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Re: caml (special) light and numerics
• Xavier Leroy
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 Date: -- (:) From: Xavier Leroy Subject: Re: caml (special) light and numerics
```
> This may be asking too much, but are there any (written) guidelines
> for efficient numerical CSL coding (besides the one on the WWW site
> that gave me some hints)?

As Pierre Weis said, don't worry too much about tail calls or heap
allocations: these are pretty efficient. Other factors like boxing of
floats (see below) will kill you much earlier if not taken into
account.

Here are what I think are the main points to remember for writing
efficient numerical code in Caml Special Light. I don't have much
experience with numerical code, in CSL or otherwise, so these are only
hints.

0- The cslopt compiler is poorly named because it basically does not
optimize the user's code. That is, everything will be executed pretty
much as written in the source. Function applications will always
perform a function call (no inlining). Function definitions fun x -> ...
will always heap-allocate a closure (no lambda-lifting, no closure
hoisting). Arithmetic expressions will be computed at the point where
they occur in the source (no loop-invariant code motion). So, the
programmer is very much in control. (Inlining might be added some day,
but probably no other optimization.)

1- Matrices in Caml are really arrays of (pointers to) arrays. So, a
matrix access a.(i).(j) reads the i-th element of a, which is a
pointer to an array, then read the j-th element of that array.
It's about as efficient as the standard layout for matrices when the
dimensions are not known at compile-time (we're trading a dereference
for a multiplication), but can be a big loss if the dimensions are
statically known and one happens to be, say, a power of 2.

A first consequence is that rows of a matrix are really individual
arrays: they need not be the same length (great for triangular matrices)
and a whole row of a matrix can be replaced in constant time: just do
a.(i) <- some_array. There might also be some creative things you can
do by sharing a row between several matrices.

Another consequence is that matrices are row-major (hope that's the
right term): the first coordinate should vary slower than the second.
E.g. to sum two N*N matrices, don't write

for j = 0 to N-1 do
for i = 0 to N-1 do
c.(i).(j) <- a.(i).(j) + b.(i).(j)
done
done

but write:

for i = 0 to N-1 do
let row_a = a.(i) and row_b = b.(i) and row_c = c.(i) in
for j = 0 to N-1 do
row_c.(j) <- row_a.(j) + row_b.(j)
done
done

(Notice the application of point 0: three loop-invariant expressions
have been lifted manually outside of the inner loop.)

2- Floating-point boxing. Because of the combination of garbage
collection, polymorphism/type abstraction, and separate compilation,
floating-point numbers are often boxed, that is, stored in a
heap-allocated block and handled through a pointer. Most Lisp and ML
implementations box (or tag) floats, and that's the main reason why
they deliver poor floating-point performance: any arithmetic operation
on boxed floats has to load from memory its arguments, compute the
result, heap-allocate a block, and store the result in it.

One of the ancestors of Caml Special Light, the Gallium experimental
compiler, implemented a fairly aggressive type-based unboxing strategy
that would completely eliminate boxing from Fortran-style
code. i.e. code without polymorphic functions. Unfortunately, it
turned out to complicate greatly the runtime system and make the
garbage collector less efficient, hence hampering the performance of
symbolic code. Since symbolic computation is still the main
application of ML, I dropped this unboxing strategy.

However, some of the unboxing tricks developed for Gallium are still
used in Caml Special Light, at least on a local scale (where they
don't interact with the garbage collector and everything). Here are
some hints on which floats are boxed and which are not:

a. Function arguments are always boxed.

b. Free variables of functions are always boxed.

c. Intermediate results of arithmetic expressions are not boxed.
By "intermediate result", I mean the result of an arithmetic
operation (+. -. *. /. ** sin cos exp log ...) that is immediately
used as argument of an arithmetic operation.

Example: fun x -> 3.14 *. x *. x
The parameter x and the result are boxed, but not x *. x.

d. "let"-bound results of arithmetic operations are not boxed if they
are only used later as arguments to arithmetic operations.

Example: let x = y +. z in x *. x               x not boxed
let x = y +. z in x *. x *. f x        x boxed (passed as argument)

e. Floats in data structures are generally boxed. E.g. a list of
floats is really a list of pointers to floats. But there are two
exceptions:

- arrays of floats (the floats are laid out contiguously, as in C;
this is not an array of pointers to floats)
- record types whose fields are all floats (same as arrays)

An access in a float array or in such a record avoids boxing in the
same way as for arithmetic operations.

Example: a.(i) <- 2.0 *. a.(i) performs only one float read and one
float store, instead of two reads, a boxing and a store as in most ML
implementations.

A few consequences of these hints:

a => inline small functions over floats

b + d => iterate with "for" and "while" loops instead of recursive functions
Example:
let x = y +. z in
for i = 0 to N-1 do a.(i) <- x *. a.(i) done
does not box x, but
let x = y +. z in
let rec iter i =
if i >= N then () else (a.(i) <- x *. a.(i); iter(i+1))
in iter 0
is not only unreadable, but causes x to be boxed because it is
free in iter.

e => use records in preference to tuples for representing points,
complex numbers, etc. Example:
type complex = {re: float; im: float}
saves 4 loads and 2 allocations for each complex addition compared with
type complex = float * float

all => Fortran/C style (big functions, loops all over the place) is
compiled better than "classic" functional style (functions,
functionals, iterators, combinators till you scream).

Another consequence of the optimization of float arrays is that there
are really two formats of arrays in the system: arrays of floats and
arrays of pointers/integers.

The illusion of a parametric array type 'a array is maintained at some
cost: when accessing an array whose static type is 'a array (e.g. in a
polymorphic function), a run-time test is generated to distinguish the
two array formats, and some additional boxing/unboxing may take place
if it's a float array. When more is known on the static type of the
array, no test is generated and no extra boxing takes place.

So, for maximal performance, don't operate over float arrays with
polymorphic array functions. For instance, transposing a float matrix with

let transpose m =
let m' = Array.new_matrix (Array.length m.(0)) (Array.length m) in
for i = 0 to Array.length m do
let row_i = m.(i) in
for j = 0 to Array.length row_i do
m'.(j).(i) <- row_i.(j)
done
done;
m'

is much more efficient if you add a type constraint:

let transpose (m: float array array) = ...

Well, that's all I can say in general. To know more, you'll have to
write some code and test it. Remember: write it cleanly, then profile
it, then optimize the hot spots -- not the other way around. And let us
know if you beat Fortran 90 in development time + running time.

One last thing:

> Another example: how can I find out (without deciphering assembler
> code) if a particular recursive function has been recognized as tail
> recursive?

Reading assembler code is always an option. You can also compile with
the "-dcmm" option. This will print one of the intermediate
representations, which shows pretty clearly tail calls vs. regular calls,
direct calls vs. indirect calls, and boxing/unboxing steps. That is,
if you can guess the semantics of that intermediate language, because