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As the hardware found within data centers becomes more heterogeneous, it is important to allow for efficient execution of algorithms across architectures. We present RAPID, a high-level programming language and combined imperative and declarative model for functionally- and performance-portable execution of sequential pattern-matching applications across CPUs, GPUs, Field-Programmable Gate Arrays (FPGAs), and Micron’s D480 AP. RAPID is clear, maintainable, concise, and efficient both at compile and run time. Language features, such as code abstraction and parallel control structures, map well to pattern-matching problems, providing clarity and maintainability. For generation of efficient runtime code, we present algorithms to convert RAPID programs into finite automata. Our empirical evaluation of applications in the ANMLZoo benchmark suite demonstrates that the automata processing paradigm provides an abstraction that is portable across architectures. We evaluate RAPID programs against custom, baseline implementations previously demonstrated to be significantly accelerated. We also find that RAPID programs are much shorter in length, are expressible at a higher level of abstraction than their handcrafted counterparts, and yield generated code that is often more compact.

We present MNCaRT, a comprehensive software ecosystem for the study and use of automata processing across hardware platforms. Tool support includes manipulation of automata, execution of complex machines, high-speed processing of NFAs and DFAs, and compilation of regular expressions. We provide engines to execute automata on CPUs (with VASim and Intel Hyperscan), GPUs (with custom DFA and NFA engines), and FPGAs (with an HDL translator). We also introduce MNRL, an open-source, general-purpose and extensible state machine representation language developed to support MNCaRT. The representation is flexible enough to support traditional finite automata (NFAs, DFAs) while also supporting more complex machines, such as those which propagate multi-bit signals between processing elements. We hope that our ecosystem and representation language stimulates new efforts to develop efficient and specialized automata processing applications.