Warp Eval

Warp evaluation

Purpose

Collect reproducible evidence about how a narrow seam in an existing codebase would behave in NVIDIA Warp. Report facts; the user decides.

Name Warp as the option under evaluation in the first line, state that no adoption recommendation will follow, and do not treat the triggering performance request as authorization to experiment.

Measured evaluations produce warp-evaluation-report/: the report, one independently applicable diff per solution, the drivers, and raw results. Never modify production code. Exits before measured work create no directory.

Hard rules

These override any local reasoning.

1. Default objectives are latency, throughput, and peak or retained memory. Count maintainability, ergonomics, packaging, extensibility, autodiff or new functionality only when the user names it; otherwise report them as constraints or costs, not benefits.

2. Correctness is a gate. If the incumbent is buggy or its contract unclear, abort that comparison until an independent oracle or clarified contract exists. Report the defect without prescribing a response.

3. Never predict savings from source shape or another workload. Materialized memory comes from profiler or allocator evidence, not source a compiler may fuse.

4. "It can be written in Warp" is a hypothesis, never a proven opportunity.

5. Gates are exact, never adjacent or analogous. Fire a gate only when every condition in its definition is established. Gate D requires a maintained implementation confirmed to execute with CUDA on an NVIDIA GPU; a fast native CPU library is a baseline, not Gate D.

6. Label every claim as observed fact, measurement, hypothesis or unknown.

7. Time the user-visible stage, not the kernel. Include Warp's cold import/init/JIT in the real process regime, transfers, launches, Python launch loops, structure build/refit, allocation, conversion, validation, compaction and synchronization. Report each cost and the end-to-end difference.

8. No measured work without explicit authorization. After stage 1, stop and ask before profiling, environment changes, prototyping, benchmarking or GPU use.

9. Authorized measured work includes Warp. If profiling exposes no gate, continue through the strongest in-project baseline, minimum Warp prototype and end-to-end comparison. If Warp is out of scope, stop before profiling.

10. Never infer environment intent. NVIDIA deployment and ownership of an optional compiled dependency are product decisions. Abort only on a stated constraint; otherwise ask once and stop (AWAITING INTENT).

11. Warp measurements are CUDA-only and synchronized. Resolve an explicit NVIDIA CUDA device; discard CPU resolution and dispatch-only timing.

12. Never recommend or rank adoption options. Report facts, measurements, hypotheses and unknowns per seam and regime.

13. Stop at the report. No rollout or production edits.

Requirements

Static screening requires the target repository and its stated product constraints. Measured work additionally requires explicit authorization, an NVIDIA CUDA GPU, target-project dependencies and a representative workload.

Limitations

• Warp CPU kernels are serial and outside this skill's evaluation scope. "Run it on the CPU instead" is not a performance fallback.

• Mesh and geometry queries compute in float32/int32 even behind a float64 public API. Absolute error grows with coordinate magnitude.

• Warp does not follow semver. Feature releases can break APIs, only the newest feature line is maintained, deprecations run roughly four monthly releases.

• latest docs track development, not the shipped release. Pin to the target project's own Warp version; if it has none, use the current stable and say which.

• Kernel-only builtins are not resolvable from Python — hasattr(wp, "mesh_query_point") is False on a release that has it. Probe the stub file or that version's docs before concluding a builtin is absent.

• enable_backward=False (kernel, module or global) removes adjoint codegen. If nothing differentiates through the seam, set it before measuring compile cost.

Output Format

Use this exact shape for an early exit: ABORT — Gate <letter>: <cited fact>; <why the scoped Warp evaluation cannot proceed>. Do not name a preferred alternative. Reporting rules: references/evidence-and-reporting.md. A delivered directory follows the template's fixed order: schema and provenance, authorization and evaluation state, stage census, one B<n> evidence section per seam/regime, caveats, then environment/reproduction. solutions/, benchmarks/ and results/ contain every linked artifact.

Examples

Gate exit: ABORT — Gate A: deployment.md requires one implementation with AMD, Apple and NVIDIA parity; a Warp-specific path cannot satisfy this scope.

Surviving candidate: Name the seam and pattern, label inferred facts as assumptions, state that no gate has fired, preview the profile/baseline/Warp prototype/benchmark scope and its cost, then ask the separate intent and authorization questions from references/authorization-checkpoint.md.

