  Predictive shift-reduce (PSR) parsing for hyperedge replacement (HR) grammars
is very efficient, but restricted to a subclass of unambiguous HR grammars. To
overcome this restriction, we have recently extended PSR parsing to generalized
PSR (GPSR) parsing along the lines of Tomita-style generalized LR parsing.
Unfortunately, GPSR parsers turned out to be too inefficient without manual
tuning. This paper proposes to use memoization techniques to speed up GPSR
parsers without any need of manual tuning, and which has been realized within
the graph parser distiller Grappa. We present running time measurements for
some example languages; they show a significant speed up by some orders of
magnitude when parsing valid graphs. But memoization techniques do not help
when parsing invalid graphs or if all parses of an ambiguous input graph shall
be determined.
