MicroSpec: Speculation-centric fine-grained parallelization for FSM computations

Abstract

Finite state machines (FSMs) are basic computation models that play essential roles in many applications. Enabling efficient parallel FSM execution is critical to the performance of these applications. However, they are very challenging to parallelize due to their inherent data dependencies that occur at each step of computations. Existing efforts on FSM parallelization either explore coarse-grained speculative parallelism or leverage parallel prefixsum. The former ignores prevalent fine-grained hardware parallelism on modern processors (such as ILP or SIMD parallelism) while the latter limits the benefits of fine-grained parallelism mainly to state enumeration. This work presents MicroSpec, a set of parallelization techniques that, for the first time, expose fine-grained speculative parallelism to FSM computations. Based on a rigorous analysis of three types of parallelism at fine-grained level, MicroSpec consists of a list of four fine-grained speculative parallelization approaches along with a speculation-oriented data transformation. Experiments on a large set of realworld FSM benchmarks show that MicroSpec achieves substantial performance improvement over the state-of-the-art.