Doca Bench

DOCA Bench (doca_bench)

Examples

• "What does this DOCA library actually deliver on this device?" — this is in scope. Stay on doca_bench; do not write a custom microbenchmark.

• "Which DOCA libraries can doca_bench drive on this install?" — probe the built-in query first; empty enumeration is a missing library, not a bench failure.

Instructions

Follow Loading order below. Open the tasks companion for configure / run / debug. Open the capabilities companion for modes and measurement soundness. Do not open every companion file.

Where to start: This is a tool skill for invoking doca_bench, the cross-library micro-benchmark harness. Open the tasks companion and start at ## configure for the three-axis decision (target library × workload shape × measurement axis), then ## run for the smoke-before-bulk flow. Open the capabilities companion when the question is what doca_bench can measure, which DOCA libraries it can drive, or how to interpret throughput / latency / op-rate output without fooling yourself on warm-up or steady-state. If DOCA is not installed yet, route to doca-setup/SKILL.md first; if the install version is < 2.7.0, doca_bench is not shipped on this host.

Example questions this skill answers well

The CLASSES of doca_bench questions this skill is built to answer, each with one worked example. The class is the load-bearing piece; the worked example is one instance.

• "What does this DOCA library actually deliver on this device?" — worked example: "throughput of DOCA Compress on my BlueField-3". Answered by the three-axis configuration in the capabilities companion

  • the smoke-before-bulk flow in the tasks companion. The same shape answers "send-side throughput of DOCA RDMA" — doca_bench is cross-library, not single-library.

• "Which DOCA libraries can doca_bench actually drive on this install?" — worked example: "is doca_sha enumerable on a granular-build install". Answered by the built-in query system surfaced in the capabilities companion

  • the tasks companion step 2 (probe-before-bench). Empty enumeration = library not installed, not bench failure.

• "Is this number reliable, or did I miss the warm-up?" — worked example: "why does my first-second number differ from my steady-state number". Answered by the measurement-soundness overlay in the capabilities companion layer 5 + the tasks companion (the eval-loop overlay treats warm-up / steady-state / outliers as re-iteration triggers, not one-shot facts).

• "Bench reports zero throughput / hangs at start / disagrees with the public docs." — worked example: "doca_bench shows zero ops for AES-GCM but doca_caps says the device supports it". Answered by the layered error taxonomy in the capabilities companion (config-syntax → device-binding → library-precondition → workload-precondition → measurement-soundness → version → cross-cutting) + the tasks companion.

• "How do I capture a baseline I can later regression-test against?" — worked example: "snapshot decompress throughput on this BlueField + DOCA version before a firmware update". Answered by the CSV output + version-overlay rule in the tasks companion (capture command line + version + device + as-deployed environment alongside the numbers; quoting numbers without the four-tuple is the cross-version regression-hunt failure mode).

• "doca_bench returns nothing for library X — what does that mean?" — worked example: "empty output for DOCA SHA". Answered by the empty-output interpretation rules in the tasks companion + the capabilities companion. Re-route through doca-caps/SKILL.md for the coarse per-device per-library capability ground truth, then back into bench once the capability is confirmed present.

Audience

This skill serves external operators, developers, and AI agents who need a reproducible, vendor-supported way to measure DOCA library performance on the user's actual install and device. Concretely:

• An external developer choosing between DOCA libraries (e.g. COMPRESS vs SHA vs DMA throughput) before committing an application design.

• A platform operator validating a tuning change (NUMA pinning, driver upgrade, firmware burn) by re-running a captured doca_bench baseline against the new state.

• An SRE / performance engineer producing a "this is what the device delivers today" artifact that downstream consumers (capacity planning, regression bisection) can cite.

• An AI agent answering "what throughput / latency should I expect from DOCA library X on device Y?" honestly — with a measured number, the command line that produced it, and the version + device + environment that scopes it — instead of guessing from datasheet headlines.

It is not for users debugging the doca_bench source code, and not a substitute for the live public DOCA Bench guide on docs.nvidia.com.

doca_bench is shipped as a tool (a single CLI binary plus a companion app for the remote half of remote-memory / RDMA / Eth scenarios), not a library you link against. The skill uses the same kind: tool three-file shape as the rest of the bundle so the agent's task-verb contract (configure / build / modify / run / test / debug) is uniform across libraries, services, and tools — even when individual verbs collapse to a routing stub for a shipped binary.

When to load this skill

Load this skill when the user is — or the agent needs to — invoke doca_bench on a real host with DOCA ≥ 2.7.0 installed (or inside the public NGC DOCA container with the equivalent version) to measure performance of a DOCA library. Concretely:

• Picking which DOCA library to benchmark for a candidate workload (RDMA vs COMPRESS vs DMA, etc.).

• Picking which measurement axis to ask for (throughput vs bulk latency vs precision latency) — the three modes the public guide documents; confirm the list with --help. The modes are not interchangeable.

