
DOCA Flow DPA Perf
OfficialFreeMeasure DPA-offloaded Flow performance on supported hardware.
Free · Opens the source repo
What DOCA Flow DPA Perf does
The DOCA Flow DPA Perf skill is designed for users who need to measure and evaluate the performance of DPA-offloaded Flow paths on compatible hardware. This skill is specifically tailored for devices such as ConnectX-7 and ConnectX-8, as well as BlueField-3, ensuring that users can accurately assess the rule update and disable rates in their networking environments. By invoking the doca_flow_dpa_perf command, users can configure their hardware settings, run initial tests, and interpret the results effectively.
To get started, users should refer to the TASKS.md file, which provides step-by-step guidance on configuring the hardware and running performance tests. The skill emphasizes the importance of confirming that the hardware is DPA-capable and that the correct device settings are in place before proceeding with performance measurements. Users can also consult CAPABILITIES.md to understand what metrics can be measured, the necessary preconditions, and how to interpret the output of the tool without misrepresentation.
This skill is particularly beneficial for platform operators, performance engineers, and application developers who are working with DPA technology. It allows them to make informed decisions about whether to offload workloads onto the DPA or keep them on the host path. By providing defensible Kops/sec numbers and detailed configuration options, the skill ensures that users can accurately characterize the performance of their DPA-offloaded workloads.
However, it is important to note that this skill is not intended for users looking to debug the tool's source code or learn about the broader DOCA APIs. It is strictly focused on performance measurement and should not be used for host/DPU-CPU Flow path evaluations, which are handled by other skills. Users should ensure they are using the right tool for their specific needs to achieve optimal results.
When to use it
Use this skill when you need to assess the performance of DPA-offloaded Flow paths on supported hardware.
When not to use it
Do not use this skill for host/DPU-CPU Flow path evaluations or for learning about DOCA APIs; those require different tools.
What you can build with it
Evaluating DPA Performance
A performance engineer uses the skill to measure Kops/sec for a specific workload on a ConnectX-8 device.
Comparing Offload Options
A platform operator assesses whether to offload a path-selector workload to the DPA using the metrics provided by the skill.
Characterizing Application Performance
A DOCA Flow application developer uses the skill to benchmark the performance of their DPA-offloaded Flow rule update path.
How to install DOCA Flow DPA Perf
View source1. Install with the skills CLI
npx skills add nvidia/skills/doca-flow-dpa-perf --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by nvidiaDOCA Flow DPA Perf (doca_flow_dpa_perf)
Where to start: This is a tool skill for invoking
doca_flow_dpa_perf, the DPA-accelerated Flow performance tool.
Open TASKS.md and start at
## configure to confirm DPA-capable
hardware + VNF Flow mode + the active / passive device split, then
## run for the smoke-before-bulk flow with a
small operation count before any sweep, then
## test for the eval-loop overlay that gates
defensible Kops/sec numbers. Open CAPABILITIES.md
when the question is what doca_flow_dpa_perf can measure,
what the DPA preconditions are, which devices it runs on,
or how to interpret update / disable / self-test output without
fooling yourself. If DOCA is not installed yet, route to
doca-setup first; if the device is
not DPA-capable (no ConnectX-7+ or BlueField-3+) then this tool is
the wrong surface and the right answer is
doca-flow-perf.
Example questions this skill answers well
The CLASSES of doca_flow_dpa_perf 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.
- "Should I measure the DPA-offloaded Flow path or the
host / DPU-CPU Flow path for this question?" — worked
example: "my workload programs path-selector entries via
DOCA Flow; do I baseline with
doca_flow_dpa_perfor withdoca_flow_perf?". Answered by the DPA-vs-host boundary inCAPABILITIES.md ## Capabilities and modesand the device-preconditions table. - "What does the DPA-offload actually accelerate, and what
doesn't it change?" — worked example: "if I move my Flow
rule update path to the DPA, what changes in the data plane
for the packets themselves?". Answered by the DPA-Provider
scope in
CAPABILITIES.md ## Capabilities and modes. - "What hardware do I need to use this tool at all?" —
worked example: "is my BlueField-2 DPA-capable?". Answered
by the device-preconditions table in
CAPABILITIES.md ## Capabilities and modes(BlueField-3 yes, BlueField-2 no; ConnectX-7 minimum supported, ConnectX-8 recommended, and later generations supported per the public guide and the shipped README on the user's install). - "How do I size my run — burst, queue, completion threshold,
number of operations, iterations — to get a defensible
Kops/sec number?" — worked example: "I want the median
iteration time and standard deviation, not a single noisy
first-iteration spike". Answered by the eval-loop overlay
in
TASKS.md ## testand the iteration-stats rule inCAPABILITIES.md ## Observability. - "My tool reports zero ops / hangs / fails the self-test —
what does that mean?" — worked example: "the tool runs but
the self-test step fails". Answered by the layered error
taxonomy in
CAPABILITIES.md ## Error taxonomy- the debug ladder in
TASKS.md ## debug.
