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IIU

Authors :
Tae Jun Ham
Jae-Yeon Won
Shivam Bharuka
Jun Heo
Yejin Lee
Jaeyoung Jang
Jae W. Lee
Source :
ASPLOS
Publication Year :
2020
Publisher :
ACM, 2020.

Abstract

Inverted index serves as a fundamental data structure for efficient search across various applications such as full-text search engine, document analytics and other information retrieval systems. The storage requirement and query load for these structures have been growing at a rapid rate. Thus, an ideal indexing system should maintain a small index size with a low query processing time. Previous works have mainly focused on using CPUs and GPUs to exploit query parallelism while utilizing state-of-the-art compression schemes to fit the index in memory. However, scaling parallelism to maximally utilize memory bandwidth on these architectures is still challenging. In this work, we present IIU, a novel inverted index processing unit, to optimize the query performance while maintaining a low memory overhead for index storage. To this end, we co-design the indexing scheme and hardware accelerator so that the accelerator can process highly compressed inverted index at a high throughput. In addition, IIU provides flexible interconnects between modules to take advantage of both intra- and inter-query parallelism. Our evaluation using a cycle-level simulator demonstrates that IIU provides an average of 13.8\times× query latency reduction and 5.4\times× throughput improvement across different query types, while reducing the average energy consumption by 18.6\times×, compared to Apache Lucene, a production-grade full-text search framework.

Details

Database :
OpenAIRE
Journal :
Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems
Accession number :
edsair.doi...........d8cd8ff663f674e46f0932991ada803c
Full Text :
https://doi.org/10.1145/3373376.3378521