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From James Carman <ja...@carmanconsulting.com>
Subject Re: [Math] How fast is fast enough?
Date Sat, 06 Feb 2016 02:07:05 GMT
Passion is a good thing. It means he gives a damn. Gilles obviously cares
quite a bit about the subject matter. I think it's great that he's willing
to crack open some code that a lot of folks wouldn't touch and propose some
interesting changes. Perhaps adding the new implementations alongside the
existing ones would make sense? Maybe in a "beta" package? That way we can
get this stuff in front of folks and let them take it for a spin. Maybe we
create another artifact that we release with less strenuous rules around
backward compatibility? Just some thoughts.

On Fri, Feb 5, 2016 at 8:54 PM Niall Pemberton <niall.pemberton@gmail.com>
wrote:

> Are you not concerned about forming a TLP of 7 around Math when one of the
> seven is clearly not a happy camper?
>
> Niall
>
> On Sat, Feb 6, 2016 at 12:07 AM, Phil Steitz <phil.steitz@gmail.com>
> wrote:
>
> > On 2/5/16 12:59 PM, Gilles wrote:
> > > On Fri, 5 Feb 2016 06:50:10 -0700, Phil Steitz wrote:
> > >> On 2/4/16 3:59 PM, Gilles wrote:
> > >>> Hi.
> > >>>
> > >>> Here is a micro-benchmark report (performed with "PerfTestUtils"):
> > >>> -----
> > >>> nextInt() (calls per timed block: 2000000, timed blocks: 100, time
> > >>> unit: ms)
> > >>>                         name time/call std dev total time ratio
> > >>> cv difference
> > >>> o.a.c.m.r.JDKRandomGenerator 1.088e-05 2.8e-06 2.1761e+03 1.000
> > >>> 0.26 0.0000e+00
> > >>>    o.a.c.m.r.MersenneTwister 1.024e-05 1.5e-06 2.0471e+03 0.941
> > >>> 0.15 -1.2900e+02
> > >>>           o.a.c.m.r.Well512a 1.193e-05 4.4e-07 2.3864e+03 1.097
> > >>> 0.04 2.1032e+02
> > >>>          o.a.c.m.r.Well1024a 1.348e-05 1.9e-06 2.6955e+03 1.239
> > >>> 0.14 5.1945e+02
> > >>>         o.a.c.m.r.Well19937a 1.495e-05 2.1e-06 2.9906e+03 1.374
> > >>> 0.14 8.1451e+02
> > >>>         o.a.c.m.r.Well19937c 1.577e-05 8.8e-07 3.1542e+03 1.450
> > >>> 0.06 9.7816e+02
> > >>>         o.a.c.m.r.Well44497a 1.918e-05 1.4e-06 3.8363e+03 1.763
> > >>> 0.08 1.6602e+03
> > >>>         o.a.c.m.r.Well44497b 1.953e-05 2.8e-06 3.9062e+03 1.795
> > >>> 0.14 1.7301e+03
> > >>>        o.a.c.m.r.ISAACRandom 1.169e-05 1.9e-06 2.3375e+03 1.074
> > >>> 0.16 1.6139e+02
> > >>> -----
> > >>> where "cv" is the ratio of the 3rd to the 2nd column.
> > >>>
> > >>> Questions are:
> > >>> * How meaningful are micro-benchmarks when the timed operation has
> > >>> a very
> > >>>   small duration (wrt e.g. the duration of other machine
> > >>> instructions that
> > >>>   are required to perform them)?
> > >>
> > >> It is harder to get good benchmarks for shorter duration activities,
> > >> but not impossible.  One thing that it would be good to do is to
> > >> compare these results with JMH [1].
> > >
> > > I was expecting insights based on the benchmark which I did run.
> >
> > You asked whether or not benchmarks are meaningful when the task
> > being benchmarked is short duration.  I answered that question.
> > >
> > > We have a tool in CM; if it's wrong, we should remove it.
> > > How its results compare with JMH is an interesting question,
> >
> > I will look into this.
