DocumentCode :
3682577
Title :
Performance Characterization of High-Level Programming Models for GPU Graph Analytics
Author :
Yuduo Wu;Yangzihao Wang;Yuechao Pan;Carl Yang;John D. Owens
Author_Institution :
Electr. &
fYear :
2015
Firstpage :
66
Lastpage :
75
Abstract :
We identify several factors that are critical to high-performance GPU graph analytics: efficient building block operators, synchronization and data movement, workload distribution and load balancing, and memory access patterns. We analyze the impact of these critical factors through three GPU graph analytic frameworks, Gun rock, Map Graph, and VertexAPI2. We also examine their effect on different workloads: four common graph primitives from multiple graph application domains, evaluated through real-world and synthetic graphs. We show that efficient building block operators enable more powerful operations for fast information propagation and result in fewer device kernel invocations, less data movement, and fewer global synchronizations, and thus are key focus areas for efficient large-scale graph analytics on the GPU.
Keywords :
"Graphics processing units","Computational modeling","Roads","Programming","Synchronization","Topology","Runtime"
Publisher :
ieee
Conference_Titel :
Workload Characterization (IISWC), 2015 IEEE International Symposium on
Type :
conf
DOI :
10.1109/IISWC.2015.13
Filename :
7314148
Link To Document :
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