Inputs

Required: the target repository and a performance, memory or scale problem with a candidate seam. Optional: explicit deployment/packaging constraints, existing profiles or logs, representative datasets and acceptance criteria. Prompt constraints take precedence over repository policy/configuration, then existing logs. User corrections override inference. Never substitute an assumption for a stated fact or measurement.

Available scripts

Use run_script("scripts/validate_report_schema.py", args=["warp-evaluation-report"]) when supported; otherwise invoke the script with Python and the report directory.

Troubleshooting

• No representative workload: mark the scope INCOMPLETE and name the missing artifact; do not invent data or fire Gate F.

• Warp resolves to CPU or no CUDA device: discard the run and stop before correctness or timing claims.

• Report validation fails: fix the report or referenced artifact; never waive the schema error.

Instructions

Every stage before the last can end the evaluation. Stop as soon as a gate fires; do not gather evidence that cannot change the scoped facts.

1. Read the code, derive the contract, check the gates

• Identify a candidate and its metric with references/target-patterns.md.

• Derive devices/residency, dtypes/shapes, sizes, frequency, process lifetime, gradients and packaging from the repository. Infer before asking.

• State the inferred contract in one line and invite correction. Unknown hardware, counts, sizes and tolerances are assumptions, never measurements. Every inference remains open to correction and cannot satisfy a gate that requires a stated fact or measurement.

• Check Gates A–E before profiling. Check Gate F now only if representative evidence already exists; otherwise carry it into stage 2. Every gate uses only the exact boundaries in references/rejection-gates.md.

• Gate F can fire in stage 1 only from representative evidence that already exists — a supplied profile, structural bound, or arithmetic on figures the user quoted. If that evidence does not exist, Gate F remains open until stage 2 profiling; inferred values never fire it.

• Gates A and E need a stated constraint. A CPU implementation, another accelerator, no Warp dependency, or a small dependency list proves nothing.

• When A/E are unresolved and a pattern survives, ask whether an optional NVIDIA path is acceptable: named extra, soft import, existing fallback, default install unchanged. Every affirmative answer must say explicitly that Warp will be prototyped and benchmarked; conditions constrain only that Warp scope. A negative or undecided answer means ABORT.

• Do not ask when another gate fired, the repository answers, or no pattern matched. No pattern means no profiling.

• If a candidate survives, combine any intent question with the authorization checkpoint — early findings, exact scope, stages, resource cost — then stop. Stage 2 requires both settled intent and explicit authorization.

2. Profile the real application

Requires explicit authorization and a settled intent question.

• Profile with the project's own profiler and representative entry points.

• Measure synchronized end-to-end stage time and peak memory before choosing a backend. Report which entry points were profiled and which a gate screened.

• Prioritize further measurement by observed cost, not source appearance.

• Name each measurement by the public method and variant actually invoked. A fallback is an execution regime of that public seam, not a different operation, and a cheaper sibling method cannot screen out the named method.

• Confirm the timed branch ran on the intended device. Unchanged cost and near-zero device allocation between host and device inputs exposes a host fallback.

• Measure the stage's free-stage ceiling and stubbed floor through the public boundary. Do not subtract per-op timings.

• If the measured ceiling proves the candidate cannot move its own metric, ABORT the affected scope under Gate F, preserve the evidence already collected, and stop. The existing authorization already covered this materiality check; do not ask for authorization again.

• If representative coverage is unavailable — no representative dataset, runnable entry point or production distribution — record the single missing artifact, mark the affected scope INCOMPLETE, and stop. This is missing evidence, not Gate F and not ABORT. An invented workload cannot prove materiality.

Protocol: references/benchmark-protocol.md.

3. Form falsifiable hypotheses

Record per candidate: source, bottleneck evidence, objective, narrow seam, mechanism Warp could change, strongest incumbent, risks, acceptance threshold, cheapest falsifying experiment. Screen against references/target-patterns.md; if none survives, write the report and stop.

4. Write the contract before the prototype

Define values, dtypes, shapes, devices, errors, mutation, ordering, ties, capacity/overflow, topology/degeneracy, tolerances, required gradients, streams, ownership, aliasing, invalidation, concurrency, capture, teardown and fallback.

• Fix tolerances before seeing Warp output. Never weaken a contract after a mismatch.

• ABORT before prototyping if the proposed seam cannot satisfy a required contract.

