论文标题
HIPERFACT：内存高性能事实处理 - 重新考虑rete推理算法
Hiperfact: In-Memory High Performance Fact Processing -- Rethinking the Rete Inference Algorithm
论文作者
论文摘要
The Rete forward inference algorithm forms the basis for many rule engines deployed today, but it exhibits the following problems: (1) the caching of all intermediate join results, (2) the processing of all rules regardless of the necessity to do so (stemming from the underlying forward inference approach), (3) not defining the join order of rules and its conditions, significantly affecting the final run-time performance, and finally (4) pointer chasing due to the overall网络结构，导致由于随机访问模式引起的CPU库的使用效率低下。 HIPERFACT方法旨在通过（1）在主要等级1事实指数存储和中间联接结果存储水平上选择高效数据结构来克服这些缺点，（2）引入岛屿事实处理，以确定连接顺序，通过确保最小的中间联接结构构建以及（3）允许衍生树来允许启用和平行读取/写入/写入/lyazy lyazy。实验评估表明，实施该方法的HiperFact原型引擎在推理和查询性能方面都取得了重大改进。此外，在综合基准的背景下，将拟议的HiperFact引擎与现有发动机进行了比较。
The Rete forward inference algorithm forms the basis for many rule engines deployed today, but it exhibits the following problems: (1) the caching of all intermediate join results, (2) the processing of all rules regardless of the necessity to do so (stemming from the underlying forward inference approach), (3) not defining the join order of rules and its conditions, significantly affecting the final run-time performance, and finally (4) pointer chasing due to the overall network structure, leading to inefficient usage of the CPU caches caused by random access patterns. The Hiperfact approach aims to overcome these shortcomings by (1) choosing cache efficient data structures on the primary rank 1 fact index storage and intermediate join result storage levels, (2) introducing island fact processing for determining the join order by ensuring minimal intermediate join result construction, and (3) introducing derivation trees to allow for parallel read/write access and lazy rule evaluation. The experimental evaluations show that the Hiperfact prototype engine implementing the approach achieves significant improvements in respect to both inference and query performance. Moreover, the proposed Hiperfact engine is compared to existing engines in the context of a comprehensive benchmark.
