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An Eight-Dimensional Systematic Evaluation of Optimized Search Algorithms on Modern Processors

Summary: Evaluates hardware-aware search algorithms (sequential, binary, k-ary) across an eight-dimensional optimization space on modern CPUs. Finds no universal winner; offers hardware- and size-driven guidance on variants to deploy for efficient index/search. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11642
Venue
VLDB
Year
2018
Pagerank
5.0546366e-05
Overall Rank
6,441 | 55.24%
DOI
10.14778/3236187.3236205

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
1,438 Benchmarking Learned Indexes 2021 VLDB 0.00011965956
9,745 Why Are Learned Indexes So Effective but Sometimes Ineffective? 2025 VLDB 4.2856385e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
101 The Case for Learned Index Structures 2018 SIGMOD 0.00049778866
104 Making B+-Trees Cache Conscious in Main Memory 2000 SIGMOD 0.00049475932
343 Implementing Database Operations Using SIMD Instructions 2002 SIGMOD 0.00026756534
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