> > > I
> > > agree, but I don't have time to make an analysis of benchmarking
> > > tools (on top of what I've been doing since December because
> > > totally innocuous changes in the RNG classes were frowned upon
> > > out of baseless fear).
> >
> > Please cut the hypberbole.
> > >
> > >>> * In a given environment (HW, OS, JVM), is there a lower limit
> > >>> (absolute
> > >>>   duration) below which anything will be deemed good enough?
> > >>
> > >> That depends completely on the application.
> > >
> > > Sorry, I thought that it was obvious: I don't speak of applications
> > > that don't care about performance. :-)
> > >
> > > For those that do, I do not agree with the statement: the question
> > > relates to finding a point below which it is the environment that
> > > overwhelms the other conditions.
> > > A point where there will be _unavoidable_ overhead (transferring data
> > > from/to memory, JVM book-keeping, ...) and perturbations (context
> > > switches, ...) such that their duration adds a constant time (on
> > > average) that may render most enhancements to an already efficient
> > > algorithm barely noticeable in practice.
> > > Similarly, but in the opposite direction, some language constructs
> > > or design choices might slow down things a bit, but without
> > > endangering any user.
> > >
> > > A problem arises when any enhancement to the design is deemed
> > > harmful because it degrades a micro-benchmark, even though that
> > > benchmark may not reflect any real use-cases.
> > > Then, the real harm is against development.
> > >
> > >>> * Can a library like CM admit a trade-off between ultimate
> > >>> performance and
> > >>>   good design?   IOW, is there an acceptable overhead in exchange
> > >>> for other qualities
> > >>>   (clarity, non-redundancy, extensibility, etc.)?
> > >>
> > >> That is too general a question to be meaningful.   We need to look
> > >> at specific cases.  What exactly are you proposing?
> > >
> > > <rant>
> > > It is quite meaningful even if it refers to general principles.
> > > Those could (should, IMO) be taken into account when managing a
> > > project like CM, on a par with "performance" (whose intrinsic value
> > > is never questioned).
> > > </rant>
> >
> > Rant all you want.  Vague generalities and hyperbole have no value.
> > >
> > > Two specific cases are:
> > > * inheritance vs delegation (a.k.a. composition)
> > > * generics (that could require runtime casts)
> >
> > This is getting closer to meaningful.  Where exactly in the code are
> > you wanting to use something and seeing benchmark damage?
> > >
> > >>> * Does ultimate performance for the base functionality (generation
> > >>> of a
> > >>>   random number) trump any consideration of use-cases that would
> > >>> need an
> > >>>   extension (of the base functionality, such as computation to
> > >>> match another
> > >>>   distribution) that will unavoidably degrades the performance
> > >>> (hence the
> > >>>   micro-benchmark will be completely misleading for those users)?
> > >>
> > >> Again, this is vague and the answer depends on what exactly you are
> > >> talking about. Significantly damaging performance of PRNG
> > >> implementations is a bad idea,
> > >
> > > Now, *this* is vague: what do you mean by "significantly"?
> > > That was actually my question in the first place.
> > If you are talking about PRNG performance, I would say a 1% hit is
> > significant.
> > > Referring to the
> > > benchmark above, people who'd know why they require ultimate
> > > performance
> > > should be able to tell what range of numbers they'd find
> > > acceptable in
> > > that table.
> > >
> > > <rant>
> > > Actually my questions are very precise, but the answers would require
> > > some decent analysis, rather than the usual "bad idea" dismissal.
> > > </rant>
> > >
> > > In the Javadoc of the "random" package, there is information about
> > > performance but no reference as to the benchmarking procedure.
> >
> > It would be great to repeat these using JMH, which is emerging as a
> > de facto standard for java benchmarking.  I will look into this.
> > >
> > > I can consistently observe a totally different behaviour (using
> > > "PerfTestUtils"):
> > >  1. "MersenneTwister" is *always* faster than all of the WELL RNGs;
> > >  2. moreover, the ratio *grows* with each of the longer periods
> > >     members of the WELL family (see the above table).
> > >
> > > This makes me wonder how someone who purports to need "ultimate"
> > > performance can have any objective basis to determine what is good
> > > or bad for his own applications.
