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Efficient Top- k query evaluation relies on practices that utilize auxiliary data structures to enable early termination. Such techniques were designed to trade-off complex work in the buffer pool against costly access to disk-resident data. Parallel in-memory Top- k selection with support for early termination presents a novel challenge because computation shifts higher up in the memory hierarchy. In this environment, data scan methods using SIMD instructions and multithreading perform well despite requiring evaluation of the complete dataset. Early termination schemes that favor simplicity require random access to resolve score ambiguity while those optimized for sequential access incur too many object evaluations. In this work, we introduce the concept of rank uncertainty , a measure of work efficiency that enables classifying existing solutions according to their potential for efficient parallel in-memory Top-fc selection. We identify data reordering and layering strategies as those having the highest potential and provide practical guidelines on how to adapt them for parallel in-memory execution (creating the VTA and SLA approaches). In addition, we show that the number of object evaluations can be further decreased by combining data reordering with angle space partitioning (introducing PTA). Our extensive experimental evaluation on varying query parameters using both synthetic and real data, showcase that PTA exhibits between 2 and 4 orders of magnitude better query latency, and throughput when compared to prior work and our optimized algorithmic variants (i.e. VTA, SLA). |
