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Cheaper by the Batch: Shared Traversal for Genotype Graph Editing — arXiv:2608.26488

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.26488
License (as archived): CC0-1.0
Archived: 2026-10-17T19:19:12.490Z
SHA-256 of the archived text: 9a43aa746160330093cbcf4d89f18484559b672a0fa49319498c65c8f0550371
Stable URL of this page: https://metraipolis.com/documento/dc_ba950bcfae83

How to cite

Cheaper by the Batch: Shared Traversal for Genotype Graph Editing — arXiv:2608.26488 — arXiv (en), https://arxiv.org/abs/2608.26488 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ba950bcfae83 (Metraipolis archive dc_ba950bcfae83, sha256 9a43aa746160330093cbcf4d89f18484559b672a0fa49319498c65c8f0550371)

BibTeX

@misc{dcba950bcfae83,
  title = {Cheaper by the Batch: Shared Traversal for Genotype Graph Editing — arXiv:2608.26488},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.26488},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ba950bcfae83; sha256 9a43aa746160330093cbcf4d89f18484559b672a0fa49319498c65c8f0550371},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.26488. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Updating a graph by inserting or replacing nodes while preserving semantics and reusing existing structure is a recurring computational problem. In population genetics, this problem arises in the genotype representation graph (GRG), a directed acyclic graph that losslessly encodes phased genetic variation across hundreds of thousands of samples by sharing subgraph structure for individual mutations. In a GRG, each mutation's carrier set is implicitly encoded as the set of leaf nodes reachable from the node it is assigned to. Updating a mutation is therefore a structural editing problem, and current approaches remap mutations individually. This paper introduces a batched mutation-remapping algorithm that replaces independent reuse-aware traversals with a single shared reverse-topological pass, identifying reuse candidates for an entire batch at once. The pass propagates compact bit-parallel per-mutation state and uses an adaptive sparse/dense carrier set representation spanning rare-to-common variant densities. Batching is the memory-scalable complement to split-based parallelism, which instead replicates graph and traversal state per worker. Our remapping is evaluated on a controlled update workload and on end-to-end allele polarization, a bulk carrier set update that is common in population genetic analysis. Our approach is up to 10.5$\times$ faster than independent remapping while preserving exact carrier-set semantics.

Tema degli esploratori: matematica e informatica teorica (retro-catalogo arXiv: annunci del 2026-08-28 in cs.DS).
Categorie arXiv: Data Structures and Algorithms (cs.DS) ; Populations and Evolution (q-bio.PE).
Pagina del lavoro: https://arxiv.org/abs/2608.26488 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T19:16:36.838Z dalla pagina https://arxiv.org/catchup/cs.DS/2026-08-28?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).