[Rd] Package compiler - efficiency problem

Karol Podemski gecon@m@inten@nce @ending from gm@il@com
Fri Aug 17 00:43:37 CEST 2018

Dear Thomas,

thank you for prompt response and taking interest in this issue. I really
appreciate your compiler project and efficiency gains in usual case. I am
aware of limitations of interpreted languages too and because of that even
when writing my first mail I had a hunch that it is not that easy to
address this problem.  As you mentioned optimisation of compiler for
handling non-standard code may be tricky and harmful for usual code. The
question is if gEcon is the only package that may face the same issue
because of compilation.

The functions generated by gEcon are systems of non-linear equations
defining the equilibrium of an economy (see
http://gecon.r-forge.r-project.org/files/gEcon-users-guide.pdf  if you want
to learn a bit how we obtain it). The rows, you suggested to vectorise, are
indeed vectorisable because they define equilibrium for similiar markets
(e.g. production and sale of beverages and food) but do not have to be
vectorisable in general case. So that not to delve into too much details I
will stop here in description of how the equations originate. However, I
would like to point that similiar large systems of linear equations may
arise in other fields ( https://en.wikipedia.org/wiki/Steady_state ) and
there may be other packages that generate similar large systems (e.g.
network problems like hydraulic networks). In that case, reports such as
mine may help you to assess the scale of the problems.

Thank you for suggestions for improvement in our approach, i am going to
discuss them with other package developers.

Karol Podemski

pon., 13 sie 2018 o 18:02 Tomas Kalibera <tomas.kalibera using gmail.com>

> Dear Karol,
> thank you for the report. I can reproduce that the function from you
> example takes very long to compile and I can see where most time is spent.
> The compiler is itself written in R and requires a lot of resources for
> large functions (foo() has over 16,000 lines of code, nearly 1 million of
> instructions/operands, 45,000 constants). In particular a lot of time is
> spent in garbage collection and in finding a unique set of constants. Some
> optimizations of the compiler may be possible, but it is unlikely that
> functions this large will compile fast any soon. For non-generated code, we
> now have the byte-compilation on installation by default which at least
> removes the compile overhead from runtime. Even though the compiler is
> slow, it is important to keep in mind that in principle, with any compiler
> there will be functions where compilation would not be improve performance
> (when the compile time is included or not).
> I think it is not a good idea to generate code for functions like foo() in
> R (or any interpreted language). You say that R's byte-code compiler
> produces code that runs 5-10x faster than when the function is interpreted
> by the AST interpreter (uncompiled), which sounds like a good result, but I
> believe that avoiding code generation would be much faster than that, apart
> from drastically reducing code size and therefore compile time. The
> generator of these functions has much more information than the compiler -
> it could be turned into an interpreter of these functions and compute their
> values on the fly.
> A significant source of inefficiency of the generated code are
> element-wise operations, such as
> r[12] <- -vv[88] + vv[16] * (1 + ppff[1307])
> ...
> r[139] <- -vv[215] + vv[47] * (1 + ppff[1434])
> (these could be vectorized, which would reduce code size and improve
> interpretation speed; and make it somewhat readable). Most of the code
> lines in the generated functions seem to be easily vectorizable.
> Compilers and interpreters necessarily use some heuristics or optimize at
> some code patterns. Optimizing for generated code may be tricky as it could
> even harm performance of usual code. And, I would much rather optimize the
> compiler for the usual code.
> Indeed, a pragmatic solution requiring the least amount of work would be
> to disable compilation of these generated functions. There is not a
> documented way to do that and maybe we could add it (and technically it is
> trivial), but I have been reluctant so far - in some cases, compilation
> even of these functions may be beneficial - if the speedup is 5-10x and we
> run very many times. But once the generated code included some pragma
> preventing compilation, it won't be ever compiled. Also, the trade-offs may
> change as the compiler evolves, perhaps not in this case, but in other
> where such pragma may be used.
> Well so the short answer would be that these functions should not be
> generated in the first place. If it were too much work rewriting, perhaps
> the generator could just be improved to produce vectorized operations.
> Best
> Tomas
> On 12.8.2018 21:31, Karol Podemski wrote:
>  Dear R team,
> I am a co-author and maintainer of one of R packages distributed by R-forge
> (gEcon). One of gEcon package users found a strange behaviour of package (R
> froze for couple of minutes) and reported it to me. I traced the strange
> behaviour to compiler package. I attach short demonstration of the problem
> to this mail (demonstration makes use of compiler and tictoc packages only).
> In short, the compiler package has problems in compiling large functions -
> their compilation and execution may take much longer than direct execution
> of an uncompiled function. Such functions are generated by gEcon package as
> they describe steady state for economy.
> I am curious if you are aware of such problems and plan to handle the
> efficiency issues. On one of the boards I saw that there were efficiency
> issues in rpart package but they have been resolved. Or would you advise to
> turn off JIT on package load (package heavily uses such long functions
> generated whenever a new model is created)?
> Best regards,
> Karol Podemski
> ______________________________________________R-devel using r-project.org mailing listhttps://stat.ethz.ch/mailman/listinfo/r-devel

	[[alternative HTML version deleted]]

More information about the R-devel mailing list