Characterizing and Mitigating Output Reporting Bottlenecks in Spatial Automata Processing Architectures

Abstract

Automata processing has seen a resurgence in importance due to its usefulness for pattern matching and pattern mining of "big data." While large-scale automata processing is known to bottleneck von Neumann processors due to unpredictable memory accesses, spatial architectures excel at automata processing. Spatial architectures can implement automata graphs by wiring together automata states in reconfigurable arrays, allowing parallel automata state computation, and point-to-point state transitions on-chip. However, spatial automata processing architectures can suffer from output constraints (up to 255x in commercial systems!) due to the physical placement of states, output processing architecture design, I/O resources, and the massively parallel nature of the architecture. To understand this bottleneck, we conduct the first known characterization of output requirements of a realistic set of automata processing benchmarks. We find that most benchmarks report fairly frequently, but that few states report at any one time. This observation motivates new output compression schemes and reporting architectures. We evaluate the benefit of one purely software automata transformation and show that output reporting costs can be greatly reduced (improving performance by up to 40% without hardware modification. We then explore bottlenecks in the reporting architecture of a commercial spatial automata processor and propose a new architecture that improves performance by up to 5.1x.