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JavaScript Object Notation (JSON) and its variants have gained great popularity in recent years. Unfortunately, the performance of their analytics is often dragged down by the expensive JSON parsing. To address this, recent work has shown that building bitwise indices on JSON data, called structural indices , can greatly accelerate querying. Despite its promise, the existing structural index construction does not scale well as records become larger and more complex, due to its (inherently) sequential construction process and the involvement of costly memory copies that grow as the nesting level increases. To address the above issues, this work introduces Pison - a more memory-efficient structural index constructor with supports of intra-record parallelism. First, Pison features a redesign of the bottleneck step in the existing solution. The new design is not only simpler but more memory-efficient. More importantly, Pison is able to build structural indices for a single bulky record in parallel, enabled by a group of customized parallelization techniques. Finally, Pison is also optimized for better data locality, which is especially critical in the scenario of bulky record processing. Our evaluation using real-world JSON datasets shows that Pison achieves 9.8X speedup (on average) over the existing structural index construction solution for bulky records and 4.6X speedup (on average) of end-to-end performance (indexing plus querying) over a state-of-the-art SIMD-based JSON parser on a 16-core machine. 

Finite-state machine (FSM) is a fundamental computation model used by many applications. However, FSM execution is known to be “embarrassingly sequential” due to the state dependences among transitions. Existing solutions leverage enumerative or speculative parallelization to break the dependences. However, the efficiency of both parallelization schemes highly depends on the properties of the FSM and its inputs. For those exhibiting unfavorable properties, the former suffers from the overhead of maintaining multiple execution paths, while the latter is bottlenecked by the serial reprocessing among the misspeculation cases. Either way, the FSM parallelization scalability is seriously compromised. This work addresses the above scalability challenges with two novel techniques. First, for enumerative parallelization, it proposes path fusion. Inspired by the classic NFA to DFA conversion, it maps a vector of states in the original FSM to a new (fused) state. In this way, path fusion can reduce multiple FSM execution paths into a single path, minimizing the overhead of path maintenance. Second, for speculative parallelization, this work introduces higher-order speculation to avoid the serial reprocessing during validations. This is a generalized speculation model that allows speculated states to be validated speculatively. Finally, this work integrates different schemes of FSM parallelization into a framework—BoostFSM, which automatically selects the best based on the relevant properties of the FSM. Evaluation using real-world FSMs with diverse characteristics shows that BoostFSM can raise the average speedup from 3.1× and 15.4× of the existing speculative and enumerative parallelization schemes, respectively, to 25.8× on a 64-core machine.