(A, ublas::range(0, dimA1),
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// ublas::range(hindex, hindex+M->size2()));
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// noalias(mr) = *M;
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// hindex += M->size2();
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// }
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// memcpy version
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Matrix A(dimA1, dimA2);
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double * Aptr = A.data().begin();
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@ -157,7 +157,7 @@ Vector column_(const Matrix& A, size_t j);
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* @param index of the row
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* @return the row in vector form
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*/
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Vector row(const Matrix& A, size_t j);
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Vector row_(const Matrix& A, size_t j);
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// left multiply with scalar
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//inline Matrix operator*(double s, const Matrix& A) { return A*s;}
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@ -11,6 +11,11 @@
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#include <typeinfo>
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#include <stdexcept>
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#ifdef GSL
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#include <gsl/gsl_blas.h> // needed for gsl blas
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#include <gsl/gsl_linalg.h>
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#endif
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#include <boost/numeric/ublas/lu.hpp>
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#include <boost/numeric/ublas/io.hpp>
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#include <boost/foreach.hpp>
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@ -37,6 +42,23 @@ namespace gtsam {
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// __attribute__ ((noinline)) // uncomment to prevent inlining when profiling
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static void updateAb(Matrix& Ab, int j, const Vector& a, const Vector& rd) {
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size_t m = Ab.size1(), n = Ab.size2()-1;
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#ifdef GSL
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// Ab(0:m,j+1:n) = Ab(0:m,j+1:n) - a(0:m)*rd(j+1:end)'
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// get a view for Ab
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gsl_matrix_view Abg = gsl_matrix_view_array(Ab.data().begin(), m, n+1);
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gsl_matrix_view Abg_view = gsl_matrix_submatrix (&(Abg.matrix), 0, j+1, m, n-j);
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// get a view for a
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gsl_vector_const_view ag = gsl_vector_const_view_array(a.data().begin(), m);
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// get a view for r
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gsl_vector_const_view rdg = gsl_vector_const_view_array(rd.data().begin()+j+1, n-j);
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// rank one update
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gsl_blas_dger (-1.0, &(ag.vector), &(rdg.vector), &(Abg_view.matrix));
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#else
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for (int i = 0; i < m; i++) { // update all rows
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double ai = a(i);
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double *Aij = Ab.data().begin() + i * (n+1) + j + 1;
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@ -45,6 +67,7 @@ namespace gtsam {
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for (int j2 = j + 1; j2 < n+1; j2++, Aij++, rptr++)
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*Aij -= ai * (*rptr);
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}
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#endif
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}
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/* ************************************************************************* */
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@ -18,6 +18,11 @@
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#include <Windows.h>
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#endif
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#ifdef GSL
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#include <gsl/gsl_blas.h> // needed for gsl blas
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#include <gsl/gsl_linalg.h>
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#endif
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#include <boost/random/normal_distribution.hpp>
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#include <boost/random/variate_generator.hpp>
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@ -232,9 +237,11 @@ namespace gtsam {
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}
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/* ************************************************************************* */
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pair<double, Vector > house(Vector &x)
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{
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const double x02 = x(0)*x(0);
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pair<double, Vector > house(const Vector &x) {
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const double x0 = x(0);
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const double x02 = x0*x0;
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// old code - GSL verison was actually a bit slower
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const double sigma = inner_prod(trans(x),x) - x02;
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double beta = 0.0;
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@ -244,16 +251,15 @@ namespace gtsam {
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beta = 0.0;
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else {
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double mu = sqrt(x02 + sigma);
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if( x(0) <= 0.0 )
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v(0) = x(0) - mu;
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if( x0 <= 0.0 )
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v(0) = x0 - mu;
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else
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v(0) = -sigma / (x(0) + mu);
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v(0) = -sigma / (x0 + mu);
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const double v02 = v(0)*v(0);
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beta = 2.0 * v02 / (sigma + v02);
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v = v / v(0);
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}
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return make_pair(beta, v);
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}
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@ -211,7 +211,7 @@ Vector operator/(double s, const Vector& v);
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* from x, such that the corresponding Householder reflection zeroes out
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* all but x.(j), j is base 0. Golub & Van Loan p 210.
