TY - GEN
T1 - GRAM
T2 - 15th IFIP International Conference on Network and Parallel Computing, NPC 2018
AU - Li, Wenke
AU - Shi, Xuanhua
AU - Huang, Hong
AU - Zhao, Peng
AU - Jin, Hai
AU - Dai, Dong
AU - Chen, Yong
N1 - Publisher Copyright:
© IFIP International Federation for Information Processing 2018.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - GPU
KW - Graph traversal
KW - Property graph
KW - Rich metadata management
UR - http://www.scopus.com/inward/record.url?scp=85059663845&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-05677-3_7
DO - 10.1007/978-3-030-05677-3_7
M3 - Conference contribution
AN - SCOPUS:85059663845
SN - 9783030056766
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 77
EP - 89
BT - Network and Parallel Computing - 15th IFIP WG 10.3 International Conference, NPC 2018, Proceedings
A2 - Snir, Marc
A2 - Valero, Mateo
A2 - Zhang, Feng
A2 - Kasahara, Hironori
A2 - Zhai, Jidong
A2 - Jin, Hai
PB - Springer-Verlag
Y2 - 29 November 2018 through 1 December 2018
ER -