Hi Stefan,
strange, I was looking at the same code just a few days ago, when I
needed to find out whether I understood filters well enough by pushing
noise through. Here's my small testcases (thankfully I didn't restart my
machine since then -- these were still in /tmp).
So, in fact, I tested the performance of boost's mt19937 + boost's
normal distribution variate, and basically, it's relatively slow,
indeed; generating 1e8 complex random numbers (meaning 200e8 random
floats) with a single thread takes
g++ (GCC) 5.1.1 20150618 (x86_64)
test_rnd (Boost)
g++ -OXXX test_rnd.cpp
test_gr (gr::random)
g++ $(pkg-config --cflags --libs gnuradio-runtime) -OXXX te_gr.cpp
-O3 (optimized for speed)
1.25 s
5.92 s
-O0 (explicitly unoptimized) (default)
23.62 s
7.13 s
Someone who cares for speed could get a 5x increase of speed by using
boost, because that person would be using optimization, anyways.
However, I can't really explain what happens with boost and the
unoptimized case; it's really three times as bad as the current
implementation.
However, asking perf about this, the -O3 version spends nearly all of
its time in main(), whereas the -O0 spends most of it in some boost
functions -- which means that -O3 definitely inlines the calculations,
and from the disassembly alone I'd say all the stack operations needed
to jump in and out of routines seem to contribute significantly to the
problem.
Of course, that's not really a benchmark for all systems. What about 32
bit? What about ARM? What about clang? Can anyone try to make a Windows
build?
Best regards,
Marcus
On 02.09.2015 14:10, Stefan Wunsch wrote:
> Hi!
>
> I have discovered that the implemented random number generator in
> gnuradio (see file [0]) is almost older than me. As written in the code,
> the implementation is taken from 'Numerical recipes in C' (see version
> from 1992). The problem is that this algorithm is really bad compared to
> current algorithms. E.g. the period length is 1e8 compared to an
> up-to-date algorithm (Mersenne twister) with a period length of about
> 1e6000.
>
> I have fixed this [1] using boost.random and the mentioned Mersenne
> twister. Furthermore I have written some test-cases and fixed the
> transformation to Laplacian random numbers (the current implementation
> is wrong). As well, I have added the random class to swig so that you
> can use this in python.
>
> Now my question: Before doing a pull request, do you have any concerns
> regarding memory use or processing load? Obviously the new
> implementation isn't that light-weight as the ten lines of code before.
> But the current implementation can not be used in any serious simulation
> or publication, which is highly dependent on good random numbers. Some
> information about the performance is given on this page: [2]. Look for
> the generator mt19937 in table 24.5.
>
> Best regards
> Stefan
>
> [0] gnuradio/gnuradio-runtime/lib/math/random.cc
> [1] https://github.com/stwunsch/gnuradio/tree/newRandom
> [2]
> http://www.boost.org/doc/libs/1_59_0/doc/html/boost_random/reference.html
>
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> Discuss-gnuradio mailing list
> [email protected]
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#include <boost/random.hpp>
#include <boost/random/normal_distribution.hpp>
#include <complex>
int main(int argc, char** argv){
int rate = 100*1000*1000;
std::vector< std::complex<float> > noise(rate);
//initialize random number generator, seed 0, for reproducability
boost::mt19937 random_num_gen(0);
// limit sigma of the normal distribution, so that nearly all values fall into (-1;1)
// A distribution object is applicable to a random number generator (which inherently produces uniform output)...
boost::normal_distribution<float> normal_distribution(0.0, 0.25);
//...using a variate generator
boost::variate_generator< boost::mt19937&, boost::normal_distribution<float> >
variate_normal(random_num_gen, normal_distribution);
//get two random numbers, one for real, one for imag part, per sample
for(int sample = 0; sample < rate; sample++)
noise[sample]=std::complex<float>(variate_normal(), variate_normal());
}
#include <boost/random.hpp>
#include <boost/random/normal_distribution.hpp>
#include <complex>
#include <gnuradio/random.h>
int main(int argc, char** argv){
int rate = 100*1000*1000;
std::vector< std::complex<float> > noise(rate);
gr::random rnd(0); //initialize
for(int i = 0; i < rate; i++) {
noise[i]=std::complex<float>(rnd.gasdev(), rnd.gasdev());
}
}
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