- the debug ladder in
- "How do I quote a DPA-perf number alongside a host-side
Flow-perf number for the same workload, in a way the next
engineer can actually compare?" — worked example: "two
Kops/sec numbers for what is supposedly the same workload".
Answered by the four-tuple capture rule in
CAPABILITIES.md ## Safety policy- the per-tool-name rule (the host tool and the DPA tool are different surfaces; their numbers are not interchangeable without naming which tool produced which).
Audience
This skill serves external operators, performance engineers, DOCA Flow application developers, and AI agents who need a defensible measurement of the DPA-offloaded Flow update path on DPA-capable hardware. Concretely:
- A platform operator deciding whether to move a path-selector workload onto the DPA versus keeping it on the host / DPU-CPU path, and wanting a number to compare.
- A performance engineer producing a "DPA Kops/sec for update operation, queue-size X, burst-size Y, N workers" baseline on a specific device + DOCA version so a downstream comparison is meaningful.
- A DOCA Flow application developer who has already used
doca-dpato land a DPA-offload of their Flow rule update path and wants to characterize what the device delivers. - An AI agent answering "what update rate should I expect from the DPA-offloaded Flow path on device Y?" honestly — with a measured number, the command line that produced it, and the device + DOCA version + as-deployed environment that scopes it — instead of guessing from datasheet headlines.
It is not for users debugging the tool's source code,
not a substitute for the live public DOCA Flow DPA Perf guide
on docs.nvidia.com, not the place to learn the doca-flow
or doca-dpa APIs (that audience belongs in
doca-flow and
doca-dpa), and not the right
tool for the host / DPU-CPU Flow path (route to
doca-flow-perf).
doca_flow_dpa_perf is shipped as a single CLI binary with
DPA-side device code linked in. The skill uses the same
kind: tool three-file shape as the rest of the bundle so
the agent's task-verb contract is uniform across the bundle.
Language scope
This skill governs invocation, output interpretation, and
recommendation-of-routing for the doca_flow_dpa_perf CLI on
DPA-capable hardware. The tool itself has both a host-side
control (C-language ARGP + DOCA + DPDK code per the shipped
flow_dpa_perf.c / flow_dpa_perf_core.c) and a DPA-side device
component (DPA-side code on the shipped DPA device runtime).
External users do not link any of this; what they configure is
the JSON-config-or-CLI invocation surface. For the
doca-dpa programming model behind the DPA-side execution
engine, see
doca-dpa; for the doca-flow
API behind the pipeline the DPA path executes, see
doca-flow.
When to load this skill
Load this skill when the user is — or the agent needs to —
invoke doca_flow_dpa_perf on a real host with DOCA installed
and a DPA-capable device attached (or the public NGC DOCA
container with the equivalent device passthrough) to measure
update / disable rates on the DPA-offloaded Flow path.
Concretely:
- Confirming DPA preconditions (DPA-capable device class, VNF Flow mode, recommended PF use, no SFs) before invoking the tool.
- Picking the active / passive device split appropriate to the user's hardware (two-port BlueField-3 active + passive; one- port ConnectX-9 active only).
- Picking the workload-shape axes (burst size, queue size, completion threshold, hash pipe algorithm, work policy, number of PSL tables, table size, number of workers).
- Picking the operation axis (update or disable-enable) per the shipped README's documented operations.
- Producing a defensible Kops/sec number with iteration stats (median, max, standard deviation) captured.
- Diagnosing zero-ops / hung / failed-self-test runs through the layered error taxonomy.
Do not load this skill for general DOCA orientation, Flow
program API work, or installation. For those, use
doca-public-knowledge-map,
the matching libs/<library> skill, or
doca-setup. Do not load it for
the host / DPU-CPU Flow path — that audience belongs in
doca-flow-perf.
