About this skill
---
license: Apache-2.0
name: doca-gpunetio
description: >
Use this skill when the user is doing hands-on DOCA GPUNetIO
programming — wiring a CUDA kernel on an NVIDIA GPU to a doca-eth
queue via doca_gpu_eth_rxq / doca_gpu_eth_txq, standing up the
per-CUDA-device doca_gpu context, designing the persistent CUDA
kernel that drains the GPU-visible queue, running the dual
capability check (DOCA cap-query plus cudaGetDeviceProperties),
registering cudaMalloc pools via doca_buf_arr_create_*, or
debugging DOCA_ERROR_* returns from the GPUNetIO API. Trigger
even when the user does not explicitly mention "DOCA GPUNetIO"
or "persistent kernel" — typical implicit phrasings include
"CUDA kernel reading packets directly from the NIC",
"GPU-initiated networking on BlueField", "DOCA_ERROR_DRIVER on
doca_gpu_create", "nvidia_peermem not loaded",
"kernel-per-packet is too slow", or "which GPU supports GPU-side
packet I/O". Refuse and route elsewhere for general CUDA
programming, DOCA Ethernet queue bring-up, DOCA DPA, or
DOCA install — those belong to other skills.
metadata:
kind: library
compatibility: >
Requires DOCA SDK at /opt/mellanox/doca on Linux (Ubuntu 22.04/24.04 or
RHEL/SLES) with a BlueField DPU or ConnectX NIC. Reads the local install
via `pkg-config doca-gpunetio`. Requires an NVIDIA GPU with CUDA toolkit
(matched to DOCA per the DOCA Compatibility Policy) and the
nvidia_peermem kernel module loaded for GPUDirect RDMA; some samples
need an InfiniBand-capable RNIC.
---
# DOCA GPUNetIO
**Where to start:** This skill assumes DOCA is already installed,
the CUDA toolkit is installed and matched to the DOCA install, and
the user is doing **hands-on GPUNetIO work** — i.e. wiring a DOCA
network queue into a CUDA kernel on an NVIDIA GPU. Open
[`TASKS.md`](TASKS.md) if the user wants to *do* something
(configure / build / modify / run / test / debug); open
[`CAPABILITIES.md`](CAPABILITIES.md) when the question is *what
can GPUNetIO express* on this version + this GPU. If the user has
not installed DOCA yet, route to
[`doca-setup`](../../doca-setup/SKILL.md) first; if the user has
not set up the underlying Ethernet RX/TX queues yet, that is a
DOCA Ethernet question (no library skill ships for it yet in this
bundle — route via
[`doca-public-knowledge-map`](../../doca-public-knowledge-map/SKILL.md)
to the public DOCA Ethernet guide).
## Example questions this skill answers well
The CLASSES of GPUNetIO questions this skill is built to answer,
each with one worked example. The agent should treat the *class*
as the load-bearing piece — the worked example is a single
instance.
- **"How do I get a CUDA kernel to receive packets directly from
the NIC?"** — worked example: *"persistent kernel on one GPU
reads packets from a `doca_gpu_eth_rxq` built on top of a
representor `doca_eth_rxq` and counts them per-flow"*. Answered
by the persistent-kernel pattern in
[`CAPABILITIES.md ## Capabilities and modes`](CAPABILITIES.md#capabilities-and-modes)
+ the GPU-side bring-up workflow in
[`TASKS.md ## configure`](TASKS.md#configure).
- **"Can I run GPUNetIO on this GPU?"** — worked example: *"my
host has one Ampere card and one Turing card; which one
supports GPU-initiated networking?"*. Answered by the dual
capability-discovery rule (DOCA cap-query AND
`cudaGetDeviceProperties` against the CUDA device ordinal) in
[`CAPABILITIES.md ## Capabilities and modes`](CAPABILITIES.md#capabilities-and-modes)
+ the device-enumeration step in
[`TASKS.md ## configure`](TASKS.md#configure).
- **"Why does my GPUNetIO setup fail with
`DOCA_ERROR_NOT_SUPPORTED` even though doca-eth came up
fine?"** — worked example: *"`nvidia_peermem` is not loaded so
GPUDirect RDMA is unavailable"*. Answered by the env preconditions
in [`CAPABILITIES.md ## Safety policy`](CAPABILITIES.md#safety-policy)
+ the env checklist in
[`TASKS.md ## configure`](TASKS.md#configure) step 1.
