CCDR-PAID: more efficient cache-conscious PAID algorithm by data reconstruction

Authors:
Yuki Matsubara;Jun Miyazaki;Goshiro Yamamoto;Yuki Uranishi;Sei Ikeda;Hirokazu Kato
Affiliations:
Nara Institute of Science and Technology, Takayama, Ikoma, Nara, Japan;Nara Institute of Science and Technology, Takayama, Ikoma, Nara, Japan;Nara Institute of Science and Technology, Takayama, Ikoma, Nara, Japan;Nara Institute of Science and Technology, Takayama, Ikoma, Nara, Japan;Nara Institute of Science and Technology, Takayama, Ikoma, Nara, Japan;Nara Institute of Science and Technology, Takayama, Ikoma, Nara, Japan
Venue:
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Year:
2012

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Abstract

In this paper, we propose the CCDR-PAID algorithm which is a technique to improve CPU cache utilization of sequential pattern mining more efficiently as an extension of existing CC-PAID which is our previous work. Compared to PAID, CC-PAID improves temporal locality by changing the access pattern to data structures and processing multiple sequential patterns with a common prefix at a time to reduce the memory access latency by suppressing CPU cache misses. In this paper, we extend CC-PAID and dynamically reconstruct data structures, so that unnecessary data access to them can be avoided. The experimental results showed that CCDR-PAID executes up to 25% faster than CC-PAID because it sufficiently reduces cache misses and, therefore, provides better CPU cache utilization due to compaction of the data structure.