Title :
Optimizing CPU cache performance for Pregel-like graph computation
Author :
Songjie Niu ; Shimin Chen
Author_Institution :
State Key Lab. of Comput. Archit., Inst. of Comput. Technol., Beijing, China
Abstract :
In-memory graph computation systems have been used to support many important applications, such as PageRank on the web graph and social network analysis. In this paper, we study the CPU cache performance of graph computation. We have implemented a graph computation system, called GraphLite, in C/C++ based on the description of Pregel. We analyze the CPU cache behavior of the internal data structures and operations of graph computation. Then we exploit CPU cache prefetching techniques to improve the cache performance. Real machine experimental results show that our solution achieves 1.9-2.2x speedups compared to the baseline implementation.
Keywords :
C++ language; cache storage; graph theory; C/C++ language; CPU cache performance; GraphLite; Pregel-like graph computation; in-memory graph computation systems; Aggregates; Arrays; Computational modeling; Prefetching; Programming; Web pages;
Conference_Titel :
Data Engineering Workshops (ICDEW), 2015 31st IEEE International Conference on
Conference_Location :
Seoul
DOI :
10.1109/ICDEW.2015.7129568