/cupynumeric-migration-readiness

name: cupynumeric-migration-readiness
description: Pre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
license: CC-BY-4.0 OR Apache-2.0
compatibility: Knowledge-driven assessment; no cuPyNumeric install required. Runtime claims target Linux x86_64/aarch64 with NVIDIA compute capability >= 7.0 and CUDA 12.x/13.x. Runtime validation is delegated to cuPyNumeric Doctor.
metadata:
  author: "NVIDIA Corporation <legate@nvidia.com>"
  version: "2.0.0"
  tags:
  - cupynumeric
  - legate
  - numpy
  - gpu
  - distributed-computing
  upstream: https://github.com/nv-legate/cupynumeric
  docs: https://docs.nvidia.com/cupynumeric/latest/

cuPyNumeric Migration Readiness

Purpose

**Use this skill BEFORE the migration, not during.** Answer one question: *which of the user's existing NumPy APIs will scale on cuPyNumeric, and which need refactoring, before they commit engineer-weeks to porting?* To answer it: read the source, classify each NumPy idiom by its expected multi-GPU scaling on the Legate/NVIDIA GPU stack, cross-reference the bundled API-support manifest, and produce a structured verdict with per-finding reasoning and recipe pointers.

**This is a static, read-only assessment.** Inspect the user's source with `Read`, `Grep`, and `Glob`. Do **not** execute the user's code, modify or write files, or print environment variables or secrets. The `legate`, and cuPyNumeric Doctor commands shown below are suggestions for the *user* to run — not actions this skill performs.

If this skill has never been seen before, head to [`references/getting-started.md`](references/getting-started.md) first.

When to use this skill

Use when the user is **about to** migrate NumPy code to GPU and asks whether it will scale on cuPyNumeric / GPU, whether they should migrate, which parts will benefit, what must change before porting, or whether the port is worth it — or mentions pre-port assessment, scaling analysis, idiom analysis, GPU refactor planning, or identifying NumPy anti-patterns for GPU.

**Decline and redirect** when the request is *not* a pre-migration assessment:

• **Post-migration performance / profiling** ("already ported, why is it slow?") → point to `legate --profile` and the upstream [profiling and debugging](https://docs.nvidia.com/cupynumeric/latest/user/profiling_debugging.html) walkthrough.
• **Custom CUDA / kernel authoring** ("write/optimize a CUDA kernel")

A graph / sparse / ML / NLP workload that the user *is* asking to migrate is still **in scope**: assess it and return **NOT RECOMMENDED** via Gate 4. That is a verdict, not a decline.

Instructions

Run all five steps below, in order. Read the user's code and reason about it semantically; do not emit a one-shot prose verdict.

Step 1 — Gather context

Elicit before scanning code. Each item below has a default tuned to the typical workload — use the default when the user does not volunteer specifics; do not block on questions.

• **Source location.** Default to the current working directory when no path is given.
• **Approximate hot-path array sizes at runtime.** Default to 30–50 million elements. Map the user's numbers (or this default) to the [Gate 2 tiers](references/decision-framework.md#gate-2-problem-size) (65K per-GPU floor; 10M+ for real single-GPU speedup; 100M+ for multi-GPU).
• **Target hardware.** Default to 1–4 GPUs, single-node. Confirm before assuming multi-node. For CPU-only runs, ask about RAM per node instead of FBMEM.
• **Dominant compute pattern.** Stencil / GEMM / Monte Carlo / reductions / mixed-with-SciPy. Ask the user to name it; otherwise infer it from the code in Step 3.

State the defaults you applied at the top of the assessment so the user can correct them. If a value is indeterminable, say so plainly and proceed with the qualitative-only assessment — do not fabricate numbers beyond the defaults above.

Step 2 — Load the API support manifest

Read [`assets/api-support.md`](assets/api-support.md), the committed snapshot of the upstream NumPy-vs-cuPyNumeric comparison table. For each NumPy API the code calls, find its line and read the leading glyph:

• `✓✓ numpy.X` — implemented and works on multi-GPU (the best path).
• `✓ numpy.X` — implemented but single-GPU/CPU only (caveats multi-node).
• `🟡 numpy.X — <note>` — partial support; read the note.
• `✗ numpy.X` — not implemented on the cuPyNumeric distributed path. Behavior on call is version-specific (some unsupported APIs route through host NumPy, others raise an exception) — either way, hot-path use is a migration blocker. Do not promise users a silent fallback to host-NumPy.

If the `Fetched:` line is more than ~90 days old, refresh the snapshot — see the **Available Scripts** section.

Step 3 — Read the code semantically

Walk the user's files with `Read` and `Grep` and classify each region of array math against [`references/idioms-that-scale.md`](references/idioms-that-scale.md) and [`references/idioms-that-block.md`](references/idioms-that-block.md) (full rationale and R-codes live there). Read semantically, not by regex: before flagging, confirm `arr` traces back to a `cupynumeric` array (or `np.*` aliased to it) and check whether the access sits inside a hot loop. Apply thes