1 research outputs found
A Speculative Parallel DFA Membership Test for Multicore, SIMD and Cloud Computing Environments
We present techniques to parallelize membership tests for Deterministic
Finite Automata (DFAs). Our method searches arbitrary regular expressions by
matching multiple bytes in parallel using speculation. We partition the input
string into chunks, match chunks in parallel, and combine the matching results.
Our parallel matching algorithm exploits structural DFA properties to minimize
the speculative overhead. Unlike previous approaches, our speculation is
failure-free, i.e., (1) sequential semantics are maintained, and (2)
speed-downs are avoided altogether. On architectures with a SIMD
gather-operation for indexed memory loads, our matching operation is fully
vectorized. The proposed load-balancing scheme uses an off-line profiling step
to determine the matching capacity of each par- ticipating processor. Based on
matching capacities, DFA matches are load-balanced on inhomogeneous parallel
architectures such as cloud computing environments. We evaluated our
speculative DFA membership test for a representative set of benchmarks from the
Perl-compatible Regular Expression (PCRE) library and the PROSITE protein
database. Evaluation was conducted on a 4 CPU (40 cores) shared-memory node of
the Intel Manycore Testing Lab (Intel MTL), on the Intel AVX2 SDE simulator for
8-way fully vectorized SIMD execution, and on a 20-node (288 cores) cluster on
the Amazon EC2 computing cloud