C[omp]ute

From: "andrew cooke" <andrew@...>

Date: Sat, 4 Apr 2009 09:20:37 -0400 (CLT)

I just finished some initial tests on "compiling" the recursive descent
parser in LEPL to a discrete finite automata (DFA) using regular
expressions.

There are some limitations, of course - I only change the lower parts of
the tree that match characters.  This is not quite as obvious as it may
sound because my regular expression engine can handle arbitrary Python
objects, so regular expressions do not have to be made of letters.  But I
do need to write the conversion from matcher to regular expression for
each matcher, and currently only handle And, Or, Any, Literal and some
calls to DepthFirst (which is the core repetition matcher).

But even that explanation is not complete, because those matchers are
actually a large fraction of what is used in most parsers (LEPL provides
many more matchers, but they are sugar built on top of these).  In
practice the biggest problem is that arbitrary transforms (functions) can
be invoked on the results as they are generated.

I ameliorated the effect of actions by making composition explicit -
composite actions are now available for inspection internally as lists of
functions, and the regular expression rewriting engine makes use of this
to identify "add" (the function used to combine strings).

Another limitation is that the fastest regular expression engine gives
only a single greedy match.  But a second engine, using a pushdown
automaton, is nearly as fast (see results below) and provides all possible
matches.


Anyway, as an example, here is the regular expression that is
auto-generated for the Float() matcher:
([\+\-]|)([0-9]([0-9])*(\.|)|([0-9]([0-9])*|)\.[0-9]([0-9])*)([Ee]([\+\-]|)[0-9]([0-9])*|)


Note that the code would be even faster if people used the Regexp()
matcher to provide a regular expression directly (which uses Python's fast
"re" library), but then you start to lose some of the other advantages of
LEPL (you only get the greedy match, the syntax is uglier, reuse is
harder).

Even then, I could replace my "greedy" engine with Python's (and keep the
automatic rewriting).  In practice, I don't do that because (1) the regexp
syntax I use is simpler and easier to target and (2) my engine works with
streams of data, while Python's requires (as far as I can tell) that the
string be in-memory (in theory you can use my regexp to parse a file that
is larger than the memory available to Python; testing large files is
still on my todo list).


Anyway, to the performance tests.  I used my standard expressions example,
but "spiced up" to add some complexity (yes, this improves the results
below).  So instead of matching integers I match float values (including
exponents).

The expression to match is '1.2e3 + 2.3e4 * (3.4e5 + 4.5e6 - 5.6e7)'

The results are (in arbitrary units):
Default config: 5.8
NFA (slower pushdown) regexp: 2.9
DFA (faster greedy) regexp: 2.8

So the parser is "twice as fast".  Note that this is only timing for
parsing - rewriting the parser will take more time with the extra
rewriting (I haven't measured it, and it's not noticeable in use, but it
must take more).


In summary the following aspects of LEPL's design helped here:
- Using a small core of matchers (with syntactic sugar on top)
- Exposing the DAG of matchers for rewriting before use
- Exposing composed actions to rewriting

Andrew