BitPCE: Batched Multi-Dimensional PolicyResolution with GPU Bitmap Algebra

Shengwei Wang

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arXiv · 2610.24974

BitPCE: Batched Multi-Dimensional PolicyResolution with GPU Bitmap Algebra

Shengwei Wang

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Abstract

Policy controllers must determine which workloads match rules spanning several label dimensions. Hash sets, compressed bitmaps, and GPU set-processing primitives address the underlying set operations. However, intermediate results, repeated scope evaluations, dependent policy updates, and host transfers can dominate the cost of a heterogeneous batch. We study this problem as batched intersection-of-unions (IoU). Our design exploits independent scope--word tasks, reusable endpoint expressions, and policy dependencies. The challenges are preserving wildcard, empty-group, and exclusion semantics, recognizing equal scopes, and retaining unchanged recipients affected by source edits. BitPCE addresses them with register-resident GPU evaluation, canonical scope compilation, and dependency-complete candidate selection under a stated rendering contract. IP-only changes reuse cached memberships. We establish correctness and word-work bounds. On a four-vCPU A10G host, resident GPU execution is 3.46--36.60$\times$ faster than the best tested native CPU path, including CRoaring, and 1.14--2.22$\times$ faster than an adapted operand-parallel GPU reduction. Gains narrow at the host-output boundary: on an eight-vCPU host, including upload and compact identifier output, resolution takes 16.3 ms versus 19.3 ms for CPU at 100K objects and 500 scopes. Sparse labels and small batches can favor CPU execution. Scope reuse, candidate selection, and incremental transfers are measured as components; complete update-to-enforcement latency remains unevaluated.

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BibTeX RIS

Shengwei Wang. 2026-10-18. BitPCE: Batched Multi-Dimensional PolicyResolution with GPU Bitmap Algebra. https://arxiv.org/abs/2610.24974

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