Modern Datalog on the GPU
arXiv (Cornell University)(2023)
摘要
Modern deductive database engines (e.g., LogicBlox and Soufflé) enable
their users to write declarative queries which compute recursive deductions
over extensional data, leaving their high-performance operationalization (query
planning, semi-naïve evaluation, and parallelization) to the engine. Such
engines form the backbone of modern high-throughput applications in static
analysis, security auditing, social-media mining, and business analytics.
State-of-the-art engines are built upon nested loop joins over explicit
representations (e.g., BTrees and tries) and ubiquitously employ range indexing
to accelerate iterated joins. In this work, we present GDlog: a GPU-based
deductive analytics engine (implemented as a CUDA library) which achieves
significant performance improvements (5–10x or more) versus prior systems.
GDlog is powered by a novel range-indexed SIMD datastructure: the hash-indexed
sorted array (HISA). We perform extensive evaluation on GDlog, comparing it
against both CPU and GPU-based hash tables and Datalog engines, and using it to
support a range of large-scale deductive queries including reachability, same
generation, and context-sensitive program analysis. Our experiments show that
GDlog achieves performance competitive with modern SIMD hash tables and beats
prior work by an order of magnitude in runtime while offering more favorable
memory footprint.
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关键词
gpu-accelerated
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