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From Gilles <gil...@harfang.homelinux.org>
Subject Re: [Math] How fast is fast enough?
Date Sat, 06 Feb 2016 02:32:04 GMT
On Sat, 6 Feb 2016 01:53:54 +0000, Niall Pemberton wrote:
> Are you not concerned about forming a TLP of 7 around Math when one 
> of the
> seven is clearly not a happy camper?

Of course I am.
Besides stopping to annoy non-Math Commons developers, I've asked
that we clarify the positive objectives of the move, as far as
development would be concerned.
No one except Thomas seemed interested.

Gilles

>
> 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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