Cheaper by the Batch: Shared Traversal for Genotype Graph Editing

2026-08-26

AI Summary

本文提出了一种批量突变重映射算法，通过共享反向拓扑遍历提高基因型图编辑效率，解决在更新突变时如何高效复用子图结构的问题。

Abstract

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.

Problem

• Genotype Representation Graph
• Mutation Remapping
• Graph Editing
• Batch Processing

Innovation

• batched mutation-remapping
• shared reverse-topological pass
• bit-parallel per-mutation state
• adaptive sparse/dense carrier set representation