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

TL;DR

This paper presents optimized algorithms for constructing simultaneous deterministic finite automata (SFA) on multicore systems, utilizing fingerprints and parallel processing to significantly improve efficiency and scalability.

Contribution

It introduces a space- and time-efficient fingerprinting method for SFA states and a parallel construction algorithm optimized for multicore architectures.

Findings

Achieved up to 1,185,41x speedup on AMD system

Achieved up to 2,113,968x speedup on Intel system

Demonstrated effective reduction in memory footprint and processing steps

Abstract

In this paper, we propose several optimizations for the SFA construction algorithm, which greatly reduce the in-memory footprint and the processing steps required to construct an SFA. We introduce fingerprints as a space- and time-efficient way to represent SFA states. To compute fingerprints, we apply the Barrett reduction algorithm and accelerate it using recent additions to the x86 instruction set architecture. We exploit fingerprints to introduce hashing for further optimizations. Our parallel SFA construction algorithm is nonblocking and utilizes instruction-level, data-level, and task-level parallelism of coarse-, medium- and fine-grained granularity. We adapt static workload distributions and align the SFA data-structures with the constraints of multicore memory hierarchies, to increase the locality of memory accesses and facilitate HW prefetching. We conduct experiments on the PROSITE protein database for FAs of up to 702 FA states to evaluate performance and effectiveness of our proposed optimizations. Evaluations have been conducted on a 4 CPU (64 cores) AMD Opteron 6378 system and a 2 CPU (28 cores, 2 hyperthreads per core) Intel Xeon E5-2697 v3 system. The observed speedups over the sequential baseline algorithm are up to 118541x on the AMD system and 2113968x on the Intel system.