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.maisonobe@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 pseudorandom 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
>> 32bit 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 MonteCarlo 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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>>>>
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>>
>>
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