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*/
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std::pair<double,Vector> house(Vector &x);
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std::pair<double,Vector> house(const Vector &x);
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/**
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* Weighted Householder solution vector,
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@ -174,15 +174,15 @@ TEST( matrix, row )
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0., -1., 0., 1., 0., 0., 0.3,
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1., 0., 0., 0., -1., 0., 0.2,
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0., 1., 0., 0., 0., -1., -0.1);
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Vector a1 = row(A, 0);
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Vector a1 = row_(A, 0);
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Vector exp1 = Vector_(7, -1., 0., 1., 0., 0., 0., -0.2);
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CHECK(assert_equal(a1, exp1));
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Vector a2 = row(A, 2);
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Vector a2 = row_(A, 2);
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Vector exp2 = Vector_(7, 1., 0., 0., 0., -1., 0., 0.2);
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CHECK(assert_equal(a2, exp2));
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Vector a3 = row(A, 3);
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Vector a3 = row_(A, 3);
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Vector exp3 = Vector_(7, 0., 1., 0., 0., 0., -1., -0.1);
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CHECK(assert_equal(a3, exp3));
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}
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@ -55,7 +55,7 @@ TEST(timeGaussianFactorGraph, linearTime)
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int T = 100000;
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double time1 = timeKalmanSmoother( T); cout << "timeKalmanSmoother( T): " << time1;
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double time2 = timeKalmanSmoother(2*T); cout << " (2*T): " << time2 << endl;
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DOUBLES_EQUAL(2*time1,time2,0.1);
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DOUBLES_EQUAL(2*time1,time2,0.2);
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}
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/* ************************************************************************* */
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@ -71,9 +71,10 @@ TEST(timeGaussianFactorGraph, planar)
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// Alex's Machine
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// Initial:
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// 1907 (N = 30) : 0.14
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// (N = 100): 16.36
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// (N = 100) : 16.36
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// Improved (int->size_t)
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// (N = 100): 15.39
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// (N = 100) : 15.39
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// Using GSL/BLAS for updateAb : 12.87
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// Switch to 100*100 grid = 10K poses
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// 1879: 15.6498 15.3851 15.5279
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@ -125,9 +125,12 @@ double timeVScaleRow(size_t m, size_t n, size_t reps) {
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/**
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* Results:
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* Alex's Machine
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* Alex's Machine (reps = 200000)
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* - ublas matrix_column : 4.63 sec
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* - naive implementation : 4.70 sec
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*
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* reps = 2000000
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* -
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*/
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double timeColumn(size_t reps) {
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// create a matrix
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@ -144,8 +147,8 @@ double timeColumn(size_t reps) {
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boost::timer t;
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for (size_t i=0; i<reps; ++i)
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for (size_t j = 0; j<n; ++j)
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result = ublas::matrix_column<Matrix>(M, j);
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//result = column(M, j);
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//result = ublas::matrix_column<Matrix>(M, j);
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result = column_(M, j);
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elapsed = t.elapsed();
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}
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return elapsed;
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@ -154,10 +157,22 @@ double timeColumn(size_t reps) {
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/*
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* Results
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* Alex's machine
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*
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* Runs at reps = 500000
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* Baseline (no householder, just matrix copy) : 0.05 sec
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* Initial : 8.20 sec
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* All in one function : 7.89 sec
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* Replace householder update with GSL, ATLAS : 0.92 sec
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*
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* Runs at reps = 2000000
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* Baseline (GSL/ATLAS householder update) : 3.61 sec
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*
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* Runs at reps = 5000000
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* Baseline : 8.76 sec
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* GSL/Atlas version of updateAb : 9.03 sec // Why does this have an effect?
