Approximate pattern matching (APM) has been widely used in big data applications, e.g., genome data analysis, speech recognition, fraud detection, computer vision, etc. Although an automata-based approach is an efficient way to realize APM, the inherent sequentiality of automata deters its implementation on general-purpose parallel platforms, e.g., multicore CPUs and many-core GPUs. Recently, however, Micron has proposed its Automata Processor (AP), a processing-in-memory (PIM) architecture dedicated for non-deterministic automata (NFA) simulation. It has nominally achieved thousands-fold speedup over a multicore CPU for many big data applications. Alas, the AP ecosystem suffers from two major problems. First, the current APIs of AP require manual manipulations of all computational elements. Second, multiple rounds of time-consuming compilation are needed for large datasets. Both problems hinder programmer productivity and end-to-end performance. Therefore, we propose a paradigm-based approach to hierarchically generate automata on AP and use this approach to create Robotomata, a framework for APM on AP. By taking in the following inputs — the types of APM paradigms, desired pattern length, and allowed number of errors as input — our framework can generate the optimized APM-automata codes on AP, so as to improve programmer productivity. The generated codes can also maximize the reuse of pre-compiled macros and significantly reduce the time for reconfiguration. We evaluate Robotomata by comparing it to two state-of-the-art APM implementations on AP with real-world datasets. Our experimental results show that our generated codes can achieve up to 30.5x and 12.8x speedup with respect to configuration while maintaining the computational performance. Compared to the counterparts on CPU, our codes achieve up to 393x overall speedup, even when including the reconfiguration costs. We highlight the importance of counting the configuration time towards the overall performance on AP, which would provide better insight in identifying essential hardware features, specifically for large-scale problem sizes.
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