PDL::Tips - Man Page

Use help help within perldl or pdl2 or use the pdldoc program from the command line for access to the PerlDL documentation. HTML versions of the pages should also be present, in the HtmlDocs/PDL directory of the PDL distribution. To find this  directory, try the following

 pdl> foreach ( map{"$_/PDL/HtmlDocs"}@INC ) { p "$_\n" if -d $_ }

The following code normalizes a bunch of vectors in $x. This works regardless of the dimensionality of $x.

        $x /= $x->sumover->dummy(0);

If you want to see what the code is actually doing, try the command

        PDL::Core::set_debugging(1);

somewhere. This spews out a huge amount of debug info for PDL into STDOUT. Plans for the future include making it possible to redirect the output, and also making it possible to select messages with more precision.

Many of the messages come from Basic/Core/pdlapi.c and you can look at the source to see what is going on.

If you have any extra time to work on these mechanisms, inform the pdl-porters mailing list.

If you are running recursively something that selects certain indices of a large ndarray, like

        while(1) {
                $inds = where($x>0);
                $x = $x->index($inds);
                $y = $y->index($inds);
                func($y,$x);
        }

If you are not writing to $y, it saves a lot of memory to change this to

                $y = $y->index($inds)->sever;

The new method sever is a causes the write-back relation to be forgotten. It is like copy except it changes the original ndarray and returns it).

Of course, the probably best way to do the above is

        $inds = xvals ($x->long);
        while(1) {
                $inds0 = where($x>0);
                $inds1 = $inds->index($inds)->sever;
                $x = $a0->index($inds1);
                $y = $y->index($inds1)->sever;
                func($y,$x);
        }

which doesn't save all the temporary instances of $x in memory. See mandel.pl in the Demos subdirectory of the PerlDL distribution for an example.

If you really want to write speedy PP code, the first thing you need to do is to make sure that your C compiler is allowed to do the necessary optimizations.

What this means is that you have to allow as many variables as possible to go into registers:

        loop(a) %{
                $a() += $COMP(foo_member) * $b()
        %}

expands to

        for(i=0; i<10000; i++) {
                a[i] += __privtrans->foo_member * b[i];
        }

is about the worst you can do, since your C compiler is not allowed to assume that a doesn't clobber foo_member which completely inhibits vectorization. Instead, do

        float foo = $COMP(foo_member);
        loop(a) %{
                $a() += foo * $b();
        %}

This is not a restriction caused by PP but by ANSI C semantics. Of course, we could copy the struct into local variables and back but that could cause very strange things sometimes.

There are many other issues on organizing loops.

We are currently planning to make PP able to do fixed-width things as well as physical ndarrays (where looping over the first dimensions would be cheaper as there are less distinct increments, which might make a difference on machines with a small number of registers).