[Paper Review] From Theory to Practice: Plug and Play with Succinct Data Structures

Simon Gog, Timo Beller+2 more|arXiv (Cornell University)|Nov 5, 2013

Algorithms and Data Compression9 references4 citations

TL;DR

This paper introduces sdsl-lite, an open-source C++ library that enables modular, composable, and highly optimized implementation of succinct data structures. By providing configurable components for bitvectors, wavelet trees, and compressed suffix arrays—with support for hugepages and detailed instrumentation—it allows researchers to efficiently prototype, benchmark, and analyze space-time trade-offs in succinct data structures, significantly reducing implementation overhead and improving reproducibility in experimental evaluations.

ABSTRACT

Engineering efficient implementations of compact and succinct structures is a time-consuming and challenging task, since there is no standard library of easy-to- use, highly optimized, and composable components. One consequence is that measuring the practical impact of new theoretical proposals is a difficult task, since older base- line implementations may not rely on the same basic components, and reimplementing from scratch can be very time-consuming. In this paper we present a framework for experimentation with succinct data structures, providing a large set of configurable components, together with tests, benchmarks, and tools to analyze resource requirements. We demonstrate the functionality of the framework by recomposing succinct solutions for document retrieval.

Motivation & Objective

• To address the lack of a standardized, modular, and composable framework for implementing succinct data structures (SDS) in practice.
• To reduce the time and complexity of implementing and benchmarking new theoretical SDS proposals by providing a reusable, optimized, and well-tested codebase.
• To enable fair, reproducible, and detailed empirical evaluation of SDSs through automated testing, visualization, and resource tracking tools.
• To support large-scale data processing by integrating low-level system optimizations such as hardware popcount and hugepage memory management.
• To facilitate rapid prototyping and exploration of design alternatives across the SDS stack, from bitvectors to complex structures like compressed suffix trees.

Proposed method

• The framework provides a modular, plug-and-play architecture where components like bitvectors, wavelet trees, and compressed suffix arrays can be composed and substituted with minimal code changes.
• It includes optimized, configurable implementations of foundational operations: rank, select, and access on bitvectors, with support for compressed and uncompressed variants.
• The library integrates hardware-aware optimizations, including use of hardware popcount instructions and support for hugepages to reduce address translation overhead during construction.
• It provides automated instrumentation tools such as memory_monitor to visualize space and time consumption during construction and query phases.
• The framework supports both in-memory and semi-external construction, with efficient serialization and deserialization routines for persistent storage of SDSs.
• Comprehensive test suites and benchmarks are included to ensure correctness and enable performance comparisons across different configurations and input types.

Experimental results

Research questions

• RQ1 How can researchers efficiently prototype and evaluate new succinct data structure designs without reimplementing low-level components from scratch?
• RQ2 To what extent do low-level system optimizations like hugepages improve the performance of large-scale succinct data structure construction?
• RQ3 Can a modular, composable library improve the reproducibility and fairness of empirical evaluations in succinct data structures research?
• RQ4 How do different combinations of wavelet tree shapes and bitvector implementations affect the space and time efficiency of complex SDSs like compressed suffix arrays?
• RQ5 What insights can be gained from visualizing memory and time consumption during the construction of large SDSs?

Key findings

• Using 1GB hugepages reduced construction time by 24% for a 1GB prefix of the enwiki-big corpus and by 35% for a 5GB prefix, demonstrating significant performance gains at scale.
• The construction of wavelet trees accounted for only 4% of the total index construction cost, indicating that optimizing this phase offers limited overall benefit.
• The library’s visualization tools enabled detailed insight into resource usage, revealing that address translation overhead becomes a bottleneck when using standard 4KB pages at scale.
• The sdsl-lite library supports both character and word-based inputs, enabling broad applicability across information retrieval and bioinformatics workloads.
• The modular design allowed for rapid recomposition and re-evaluation of document retrieval systems, demonstrating its utility in exploring time-space trade-offs.
• The inclusion of automated tests and benchmarks ensures consistency and reproducibility, lowering the barrier to entry for new researchers in the field.