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* Inlining house() : 6.33 sec
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* Inlining householder_update [GSL] : 6.15 sec
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*
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*/
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double timeHouseholder(size_t reps) {
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// create a matrix
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@ -182,35 +197,35 @@ double timeHouseholder(size_t reps) {
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int main(int argc, char ** argv) {
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// // Time collect()
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// cout << "Starting Matrix::collect() Timing" << endl;
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// //size_t p = 100000; size_t m = 10; size_t n = 12; size_t reps = 50;
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// size_t p = 10; size_t m = 10; size_t n = 12; size_t reps = 10000000;
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// double collect_time1 = timeCollect(p, m, n, false, reps);
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// double collect_time2 = timeCollect(p, m, n, true, reps);
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// cout << "Average Elapsed time for collect (no pass) [" << p << " (" << m << ", " << n << ") matrices] : " << collect_time1 << endl;
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// cout << "Average Elapsed time for collect (pass) [" << p << " (" << m << ", " << n << ") matrices] : " << collect_time2 << endl;
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//
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// // Time vector_scale_column
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// cout << "Starting Matrix::vector_scale(column) Timing" << endl;
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// size_t m1 = 400; size_t n1 = 480; size_t reps1 = 1000;
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// double vsColumn_time = timeVScaleColumn(m1, n1, reps1);
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// cout << "Elapsed time for vector_scale(column) [(" << m1 << ", " << n1 << ") matrix] : " << vsColumn_time << endl;
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//
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// // Time vector_scale_row
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// cout << "Starting Matrix::vector_scale(row) Timing" << endl;
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// double vsRow_time = timeVScaleRow(m1, n1, reps1);
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// cout << "Elapsed time for vector_scale(row) [(" << m1 << ", " << n1 << ") matrix] : " << vsRow_time << endl;
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//
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// // Time column_() NOTE: using the ublas version
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// cout << "Starting column_() Timing" << endl;
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// size_t reps2 = 200000;
|
||||
// double column_time = timeColumn(reps2);
|
||||
// cout << "Time: " << column_time << " sec" << endl;
|
||||
// Time collect()
|
||||
cout << "Starting Matrix::collect() Timing" << endl;
|
||||
//size_t p = 100000; size_t m = 10; size_t n = 12; size_t reps = 50;
|
||||
size_t p = 10; size_t m = 10; size_t n = 12; size_t reps = 10000000;
|
||||
double collect_time1 = timeCollect(p, m, n, false, reps);
|
||||
double collect_time2 = timeCollect(p, m, n, true, reps);
|
||||
cout << "Average Elapsed time for collect (no pass) [" << p << " (" << m << ", " << n << ") matrices] : " << collect_time1 << endl;
|
||||
cout << "Average Elapsed time for collect (pass) [" << p << " (" << m << ", " << n << ") matrices] : " << collect_time2 << endl;
|
||||
|
||||
// Time vector_scale_column
|
||||
cout << "Starting Matrix::vector_scale(column) Timing" << endl;
|
||||
size_t m1 = 400; size_t n1 = 480; size_t reps1 = 1000;
|
||||
double vsColumn_time = timeVScaleColumn(m1, n1, reps1);
|
||||
cout << "Elapsed time for vector_scale(column) [(" << m1 << ", " << n1 << ") matrix] : " << vsColumn_time << endl;
|
||||
|
||||
// Time vector_scale_row
|
||||
cout << "Starting Matrix::vector_scale(row) Timing" << endl;
|
||||
double vsRow_time = timeVScaleRow(m1, n1, reps1);
|
||||
cout << "Elapsed time for vector_scale(row) [(" << m1 << ", " << n1 << ") matrix] : " << vsRow_time << endl;
|
||||
|
||||
// Time column_() NOTE: using the ublas version
|
||||
cout << "Starting column_() Timing" << endl;
|
||||
size_t reps2 = 2000000;
|
||||
double column_time = timeColumn(reps2);
|
||||
cout << "Time: " << column_time << " sec" << endl;
|
||||
|
||||
// Time householder_ function
|
||||
cout << "Starting householder_() Timing" << endl;
|
||||
size_t reps_house = 500000;
|
||||
size_t reps_house = 5000000;
|
||||
double house_time = timeHouseholder(reps_house);
|
||||
cout << "Elapsed time for householder_() : " << house_time << " sec" << endl;
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue