Doca Bench

DOCA Bench (doca_bench)

Where to start: This is a tool skill for invoking doca_bench, the cross-library micro-benchmark harness. Open TASKS.md and start at ## configure for the three-axis decision (target library × workload shape × measurement axis), then ## run for the smoke-before-bulk flow. Open CAPABILITIES.md 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 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 CAPABILITIES.md ## Capabilities and modes
  • the smoke-before-bulk flow in TASKS.md ## run. 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 CAPABILITIES.md ## Capabilities and modes
  • TASKS.md ## configure 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 CAPABILITIES.md ## Error taxonomy layer 5 + TASKS.md ## test (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 CAPABILITIES.md ## Error taxonomy (config-syntax → device-binding → library-precondition → workload-precondition → measurement-soundness → version → cross-cutting) + TASKS.md ## debug.
• "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 TASKS.md ## test (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 TASKS.md ## debug + CAPABILITIES.md ## Error taxonomy. Re-route through doca-caps 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 vs max-bandwidth) — the four modes defined in tools/bench/doca_bench/configuration.hpp 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 TASKS.md ## debug).

Do not load this skill for general DOCA orientation, library API work, or installation. For those, use doca-public-knowledge-map, the matching libs/<library> skill, or doca-setup. 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.