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Engineering rank queries on bit vectors and strings.

Simon Gene Gottlieb1, Knut Reinert2,3

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Summary
This summary is machine-generated.

This study introduces three optimizations for rank support on Burrows-Wheeler-Transform (BWT) data structures, crucial for genomic data analysis. These improvements enhance space efficiency and query speed for the FM-Index, making genomic data processing faster and more accessible.

Keywords:
Bit vectorFM-IndexRank supportSIMDString

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Area of Science:

  • Computer Science
  • Bioinformatics
  • Data Structures

Background:

  • Rank support on strings is essential for applications like the FM-Index used in genomic data analysis.
  • The FM-Index relies on rank operations on the Burrows-Wheeler-Transform (BWT) for efficient indexing and analysis.
  • Current implementations often use bit vectors with rank support, which can be optimized.

Purpose of the Study:

  • To present three novel implementation improvements for rank support on BWT.
  • To reduce space overhead and enhance the runtime performance of rank queries.
  • To provide more efficient alternatives to existing data structures like Wavelet Trees.

Main Methods:

  • Introduced 'paired-blocks' to halve the space overhead of the support structure to 1.6%.
  • Developed a bit masking method for population count (popcount) to accelerate 512-bit blocks using AVX512 SIMD.
  • Proposed 'flattened bit vectors' (fBV) as a revision of EPR-dictionaries for improved space and speed.

Main Results:

  • Paired-blocks achieved a space overhead of only 1.6%.
  • The popcount masking method significantly improved runtime with AVX512.
  • Flattened bit vectors (fBV) offer competitive size and are 2x-9x faster than Wavelet Trees for rank operations.

Conclusions:

  • The proposed optimizations significantly improve the efficiency of rank support for BWT.
  • These advancements make FM-Index and genomic data analysis more performant and space-efficient.
  • Flattened bit vectors present a compelling alternative to Wavelet Trees for rank queries.