• Pre-register, before timing: workload provenance, the state variable and production range controlling cost, tuning knobs, incumbent run-to-run spread, and the oracle applied to every implementation.

Hazards and adversarial checks: references/semantic-contract.md.

5. Improve the baseline first

Algorithm before backend: (1) a better or output-sensitive algorithm; (2) chunking, tiling, sparse output, layout, rematerialization; (3) the incumbent framework's compiler and native primitives; (4) what the project already depends on — its own accelerator backend, a parallel idiom it ships but leaves off, or a capability an existing dependency exposes and nobody wired up; (5) only then narrow Warp.

• Compare only in-scope options: the improved incumbent, capabilities reachable through current dependencies, and Warp. Do not add unrelated libraries.

• Search declared dependencies for dormant backends, flags and bindings before calling a route absent.

• Keep the improved incumbent as the oracle. A result against an untuned, incorrect or asymptotically inferior baseline does not establish a backend comparison.

Close every in-project route before prototyping Warp:

A capability present but unbound is reachable, not absent. If exposing it costs no more than the planned Warp seam, measure it first. Ladder details: references/baselines.md. Waived routes do not block stage 6; every route not explicitly waived must be measured or evidenced absent before the Warp prototype begins.

6. Prototype the minimum unit

• Work in a separate copy, production unchanged, integration seam off by default. Prototype only enough to test the hypothesis.

• Select an explicit CUDA device for every Warp prototype and verify that Warp resolves it as CUDA before correctness or performance work. Never exercise or report Warp's CPU backend.

• Size the unit by shared data and structure lifetime, not function boundaries: build/refit/query costs that amortize together are one unit.

• Name variants before timing. Preserve each solution as an independent patch against the pinned baseline, and prove it applies cleanly and reproduces the measured result.

• Surface unsupported cases and overflow before timing.

• Run the adversarial contract checks, audit correctness at production scale, and deliberately break a branch to prove the tests fail. Any failed required check returns ABORT for the affected scope.

• Before treating a disappointing compute-bound or reuse-heavy result as representative, consider the stronger formulations in references/target-patterns.md. One naive kernel does not bound Warp's potential.

7. Benchmark the whole boundary

• Copy scripts/driver-template.py once per bottleneck and measure through scripts/measure.py. Do not hand-roll timing or memory.

• Warp launches are asynchronous. The measurement helper must synchronize the selected device immediately before starting and after enqueueing every timed region, before stopping its wall timer.

• Include every rule-7 cost, the public call, the immediate downstream stage, realistic sizes, and cold/warm process regimes.

• Transcribe every report cell from emitted JSON; absent records read not measured.

• Report null_test, and one-time costs both separately and amortized. Serialize GPU measurements under an exclusive device lock. Below 1.5× is no measured difference.

• ABORT for a seam and regime whose predeclared end-to-end performance or memory requirement fails, after preserving the measurements.

Full protocol: references/benchmark-protocol.md.

8. Summarize the evidence

• Per seam and execution regime, report semantic results, strongest-baseline measurements, workload provenance, time, memory, lifecycle, portability and ownership facts.

• Use pass, fail, not measured, not available, no representative data, unknown or n/a only where a stated criterion makes that status objective. Missing evidence remains missing.

• Mark the report complete only when every evaluated seam and regime includes an end-to-end Warp measurement. Use aborted — <gate and scope> when a gate fired, or incomplete — <missing evidence and scope> when representative evidence was unavailable. Preserve everything collected. Never deliver an incumbent-only report as a completed warp-eval.

• Give every stage worth ≥10 % of the measured total a census row with a status and a one-line reason, including the screened-out stages.

• Check each gate against the metric that candidate's pattern exhausts, not the study's headline objective.

9. Hand back

• Use assets/warp-evaluation-report-template.md unchanged in schema. Record authorization and scope.

• One table per bottleneck: baseline, current-dependency solutions, then Warp; absolute time and peak memory, ratios, contract status, evidence gaps.

• Include prose only where it explains the measurements or their bounds. Working artifacts belong in results/.

uv run python scripts/validate_report_schema.py <report-directory>

• Run the validator once the first measurement establishes a census/table, and again before delivery. Fix every error.

• Verify builds and imports from artifacts, not exit status. Ship the exact drivers run. Then stop.