Hi,

On 04.10.2013 18:52, Reich, Darrell wrote:

Hi—

So far my code below has been unable to beat the Fortran 77 program benchmark using a quad core i7.

This can be especially when you ar eusing optimized blas routines in Fortran or don't compile with the proper release mode defines (maximum optimizations and #define BOOST_UBLAS_NDEBUG).

While debugging this, I get unexpected results from M.filled1(), M.filled2() when compared to M.size1() and M.size2(). I expected that filled/size * 100 = % filled but filled2 > size2? v.filled and v.size are what I expected.

filled2 is the number of non zero elements in your matrix this is between 0 and size1()*size2().

When I switch compressed_vector to vector, the code crashes. I had to remove the debug print for v.filled since vector does not implement it. I assume here filled = size.

there are a few things wrong with your code. Let's start with the most obvious:

v1[i].vectors.resize(n);

should be

v1[i].vectors[j].resize(n);

this explains completely your problem with noalias: you have to initialize the target vector accordingly.

also use axpy_prod instead of prod. Still it is likely that sparse vectors and matrices will give you horrile results. dnse vectors should be reasonable, though.

Greetings,
Oswin

When I switch to column major form (code commented out below) it takes longer. I suspect that is no surprise.

I’m sure it is time to go from basic boost to advanced. Thanks! We’re using version 1.54.

Extra credit: I’m sure there is an optimized way to do the += and the copy too?

I tried noalias(v2) = prod(M,v1) but it crashes with the example below.

I tried writing my own product function using iterators but it crashes too.

Boost FOREACH looks interesting but I can’t find an example for a Matrix and why rewrite prod if we don’t have to

I think Plan A get boost prod working faster versus Plan B replace prod with rewritten product if the best if you have any suggestions on what I could do better. Thanks!

Visual Studio C++ 2012 compiler settings for Win32 build on Windows 7 64-bit:

/Yu"StdAfx.h" /MP /GS /GL /analyze- /W3 /Gy- /Zc:wchar_t /Zi /Gm /O2 /Ob2 /Fd"Release\vc100.pdb" /fp:fast /D "WIN32" /D "NDEBUG" /D "_WINDOWS" /D "_USRDLL" /D "TEST_EXPORTS" /D "_WINDLL" /D "_UNICODE" /D "UNICODE" /errorReport:prompt /WX- /Zc:forScope /Gd /Oy- /Oi /MT /Fa"Release\" /EHsc /nologo /Fo"Release\" /Ot /Fp"Release\Test.pch"

#include "stdafx.h" // copied all boost includes into this pre-compiled header file

#include <boost/numeric/ublas/matrix.hpp>

#include <boost/numeric/ublas/matrix_sparse.hpp>

#include <boost/numeric/ublas/matrix_expression.hpp>

#include <boost/numeric/ublas/matrix_proxy.hpp>

#include <boost/numeric/ublas/vector.hpp>

#include <boost/numeric/ublas/vector_sparse.hpp>

#include <boost/numeric/ublas/vector_expression.hpp>

#include <boost/numeric/ublas/vector_proxy.hpp>

#include <boost/numeric/ublas/lu.hpp>

#include <boost/numeric/ublas/io.hpp>

// make release build faster

#ifdef NDEBUG

#define BOOST_UBLAS_NDEBUG

#endif

struct VECTOR

{

   std::vector<boost::numeric::ublas::compressed_vector<float>> vectors;

};

std::vector<boost::numeric::ublas::compressed_matrix<float>> theMatrix;

//std::vector<boost::numeric::ublas::compressed_matrix<float, boost::numeric::ublas::column_major>> theMatrix;

std::vector<VECTOR> v1;

std::vector<VECTOR> v2;

// ...

ilen = 22;

jlen = 2;

xlen = 250;

ylen = 250;

n = xlen * ylen; // size = 64,000

theMatrix.reserve(ilen);

v1.reserve(ilen);

v2.reserve(ilen);

// ...

v2[i].vectors.reserve(jlen);

v2[i].vectors.reserve(jlen);

// ...

theMatrix[i].resize(n,n); // sparse ~7 diagonals filled

v1[i].vectors.resize(n); // starts sparse, ends filled

v2[i].vectors.resize(n);

// ...

theMatrix[i](j,k) = x; // load matrix once

// ...

v2[i].vectors[j] = prod(theMatrix[i], v1[i].vectors[j]);

// ...

v2[i].vectors[j](k) += datapoint; // add more to vector each time

// ...

// copy values back to save for next time step...

for (int i = 0; i < ilen; i++)

{

   for (int j = 0; j < jlen; j++)

   {

         v1[i].vectors[j] = v2[i].vectors[j];

   }

}
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