Micro‐ and macro‐optimizations of SaaT search

By Andrew Trotman, Matt Crane. 2019

17 citations recorded by the source corpus.

We introduce and test several micro‐ and macro‐optimizations to the Score‐at‐a‐Time approach to processing impact‐ordered postings lists in a search engine. Our micro‐optimizations are at the single‐assembly instruction level, but our macro‐optimizations are algorithmic. Overall, we see an improvement of 37% on our baseline (22% on state of the art). We experiment with parallel search and present evidence that we have hit the memory wall.

Subfields: Information Systems, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Jimmy J. Lin
2. Joel M. Mackenzie
3. Andrew Trotman
4. Chris Kamphuis
5. A. D. Vries
6. Craig Macdonald
7. Antonio Mallia
8. Michal Siedlaczek

In Signal Processing

1. Jimmy J. Lin
2. Joel M. Mackenzie
3. Andrew Trotman
4. Craig Macdonald
5. A. D. Vries
6. Matt Crane

Citations in the corpus

• Supporting Interoperability Between Open-Source Search Engines with the Common Index File Format (2020, methodology): “The Terrier wrapper we have implemented for the Lucene IndexReader API allows a Terrier postings list iterator to directly Results on Robust04 are shown in Table 1, where it is now possible”