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.

• 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.

• 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.

• Never predict savings from source shape or another workload. Materialized

memory comes from profiler or allocator evidence, not source a compiler may

fuse.

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

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

• No measured work without explicit authorization. After stage 1, stop and

ask before profiling, environment changes, prototyping, benchmarking or GPU

use.

• 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

| State | Reached when | Report directory |

|---|---|---|

| ABORT | Any gate establishes that the evaluated seam cannot satisfy the stated scope | No if nothing was measured; otherwise preserve the evidence already collected |

| AWAITING INTENT | The environment gates turn on a fact only the user has | No — one question, both branches concrete |

| AWAITING AUTHORIZATION | A candidate pattern survives stage 1 | No — early findings plus scope/resource preview |

| INCOMPLETE | Authorized work cannot obtain representative evidence required for the scoped evaluation | Yes — preserve collected evidence and name the one missing artifact |

| Report delivered | Authorized work produced measurements, or stopped after the report directory existed | Yes — facts per seam and regime, with missing evidence explicit |

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

| Script | Purpose | Arguments |

|---|---|---|

| scripts/driver-template.py | Copy once per bottleneck; define workloads and variants | Edit placeholders, then run the copied driver |

| scripts/measure.py | Import from drivers for synchronized timing, memory and isolated cases | Python API; do not execute directly |

| scripts/validate_report_schema.py | Validate the delivered report and evidence links | <report-directory> |

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 | Fires when |

|---|---|

| A | Production is *stated* CPU-only or to need non-NVIDIA portability, with no acceptable optional CUDA path |

| B | Data must cross the host/device boundary per small or infrequent call and the boundary cannot be widened |

| C | The region is dense tensor algebra already mapped to a tuned framework or vendor library |

| D | A mature CUDA implementation already meets the contract, and no non-performance objective was requested |

| E | A stated policy blocks Warp's dependency, compilation, cache or fallback obligations |

| F | Representative evidence proves the region too small a share of its requested metric for any backend to move it |

• 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:

| State | What it takes to claim it |

|---|---|

| measured | timed through the same boundary as the baseline |

| absent | a cited declaration, symbol table or missing flag proves it is unavailable |

| waived | you asked the user and they chose to skip it; record their words |

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.