%0 Conference Proceedings
%T GRAM: A GPU-Based Property Graph Traversal and Query for HPC Rich Metadata Management
%+ Services Computing Technology and System Lab [HUST]
%+ Texas Tech University [Lubbock] (TTU)
%A Li, Wenke
%A Shi, Xuanhua
%A Huang, Hong
%A Zhao, Peng
%A Jin, Hai
%A Dai, Dong
%A Chen, Yong
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 15th IFIP International Conference on Network and Parallel Computing (NPC)
%C Muroran, Japan
%Y Feng Zhang
%Y Jidong Zhai
%Y Marc Snir
%Y Hai Jin
%Y Hironori Kasahara
%Y Mateo Valero
%I Springer International Publishing
%3 Network and Parallel Computing
%V LNCS-11276
%P 77-89
%8 2018-11-29
%D 2018
%R 10.1007/978-3-030-05677-3_7
%K Rich metadata management
%K Property graph
%K Graph traversal
%K GPU
%Z Computer Science [cs]Conference papers
%X In HPC systems, rich metadata are defined to describe rich information about data files, like the executions that lead to the data files, the environment variables, and the parameters of all executions, etc. Recent studies have shown the feasibility of using property graph to model rich metadata and utilizing graph traversal to query rich metadata stored in the property graph. We propose to utilize GPU to process the rich metadata graphs. There are generally two challenges to utilize GPU for metadata graph query. First, there is no proper data representation for the metadata graph on GPU yet. Second, there is no optimization techniques specifically for metadata graph traversal on GPU neither. In order to tackle these challenges, we propose GRAM, a GPU-based property graph traversal and query framework. GRAM uses GPU to express metadata graph in Compressed Sparse Row (CSR) format, and uses Structure of Arrays (SoA) layout to store properties. In addition, we propose two new optimizations, parallel filtering and basic operations merging, to accelerate the metadata graph traversal. Our evaluation results show that GRAM can be effectively applied to user scenarios in HPC systems, and the performance of metadata management is greatly improved.
%G English
%Z TC 10
%Z WG 10.3
%2 https://inria.hal.science/hal-02279550/document
%2 https://inria.hal.science/hal-02279550/file/477597_1_En_7_Chapter.pdf
%L hal-02279550
%U https://inria.hal.science/hal-02279550
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC10
%~ IFIP-NPC
%~ IFIP-WG10-3
%~ IFIP-LNCS-11276