CPU-GPU异构环境下的大规模商品知识查询处理  

Query processing of large-scale product knowledge in a CPU-GPU heterogeneous environment

作　　者：方创新 宋浩 林煜明[1] 周娅[1] FANG Chuangxin;SONG Hao;LIN Yuming;ZHOU Ya(Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin Guangxi 541004,China)

机构地区：[1]桂林电子科技大学广西可信软件重点实验室,广西桂林541004

出　　处：《华东师范大学学报（自然科学版）》2021年第5期157-168,共12页Journal of East China Normal University(Natural Science)

基　　金：国家自然科学基金(62062027,U1811264);广西自然科学基金(2018GXNSFDA281049,2020GXNSFA A159012);广西创新驱动发展专项资金(桂科AA19046004);桂林市重点研发计划(2020010304);桂林电子科技大学研究生教育创新计划资助项目(2021YCXS075);广西可信软件重点实验室研究课题(kx202021)。

摘　　要：知识图谱是将无结构的知识进行结构化表示和组织的有效途径,已经成为支持众多智能应用的基础设施.然而,与商品相关的知识通常呈现出海量性、异质性和层次性的特点,这对现有基于关系模型和图模型的知识查询处理方法提出了挑战.针对商品知识的这些特点,本文设计与实现了一种利用CPU和GPU协同计算的商品知识查询处理方法.首先,为了充分发挥GPU的并行计算能力,提出了一种基于稀疏矩阵的商品知识存储策略,并针对商品知识进行存储优化;其次,根据稀疏矩阵的存储结构设计了一种查询转换方式,将SPARQL查询转化为对应的矩阵计算,并将连接查询算法扩展到GPU上进行加速.为了验证所提出方法的有效性,我们在LUBM数据集和一个半合成的商品数据集上进行了一系列的实验.结果表明,本文提出的方法,不仅在海量商品知识下相对于现有RDF查询引擎在检索效率上有较大提升,而且在通用的RDF标准数据集上也能取得较好的检索性能,并验证了GPU加速查询处理的有效性.Knowledge graphs are an effective way to structurally represent and organize unstructured knowledgeare;in fact, these graphs are commonly used to support many intelligent applications. However,product-related knowledge is typically massive in scale, heterogeneous, and hierarchical;these characteristics present a challenge for traditional knowledge query processing methods based on relational and graph models. In this paper, we propose a solution to address these challenges by designing and implementing a product knowledge query processing method using CPU and GPU collaborative computing.Firstly, in order to leverage the full parallel computing capability of GPU, a product knowledge storage strategy based on a sparse matrix is proposed and optimized for the scale of the task. Secondly, based on the storage structure of the sparse matrix, a query conversion method is designed, which transforms the SPARQL query into a corresponding matrix calculation, and extends the join query algorithm to the GPU for acceleration. In order to verify the effectiveness of the proposed method, we conducted a series of experiments on an LUBM dataset and a semisynthetic dataset of products. The experimental results showed that the proposed method not only improves retrieval efficiency for large-scale product knowledge datasets compared with existing RDF query engines, but also achieves better retrieval performance on a general RDF standard dataset.

关 键 词：商品知识 异构环境 RDF数据 查询处理 

分 类 号：TP392[自动化与计算机技术—计算机应用技术]