Reimplemented matrix::householder_update using GSL and BLAS, you'll need to edit Makefile.am to enable the new version -- see email
Alex Cunningham
parent
188561d925
commit
063aa14118
20
configure.ac
20
configure.ac
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@ -23,6 +23,26 @@ AC_ARG_ENABLE([debug],
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*) AC_MSG_ERROR([bad value ${enableval} for --enable-debug]) ;;
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esac],[debug=false])
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AM_CONDITIONAL([DEBUG], [test x$debug = xtrue])
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# enable using GSL for linalg
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AC_ARG_ENABLE([gsl],
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[ --enable-gsl Enable the GSL library],
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[case "${enableval}" in
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yes) gsl=true ;;
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no) gsl=false ;;
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*) AC_MSG_ERROR([bad value ${enableval} for --enable-gsl]) ;;
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esac],[gsl=false])
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AM_CONDITIONAL([GSL], [test x$gsl = xtrue])
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# enable using ATLAS for BLAS
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AC_ARG_ENABLE([atlas],
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[ --enable-atlas Enable ATLAS optimized BLAS],
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[case "${enableval}" in
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yes) atlas=true ;;
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no) atlas=false ;;
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*) AC_MSG_ERROR([bad value ${enableval} for --enable-atlas]) ;;
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esac],[atlas=false])
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AM_CONDITIONAL([ATLAS], [test x$atlas = xtrue])
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# Checks for programs.
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AC_PROG_CXX
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@ -266,6 +266,26 @@ include_HEADERS = $(headers)
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AM_CXXFLAGS += -I..
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AM_LDFLAGS = -L../CppUnitLite -lCppUnitLite $(BOOST_LDFLAGS) $(boost_serialization) #-L. -lgtsam
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# TO ENABLE GSL AND ATLAS, uncomment this part!
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# Direct call for gsl and atlas
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# AM_LDFLAGS += -lgsl -lcblas -latlas
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# libgtsam_la_LDFLAGS += -lgsl -lcblas -latlas
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# AM_CXXFLAGS += -DGSL
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# libgtsam_la_CPPFLAGS += -DGSL
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# GSL using ATLAS
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# if GSL
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# AM_CXXFLAGS += -DGSL
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# libgtsam_la_CPPFLAGS += -DGSL
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# if ATLAS
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# AM_LDFLAGS += -lgsl -lcblas -latlas
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# libgtsam_la_LDFLAGS += -lgsl -lcblas -latlas
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# else
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# AM_LDFLAGS += -lgsl -lgslcblas
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# libgtsam_la_LDFLAGS += -lgsl -lgslcblas
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# endif
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# endif
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TESTS = $(check_PROGRAMS)
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CXXLINK = $(LIBTOOL) $(AM_LIBTOOLFLAGS) $(LIBTOOLFLAGS) --mode=link \
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$(CXXLD) -g $(AM_CXXFLAGS) $(CXXFLAGS) $(AM_LDFLAGS) $(LDFLAGS) \
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@ -9,6 +9,11 @@
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#include <iomanip>
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#include <list>
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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/matrix_proxy.hpp>
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#include <boost/foreach.hpp>
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#include <boost/numeric/ublas/lu.hpp>
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@ -19,6 +24,7 @@
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#include "Vector.h"
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#include "svdcmp.h"
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using namespace std;
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namespace ublas = boost::numeric::ublas;
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@ -276,6 +282,14 @@ void householder_update(Matrix &A, int j, double beta, const Vector& vjm) {
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const size_t m = A.size1(), n = A.size2();
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#ifdef GSL
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// 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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#else
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// elegant but slow: A -= Matrix(outer_prod(v,beta*trans(A)*v));
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// faster code below
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@ -299,6 +313,7 @@ void householder_update(Matrix &A, int j, double beta, const Vector& vjm) {
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// A(r,c) -= vjm(r-j) * wjn(c-j);
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(*a) -= v[s] * wc;
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}
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#endif
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}
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/* ************************************************************************* */
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@ -379,29 +394,30 @@ weighted_eliminate(Matrix& A, Vector& b, const Vector& sigmas) {
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/* ************************************************************************* */
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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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// 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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const size_t m = A.size1(), n = A.size2(), kprime = min(k,min(m,n));
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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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double beta; Vector vjm;
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boost::tie(beta,vjm) = house(xjm);
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// Original version
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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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// 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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// 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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// 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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// calculate the Householder vector
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double beta; Vector vjm;
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boost::tie(beta,vjm) = house(xjm);
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} // column j
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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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// 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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}
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/* ************************************************************************* */
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@ -156,6 +156,8 @@ double timeColumn(size_t reps) {
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* Alex's machine
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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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double timeHouseholder(size_t reps) {
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// create a matrix
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@ -178,48 +180,33 @@ double timeHouseholder(size_t reps) {
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return elapsed;
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}
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//double data[] = {-5, 0, 5, 0, 0, 0, -1,
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// 00, -5, 0, 5, 0, 0, 1.5,
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// 10, 0, 0, 0,-10, 0, 2,
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// 00, 10, 0, 0, 0,-10, -1};
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//
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//// check in-place householder, with v vectors below diagonal
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//double data1[] = {
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// 11.1803, 0, -2.2361, 0, -8.9443, 0, 2.236,
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// 0, 11.1803, 0, -2.2361, 0, -8.9443, -1.565,
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// -0.618034, 0, 4.4721, 0, -4.4721, 0, 0,
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// 0, -0.618034, 0, 4.4721, 0, -4.4721, 0.894 };
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//Matrix expected1 = Matrix_(4,7, data1);
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//Matrix A1 = Matrix_(4, 7, data);
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//householder_(A1,3);
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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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// 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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// 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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// Time column_() NOTE: using the ublas version
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cout << "Starting column_() Timing" << endl;
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size_t reps2 = 200000;
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double column_time = timeColumn(reps2);
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cout << "Time: " << column_time << " sec" << endl;
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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;
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// double column_time = timeColumn(reps2);
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// cout << "Time: " << column_time << " sec" << endl;
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// Time householder_ function
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cout << "Starting householder_() Timing" << endl;
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