• Probing the install's granular-build state so the agent can honestly report "this library is not exposed on this install" instead of inventing a workload.

• Capturing a documented baseline (command line + version + device

  • as-deployed environment + numbers) for later regression hunts.

• Requiring the workload owner to predeclare acceptable variance and obtaining two consecutive runs within that tolerance before reporting a stable result; otherwise escalating the variance.

• Diagnosing why a bench run reported zero / unstable / unexpected results (the error-taxonomy walk in the tasks companion).

Do not load this skill for general DOCA orientation, library API work, or installation. For those, use doca-public-knowledge-map/SKILL.md, the matching libs/<library> skill, or doca-setup/SKILL.md. Do not load it for application-level end-to-end benchmarking either — doca_bench measures the DOCA library surface, not the user's application above it.

What this skill provides

This is a thin loader. Substantive material lives in two companion files:

• the capabilities companion — what doca_bench can measure (the cross-library scope, the three-axis configuration model, the documented operating modes, the warm-up / pipeline / multi-core concepts that constrain measurement soundness), the version overlay (doca-bench-specific facts on top of the canonical doca-version rules), the layered error taxonomy (config-syntax / device-binding / library-precondition / workload-precondition / measurement-soundness / version / cross-cutting), the observability surface (screen + CSV output, real-time stats, query system), and the safety posture (the public guide's "not for production" warning, the host vs BlueField execution rule, the companion-app attack surface).

• the tasks companion — step-by-step workflows for the in-scope task verbs: configure (the three-axis decision + the probe-before-bench step), build (route to install — the binary is shipped, the companion app is shipped), modify (refuse — do not patch the bench binary; modify the bench invocation instead), run (the smoke-before-bulk flow), test (the eval loop — warm-up, steady-state, outliers, cross-version), debug (walk the error taxonomy layer by layer), plus a Deferred task verbs block routing out-of-scope questions and a Command appendix of doca_bench-specific invocation classes.

The skill assumes a host where DOCA ≥ 2.7.0 is already installed (or the public NGC DOCA container is running at an equivalent version) and the operator has whatever permissions the public guide requires for doca_bench to bind devices and allocate resources on their platform.

What this skill deliberately does not ship

This skill is agent guidance, not a samples or scripts bundle. To keep the boundary clean, it deliberately does not contain — and pull requests should not add:

• Specific flag strings or scenario / metric / attribute names beyond what the public DOCA Bench guide documents. The flag surface evolves and is install-specific; the documented invocations + --help on the installed version are the authoritative answer. Inventing a flag is the most common hallucination failure for this skill.

• Pre-baked example output or expected throughput numbers. Bench output is device-, version-, firmware-, NUMA-, and tuning-specific. A captured number pinned to one platform and one DOCA version misleads operators on a different platform / version.

• Wrappers, parsers, or scripts in any language that consume doca_bench CSV or stdout. The output formats are documented; if a user wants to script against them, the right answer is "read the live guide, write the parser against your installed version".

• A samples/ or reference/ subtree. This is a thin loader for a documented CLI; substantive material lives on the public page and in --help.

Loading order

1. Read this skill first to confirm the user's question is in scope (the user actually wants to invoke doca_bench for measurement, not learn about a DOCA library in general).

2. For what doca_bench measures, the three-axis model, the version overlay, the error taxonomy, observability surface, and safety posture, see the capabilities companion.

3. For the documented invocations and the smoke-before-bulk workflow — configure, build, modify, run, test, debug — see the tasks companion.

Related skills

• doca-public-knowledge-map/SKILL.md — routing to the public DOCA Bench page on docs.nvidia.com and the rest of the public DOCA documentation set.

• doca-version/SKILL.md — the canonical version-detection chain, four-way match rule, NGC container semantics, and headers-win-over-docs rule. The ## Version compatibility section in this skill is a thin overlay on top of doca-version; the body lives there.

• doca-structured-tools-contract/SKILL.md — the bundle-wide contract for structured-output helper tools. When a site provides a helper that satisfies the detect-prefer-fallback-report loop, the ## Command appendix in the tasks companion uses it; otherwise it falls back to the manual commands.

• doca-setup/SKILL.md — env preparation, install verification, hugepages, NUMA awareness, and the I have no install yet path with the public NGC DOCA container.

• doca-debug/SKILL.md — the cross-cutting debug ladder. Bench surfaces its own error taxonomy in the capabilities companion; when the cause turns out to be below DOCA (driver, firmware, NUMA), the bench taxonomy hands off to doca-debug.

• doca-caps/SKILL.md — the sibling DOCA tool for the coarse per-device per-library capability snapshot. Bench probes capability at finer grain via its own query system; doca_caps is the cheaper first step to confirm the device is even visible to DOCA.

• The matching libs/<library> skill — e.g. doca-comch/SKILL.md, doca-compress/SKILL.md — for the workload-side preconditions, capability-query rules, and error-taxonomy overlays of the library under test. Bench drives the library; the library skill explains what "healthy" means for it.