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© 2006-2015 Andrew Cooke (site) / post authors (content).

40-fold Speedup in LEPL Parsing

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

Date: Fri, 6 Mar 2009 19:51:54 -0300 (CLST)

OK, so this was a somewhat extreme test case, but by rearranging the
grammar I can make it more likely to terminate and so avoid lots of
pointless recursion.  Here's the code that does the rewriting - I'm
pleased at how simple it is, after all the work making matchers graphs,

def optimize_or(graph):
  When a left-recursive rule is used, it is much more efficient
  if it appears last in an `Or` statement, since that forces the
  alternates (which correspond to the terminating case in a
  recursive function) to be tested before the LMemo limit is

  This rewriting may change the order in which different results
  for an ambiguous grammar are returned.
  for delayed in [x for x in preorder(graph)
                  if type(x) is Delayed]:
    for loop in loops(delayed):
      for i in range(len(loop)):
        if isinstance(loop[i], Or):
          # we cannot be at the end of the list here,
          # since that is a Delayed instance
          matchers = loop[i].matchers
          target = loop[i+1]
          # move target to end of list
          index = matchers.index(target)
          del matchers[index]
  return graph

Even on a more reasonable case (the LMemo example in the docs), the
speedup is about a factor of 2 (that includes some additional tweaks

It's surprising how uniform the profiles are (the flip side of that is
that there's little to optimise from a code-tweaking pov).  The only other
improvement I can think of at the moment is adding a tokenizer (which will
allow LMemo lookahead to use larger "chunks", advancing by words rather
than letters).


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