What this skill provides
This is a thin loader. Substantive material lives in two companion files:
CAPABILITIES.md— whatdoca_flow_dpa_perfmeasures (the DPA-Provider-on-DPA-device update / disable path specifically), the DPA-vs-host-path boundary, the device-preconditions table (ConnectX-7+ / BlueField-3+), the documented VNF-only Flow-mode rule, the PF-vs-VF-vs-SF rule (SFs not supported on DPA), the workload-shape axes (burst, queue, completion threshold, hash pipe algorithm, work policy, PSL tables, table size, workers), the operation axis (update vs disable-enable), the version overlay (this tool rides thedoca-flowanddoca-dpaversions it links against; the canonical rules live indoca-version), the layered error taxonomy (config-syntax / device-binding / dpa-precondition / workload-precondition / measurement-soundness / self-test / version / cross-cutting), the observability surface (iteration statistics, self-test path-selector verification, tcpdump-side traffic verification), and the safety posture (smoke-before-bulk, four-tuple capture, name the tool that produced the number).TASKS.md— step-by-step workflows for the in-scope task verbs:install(route to setup; the binary is shipped),configure(DPA-preconditions + active / passive device + workload-shape decision),build(route to install — the binary is shipped),modify(refuse — modify the invocation, not the binary),run(smoke before bulk),test(eval loop),debug(layered diagnosis),use(consume the captured number), plus aDeferred task verbsblock routing out-of-scope questions and aCommand appendix.
The skill assumes a host where DOCA is already installed (or the NGC DOCA container is running) on a DPA-capable device and the operator has the permissions to bind the device and allocate the DPA execution resources the tool needs.
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:
- Verbatim default values for flag inventories beyond what
the shipped README or installed
--helpdocuments. Read defaults from the README first, then fall back to the installed binary's--help. If neither defines a needed default, stop and request the operator's explicit value instead of guessing. The flag surface is install-specific within the documented surface; the documented invocations +--helpon the installed version are the authoritative answer. Inventing a flag is the most common hallucination failure. - Pre-baked example Kops/sec numbers or expected throughput numbers. Output is device-, firmware-, DOCA-version-, workload-, and platform-specific; a pinned number for one platform misleads operators on a different platform / version. The shipped README's example numbers are illustrative, not a baseline the agent should quote as ground truth.
- Wrappers, parsers, or scripts in any language that consume the tool's stdout / CSV. The output format is documented; if a user wants to script against it, the right answer is "read the live guide, write the parser against your installed version".
- A
samples/orreference/subtree. This is a thin loader for a documented CLI; substantive material lives on the public page, in--help, and in the shipped README on the user's install.
Loading order
- Read this
SKILL.mdfirst to confirm the user's question is in scope (the user actually wants to invokedoca_flow_dpa_perfon DPA-capable hardware, not measure the host / DPU-CPU Flow path). - For what
doca_flow_dpa_perfmeasures, the DPA-vs-host boundary, the device-preconditions table, the workload- shape axes, the version overlay, the error taxonomy, the observability surface, and the safety posture, see CAPABILITIES.md. - For the documented invocations and the smoke-before-bulk
workflow —
install,configure,build,modify,run,test,debug,use— see TASKS.md.
Related skills
doca-flow— the base library whose pipeline this tool measures on the DPA path. The pipe / entry / rule surface this tool drives is created bydoca-flowprogram code; the library's pipe attributes and capability surface are the upstream context.doca-dpa— the programming model behind the DPA execution engine the tool runs on. When the user's question goes from "measure the DPA path" to "why is the DPA path doing this", that skill is the next stop.doca-flow-perf— the host / DPU-CPU Flow performance tool. The cross-tool comparison rule lives inCAPABILITIES.md ## Capabilities and modes: name which tool produced which number.doca-flow-tune— the Flow tuning tool. A DPA-perf number is the kind of baselinedoca-flow-tunethen optimizes on top of, via a Flow-program modify-a-sample loop.doca-public-knowledge-map— routing to the public DOCA Flow DPA Perf page ondocs.nvidia.comand the rest of the public DOCA documentation set.doca-version— canonical DOCA version-handling rules. The## Version compatibilitysection in this skill is a thin overlay on top.doca-setup— env preparation, install verification, hugepages, NUMA awareness, and the I have no install yet path with the public NGC DOCA container.doca-debug— the cross-cutting debug ladder. DPA-perf surfaces its own error taxonomy; when the cause turns out to be below DOCA, the taxonomy hands off todoca-debug.doca-hardware-safety— the cross-cutting hardware-safety meta-policy this skill's## Safety policyoverlays.
Frequently asked questions about DOCA Flow DPA Perf
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