Re: [boost] [random] new threefry random engine

On 02 May 2014, at 16:30, John Salmon wrote:

On Fri, May 2, 2014 at 8:32 AM, Peter Dimov wrote:

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Thijs van den Berg wrote:

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In that case it’s best to use a random engine adaptor.

counter_based_engine< Threefry<2,unsigned> > eng; uniform_real_distribution<float> zero_one(0., 1.);

eng.seed(thread_id);

for(int i=0; i

The adaptor needs to be in the loop; you don't have a thread_id outside it.

uniform_real_distribution<float> zero_one(0., 1.);

#pragma omp parallel for for( int i=0; i > eng; eng.seed( i );

out[i] = in[i]*zero_one( eng ); }

In principle, this works with any engine, not just a counter-based one, as long as creating and seeding is quick and consecutive seeds result in random first output.

Yes. But Boost's documentation advises that in general, these requirements are *not* met:

http://www.boost.org/doc/libs/1_55_0/doc/html/boost_random/reference.html#bo...

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Pseudo-random number generators should not be constructed (initialized) frequently during program execution, for two reasons. First, initialization requires full initialization of the internal state of the generator. Thus, generators with a lot of internal state (see below) are costly to initialize. Second, initialization always requires some value used as a "seed" for the generated sequence. It is usually difficult to obtain several good seed values... </snip>

This is very good advice because as far as I know, none of the existing engines satisfy all the requirements: small state, quick-to-initialize, quick-to-run and high-quality, statistically independent results when seeded consecutively.

Counter-based RNGs are specifically designed to meet these requirements. State ranges from 8 to 32 bytes. They generate 8 to 32 random bytes at a time at rates of 2-4 cycles-per-byte. Initialization time is negligible compared to generation. Output passes TestU01.

John Salmon

I think there isn’t any discussion about counter based random engines being a good idea. What needs to be made clear is the benefits of having a random function concept next to a random engine concept. When would a user have clear benefit of using a random function directly instead of a random engine that implements the same algorithm? The concept and benefit need to be made clear IMO. So far I’ve hear about 4 different types of random functions. The challenge is to capture them all in a single concepts.