docs: design PDL NumPy and CUDA integration - #1303
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Describe realistic compatibility routes, direct CUDA fallback, NumPy interoperability, performance break-even measurements, milestones, and handoff. Generated with [OpenAI Codex](https://openai.com/codex) Co-Authored-By: OpenAI Codex <175728472+openai-codex[bot]@users.noreply.github.com>
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Summary
Adds a design proposal for numerical performance and interoperability in PerlOnJava, covering PDL, direct CUDA, NumPy-compatible arrays and interchange, optional Python/NumPy bridging, and a measurable performance strategy that accounts for JVM warmup.
The design includes:
.npyinterchange, DLPack considerations, and an optional persistent CPython bridge;Validation
make check-links— 504 links checked, 0 errors.Documentation-only change; no source or runtime tests are required.