> > >
> > >> unless there are actual practical use
> > >> cases you can point to that whatever changes you are proposing
> > >> enable.
> > >
> > > As I've explained in very much details in another thread, I've
> > > reviewed (from a design POV) the RNG code in "random" and IMHO, there
> > > is room for improvement (cf. above for what I mean by that term).
> > > <rant>
> > > I have some code ready for review but I had to resort to what I
> > > considered sub-optimal design (preemptively renouncing to propose a
> > > "delegation"-based design) solely because of the destructive
> > > community
> > > process that takes place here.[1]
> > > </rant>
> >
> > More vague hyperbole that serves no purpose.  Please focus on actual
> > code or design issues.
> > >
> > > The practical use-cases is anything that needs further processing of
> > > the numbers produced according to a uniform distribution:
> >
> > Isn't that what the samplers in the distributions package do?  What
> > we need from the PRNG implementations is just blocks of bits.  Since
> > we wanted a pluggable replacement for j.u.Random, we added uniform
> > ints, longs and floats and gaussian floats.  The samplers just need
> > uniform doubles.  The practical use case we need is well-supported
> > in the code we have.  What is missing, exactly?
> > > I agree that
> > > there would be little sense to code that latter part in a "pure" OO
> > > way[2].  And Luc made it indeed quite efficient, I think, in the
> > > various
> > > concrete classes.
> > > What I want to reconsider is how those concrete low-level
> > > algorithms can
> > > be plugged in a higher-level function that just requires a "source of
> > > randomness", as I'd call a provider of "int" (or "long") values,
> > > where
> > > the high level functionality does not care at all about the
> > > provider's
> > > inner working (a.o. how it's seeded!).
> >
> > This is why many higher-level samplers and other things that require
> > random data inside [math] have a pluggable RandomGenerator.
> > >
> > > A case in point is the sampling of other distributions (namely the
> > > Normal distribution).
> >
> > Or any of the others.  We have a default, inversion-based method
> > that the abstract distribution classes provide and some pretty good
> > specialized implementations within individual distributions.  Most
> > of these just require uniform random doubles as source.
> >
> > >
> > > Here is the benchmark report:
> > > -----
> > > nextGaussian() (calls per timed block: 2000000, timed blocks: 100,
> > > time unit: ms)
> > >                         name time/call std dev total time ratio
> > > cv difference
> > > o.a.c.m.r.JDKRandomGenerator 1.200e-05 1.7e-06 2.4001e+03 1.000
> > > 0.14 0.0000e+00
> > > o.a.c.m.r.JDKRandomGenerator 7.646e-05 5.1e-06 1.5292e+04 6.371
> > > 0.07 1.2892e+04
> > >    o.a.c.m.r.MersenneTwister 6.396e-05 3.6e-06 1.2793e+04 5.330
> > > 0.06 1.0393e+04
> > >           o.a.c.m.r.Well512a 6.880e-05 5.0e-06 1.3760e+04 5.733
> > > 0.07 1.1360e+04
> > >          o.a.c.m.r.Well1024a 6.956e-05 3.0e-06 1.3913e+04 5.797
> > > 0.04 1.1513e+04
> > >         o.a.c.m.r.Well19937a 7.262e-05 2.0e-06 1.4525e+04 6.052
> > > 0.03 1.2125e+04
> > >         o.a.c.m.r.Well19937c 7.164e-05 4.3e-06 1.4329e+04 5.970
> > > 0.06 1.1928e+04
> > >         o.a.c.m.r.Well44497a 8.166e-05 3.2e-06 1.6332e+04 6.804
> > > 0.04 1.3931e+04
> > >         o.a.c.m.r.Well44497b 8.259e-05 4.6e-06 1.6518e+04 6.882
> > > 0.06 1.4118e+04
> > >        o.a.c.m.r.ISAACRandom 6.724e-05 5.4e-06 1.3449e+04 5.603
> > > 0.08 1.1049e+04
> > > -----
> > > where the first line is JDK's "nextInt()" and the remaining are
> > > "nextGaussian()".
> > >
> > > The generation time is thus about 4-fold that of "nextInt()".
