Re: [Chicken-users] Was crunch really discontinued? Is there an alternative?

[Alex Shinn]

Jeronimo Pellegrini <address@hidden> writes:

> http://aleph0.info/scheme/
>
> The times I listed before are for 100000 repetitions on small matrices
> (3x4, 4x6), so as to alsoinclude function call overhead in the
> benchmark.
>
> I have uploaded two 100x100 random matrices also, and the results for
> 20 repetitions on them (results.txt).
>
> I understand that micro-benchmarks like this are usually not
> significative, but in this case they make some sense, since it's the
> kind of thing my programs will do most of the time.

OK, there are lots of things going on here :)

The first is that you're using a naive multiplication
algorithm - there are faster algorithms, and algorithms that
take L1 cache consideration into account for very large
matrices, and BLAS does all of this in addition to being
written in highly tuned Fortran.  If matrix operations are
really what you want to do, then as Ivan says just use BLAS
:)

If you were more curious about the speed of Scheme compilers
for their own sake, and not about actually getting work
done, then there are several reasons for the slowness.  The
first is that SRFI-25 is inherently slow - the design makes
it difficult to implement efficiently, and so that's slowing
down all of the Scheme implementations.  It's easy enough to
just implement your own matrices on top of vectors for a
huge speed boost.

The next problem is that presumably you want to test
floating point, although the test case you use involves only
fixnums.  Floating point involves heap allocation for every
operation in I think all Scheme implementations except
Stalin.  Stalin can unbox floating point numbers if it can
prove all of the types involved are inexact (it wouldn't
work on this example because of the general READ, you'd have
to tweak it so that Stalin's type inference would kick in).
This makes Scheme in general unsuited to floating point
intensive computations.

Given that you're only testing fixnums here, the -fixnum
optimization gives a big boost.

For more low-level tweaking, you can get a small boost out
of Chicken by unifying the do loops as described here:

http://lists.gnu.org/archive/html/chicken-users/2009-02/msg00050.html

The attached variation of the code, with the -fixnum flag,
will get this specific example within the ballpark of BLAS
(0.1 seconds on my machine), but only for the small example
you're using.  As the matrices get larger BLAS will become
increasingly faster, and Scheme will be painfully slow with
inexacts.

-- 
Alex

matrix-bench-chicken.scm
Description: Binary data