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In this paper, we propose several optimizations for the SFA constructionalgorithm, which greatly reduce the in-memory footprint and the processingsteps required to construct an SFA. We introduce fingerprints as a space- andtime-efficient way to represent SFA states. To compute fingerprints, we applythe Barrett reduction algorithm and accelerate it using recent additions to thex86 instruction set architecture. We exploit fingerprints to introduce hashingfor further optimizations. Our parallel SFA construction algorithm isnonblocking and utilizes instruction-level, data-level, and task-levelparallelism of coarse-, medium- and fine-grained granularity. We adapt staticworkload distributions and align the SFA data-structures with the constraintsof multicore memory hierarchies, to increase the locality of memory accessesand facilitate HW prefetching. We conduct experiments on the PROSITE proteindatabase for FAs of up to 702 FA states to evaluate performance andeffectiveness of our proposed optimizations. Evaluations have been conducted ona 4 CPU (64 cores) AMD Opteron 6378 system and a 2 CPU (28 cores, 2hyperthreads per core) Intel Xeon E5-2697 v3 system. The observed speedups overthe sequential baseline algorithm are up to 118541x on the AMD system and2113968x on the Intel system.

Efficient Construction of Simultaneous Deterministic Finite Automata on Multicores Using Rabin Fingerprints