Morton filters

Author:

Breslow Alex D.1,Jayasena Nuwan S.1

Affiliation:

1. Advanced Micro Devices, Inc.

Abstract

Approximate set membership data structures (ASMDSs) are ubiquitous in computing. They trade a tunable, often small, error rate ( ϵ ) for large space savings. The canonical ASMDS is the Bloom filter, which supports lookups and insertions but not deletions in its simplest form. Cuckoo filters (CFs), a recently proposed class of ASMDSs, add deletion support and often use fewer bits per item for equal ϵ . This work introduces the Morton filter (MF), a novel AS-MDS that introduces several key improvements to CFs. Like CFs, MFs support lookups, insertions, and deletions, but improve their respective throughputs by 1.3x to 2.5x, 0.9x to 15.5x, and 1.3x to 1.6x. MFs achieve these improvements by (1) introducing a compressed format that permits a logically sparse filter to be stored compactly in memory, (2) leveraging succinct embedded metadata to prune unnecessary memory accesses, and (3) heavily biasing insertions to use a single hash function. With these optimizations, lookups, insertions, and deletions often only require accessing a single hardware cache line from the filter. These improvements are not at a loss in space efficiency, as MFs typically use comparable to slightly less space than CFs for the same epsis; .

Publisher

VLDB Endowment

Subject

General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development

Cited by 57 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Aleph Filter: To Infinity in Constant Time;Proceedings of the VLDB Endowment;2024-07

2. Beyond Bloom: A Tutorial on Future Feature-Rich Filters;Companion of the 2024 International Conference on Management of Data;2024-06-09

3. Wormhole Filters: Caching Your Hash on Persistent Memory;Proceedings of the Nineteenth European Conference on Computer Systems;2024-04-22

4. A Micro-architecture that supports the Fano–Elias encoding and a hardware accelerator for approximate membership queries;Microprocessors and Microsystems;2024-03

5. Accelerating BERT inference with GPU-efficient exit prediction;Frontiers of Computer Science;2024-01-22

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