> > > Thus, degrading the performance of "nextInt()" by 10% would
> > > degrade the
> > > performance of "nextGaussian()" by half that.
> > >
> > > For a performance discussion to be meaningful, I think that we'd need
> > > to know how that fact would affect, even modestly, any moderately
> > > complex
> > > post-processing of the generated values.
> > >
> > > Another case, for modularity, would be to consider that other
> > > algorithms could
> > > be implemented to provide the required distribution.[3]
> > > In the current design (inheritance-based), that can only be done
> > > by creating
> > > a subclass, even though the core functionality ("nextDouble()") is
> > > not
> > > overridden.
> > >
> > >>> * What are usages of the CM RNGs?
> > >>>   Do those use-cases strictly forbid "loosing" a dozen
> > >>> milliseconds per
> > >>>   million calls?
> > >>
> > >> There are many different use cases.  My own applications use them in
> > >> simulations to generate random deviates, to generate random hex
> > >> strings as identifiers and in stochastic algorithms like some of our
> > >> internal uses.  The last case is definitely sensitive to PRNG
> > >> performance.
> > >
> > > Thanks for giving examples, but since we talk about performance, I
> > > was hoping for some real flesh, like the relative duration of numbers
> > > generation (e.g. the total duration of calls to the "RandomGenerator"
> > > instances wrt to the total duration of the application).
> > >
> > > I don't know if by "last case", you are referring to code that is
> > > inside CM.  I didn't spot anything that makes "heavy" usage of a
> > > RNG (in the sense that generation would count as a sizable part of
> > > the whole processing).
> > monteCarloP in KolmogorovSmirnovTest is one to check.
> > >
> > > As I pointed out many times: if an application is severely dependent
> > > on the performance of RNG, the user probably will turn to specific
> > > tools (e.g. GPUs? [4]) rather than use CM.
> >
> > That is a bogus argument.  We should make our PRNGs simple and fast
> > so their use can extend to performance-sensitive applications.
> > >
> > > Conversely, using Java might be preferred for its flexibility, which
> > > is destroyed by a search for ultimate performance (which nobody seems
> > > able to define reasonably).
> > > Performance is not a goal in itself; it should not be a trophy which
> > > sits uselessly on a shelf.
> >
> > Nor should "beautiful design" in the eyes of one person.
> > >
> > > My goal is not to deliberately slow things down; it is to allow some
> > > leeway so that designs which are deemed better (on all counts except,
> > > perhaps, performance) are given a chance to show their strengths, in
> > > particular in areas where performance in absolute terms is "good
> > > enough" for all use-cases which CM should care about (hence the need
> > > of actual data points[5]).
> >
> > I see no reason that we can't have it both ways - good design and
> > good performance. What we have now, modulo maybe some small changes
> > to reduce code duplication, works fine.  If you want to play with
> > 64-bit generators and can find reference implementations and verify
> > that they do in fact perform better, great.  If not, I don't see the
> > point.  You can rant and complain all you want; but I am not going
> > to let us trash performance or correctness of code in the random
> > class or anywhere else just because you think it is somehow "better
> > designed"  unless you can show specific, practical use cases
> > demonstrating the value of the changes.
> >
> > Phil
> > >
> > >
> > > Gilles
> > >
> > > [1] "Is it faster?"
> > >     "No."
> > >     "Then, no."
> > > [2] Although that is in some sense what you indirectly defend by
> > > wanting
> > >     to stick with a meaningless "next(int bits)" method.
> > > [3] http://www.doornik.com/research/ziggurat.pdf
> > > [4] http://http.developer.nvidia.com/GPUGems3/gpugems3_ch37.html
> > > [5] Hence the need to agree on a methodology/policy for benchmarking.
> > >
> > >>
> > >> Phil
> > >>
> > >> [1] http://openjdk.java.net/projects/code-tools/jmh/
> > >>>   IOW, would those users for which such a difference matters use
> > >>> CM at all?
> > >>
> > >>>
> > >>>
> > >>> Thanks,
> > >>> Gilles
> > >
> > >
> > >
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> > >
> > >
> >
> >
> >
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> >
> >
>

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