Le 01/08/2011 22:40, Luc Maisonobe a écrit :
Hi Phil,
Le 01/08/2011 20:39, Phil Steitz a écrit :
On 8/1/11 1:31 AM, luc.maison...@free.fr wrote:
Hi Phil,
----- Mail original -----
In my own applications, I noticed what appears to be poor
performance in the nextInt(int) method of the Mersenne twister,
which I was using to *improve* speed. I think that for small n, the
default implementation in BistreamGenerator may be running too many
iterations.
Mersenne twister uses a quite large pool. It creates pseudo-random bits
by twisting it and creates large bunches at a time (624 words at a
time).
Hence when you ask for large sets, you should have several calls that
return fast, and one call that takes a longer time to generate another
large pool.
So good performances are obtained for generating large sets, not
small sets.
Well generators should be faster and are preferred over Mersenne
twister now,
which is now an old generator. Well generators also have large pools,
but they
don't generate bits in large batches in advance, they do generate a
few words
at a time.
Yeah, I know. Both are faster than the JDK, though, even for just
32-bit chunks in my tests at least.
One thing I have been thinking about is exposing nextInt[],
nextDouble[], nextGaussian[] etc methods that take advantage of the
pools. So you basically generate a large block of bits use this to
fill the output arrays.
Seems a very good idea. Most of the time, people generate only one kind
of numbers several times, so it really does make sense.
I am still figuring out how the code works, but I
thought it would be good to run some benchmarks - using Gilles' new
stuff - against the Harmony implementation in java.util.Random of
this method. That led me to notice that there are no unit tests for
BitstreamGenerator. I propose that we add
0) RandomGeneratorAbstractTest with an abstract makeGenerator
method including fixed seed tests for all RandomGenerator methods
1) BitstreamGeneratorTest extending RandomGeneratorAbstractTest
implementing makeGenerator with a BitStreamGenerator that uses the
JDK generator for next(int)
2) Make the test classes for Mersenne and Weil generators extend
RandomGeneratorAbstractTest, moving redundant tests up into the base
class
Sound reasonable?
+1
Also, any recollection why we are using a
different implementation in BitStreamGenerator for next(int) than
Harmony and the JDK use?
I don't understand what you mean. next(int) is used to generate the raw
bits and is the heart of each generator. Each generator has its own
implementation. Replacing next(int) by the JDK generation would imply
dropping completely Mersenne twister and Well generators.
I am sorry. I meant nextInt(int). It is that code that seems to be
slow in BitStreamGenerator and different from the JDK and Harmony.
Could you point me at some code ? There are many pitfalls in netInt(int)
if one wants to make sure the generator is uniform, which explain the
strange implementation, with the mask computation and the loop. By the
way, even this implementation would benefit from your proposed array
generation, as the mask could be computed only once.
I have looked at the implementation for JDK and Harmony and am a little
puzzled.
The trick for the power of two (i.e. if ((n & -n) == n)) is not useful
for the very elaborate generators like Mersenne twister or Well. Both
are proven to be equidistributed even for the low order bits. They are
based on linear recurrences but not linear congruences and do not suffer
from the drawbacks of the latter.
What puzzles me more is the loop. It is documented as avoiding the
uneven distributions, but at first glance the modulo operation bothers
me. As documentation explicitly states it is designed for this, it is
most probably true, I simply don't understand how yet.
So our current implementation is slow, then go ahead and change it to
the one you showed me. I would simply suggest to get rid of the ((n &
-n) == n) test. I'll try to understand the condition in the while loop
to understand how it rejects uneven distributions, just out of curiosity
for myself.
Luc
Luc
Phil
Mersenne twister and Well should be fast for generating large sets, but
most importantly they have very good and *proven* properties
(equidistribution
on large dimensions, null correlation, maximal period ...). These
properties
are essential for example in Monte-Carlo simulations with lots of
variables that
must be independent or have controlled correlations.
Luc
The Harmony impl is almost identical to
what is documented in the JDK javadoc.
Phil
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