Southwest University

Greedy State Boundary Search (GSBS) finds transitions between stable neural activity patterns, but it rebuilt state templates and recomputed correlations for every candidate boundary, making analyses across recordings and settings expensive. HP-GSBS rewrites the matching objective as additive interval contributions and computes t-distance from reusable correlation statistics, adding streaming candidate reduction, sequential local refinement and active-block reuse across CPU, PyTorch CUDA and Triton paths. Across seven controlled workloads with two input realizations each, median same-input speedups range from 154 to 1,730, and all 168 optimized executions reproduce complete reference boundary histories and selected state counts, with a maximum absolute t-distance difference of 1.88×10⁻¹¹.

Greedy State Boundary Search (GSBS) finds transitions between stable neural activity patterns, but it rebuilt state templates and recomputed correlations for every candidate boundary, making analyses across recordings and settings expensive. HP-GSBS rewrites the matching objective as additive interval contributions and computes t-distance from reusable correlation statistics, adding streaming candidate reduction, sequential local refinement and active-block reuse across CPU, PyTorch CUDA and Triton paths. Across seven controlled workloads with two input realizations each, median same-input speedups range from 154 to 1,730, and all 168 optimized executions reproduce complete reference boundary histories and selected state counts, with a maximum absolute t-distance difference of 1.88×10⁻¹¹.