- **"How do I move data between CUDA-allocated buffers and a DOCA
queue?"** — worked example: *"use `cudaMalloc` for the receive
buffer pool and register it with DOCA via `doca_buf_arr_create_*`
before starting the context"*. Answered by the CUDA-allocator
+ DOCA-registration overlay in
[`CAPABILITIES.md ## Safety policy`](CAPABILITIES.md#safety-policy)
+ the buffer-prep step in
[`TASKS.md ## configure`](TASKS.md#configure) step 4.
- **"Is the GPUNetIO API I'm reading about on my installed DOCA +
CUDA combination?"** — worked example: *"is the persistent-kernel
helper available with the CUDA toolkit version I have?"*.
Answered by the version-compatibility overlay in
[`CAPABILITIES.md ## Version compatibility`](CAPABILITIES.md#version-compatibility)
which cross-links the canonical detection chain in
[`doca-version`](../../doca-version/SKILL.md) and adds the
GPUNetIO-specific *DOCA must match CUDA* overlay.
- **"What does this `DOCA_ERROR_*` from a GPUNetIO call mean and
which layer caused it?"** — worked example: *"`DOCA_ERROR_DRIVER`
on `doca_gpu_*_create` — is it DOCA, CUDA, or the underlying
doca-eth queue?"*. Answered by the GPUNetIO overlay on the
cross-library taxonomy in
[`CAPABILITIES.md ## Error taxonomy`](CAPABILITIES.md#error-taxonomy)
+ the layered ladder in
[`TASKS.md ## debug`](TASKS.md#debug) that escalates to
[`doca-debug`](../../doca-debug/SKILL.md).
## Audience
This skill serves **external developers building applications
that consume the DOCA GPUNetIO library** — i.e., users whose code
calls `doca_gpu_*` from host C/C++ to stand up the per-GPU
context and the GPU-visible queue handles, and whose CUDA kernel
(`.cu` translation unit) uses those handles from device code to
submit / receive packets. The canonical target shape is the GPU
Packet Processing reference application: a CUDA persistent
kernel on an NVIDIA GPU that polls a GPU-visible RX queue and
processes packets in-place on the GPU. It is *not* for NVIDIA
developers contributing to DOCA GPUNetIO itself.
**Language scope.** DOCA GPUNetIO ships as a C / CUDA library
with `pkg-config` module name `doca-gpunetio`. The host-side API
is C; the device-side API is CUDA C++ used inside a `.cu`
kernel. The shipped samples and the GPU Packet Processing
reference application are written in C + CUDA C++ (NVIDIA's
choice). Other-language consumers are limited in practice — the
device-side API has no FFI escape hatch because the kernel must
be a CUDA translation unit — but a Rust / Go / Python host-side
wrapper that drives the host-side `doca_gpu_*` setup and
launches a CUDA kernel built separately is still useful, and the
skill keeps the lifecycle, capability-discovery, env-precondition,
and error-taxonomy guidance language-neutral.
## When to load this skill
Load this skill when the user is doing hands-on DOCA GPUNetIO
work, in any host language plus CUDA. Concretely:
- Initializing a `doca_gpu` against a specific CUDA device
ordinal on a host with one or more NVIDIA GPUs.
- Creating a GPU-visible queue handle (`doca_gpu_eth_rxq`,
`doca_gpu_eth_txq`) on top of an existing `doca_eth_rxq` /
`doca_eth_txq` from DOCA Ethernet, and passing the handle into
a CUDA kernel for device-side use.
- Writing or modifying the persistent CUDA kernel that drains
the GPU-visible RX queue in a long-running loop (the canonical
GPU Packet Processing shape).
- Allocating GPU buffers via `cudaMalloc` and registering them
with DOCA via the `doca_buf_arr_create_*` family before
`doca_ctx_start()`.
- Checking which GPUNetIO features are supported on the active
`doca_devinfo` (DOCA cap-query family) AND on the candidate
CUDA device (`cudaGetDeviceProperties` and CUDA-driver-version
checks).
- Debugging a `DOCA_ERROR_*` returned from a GPUNetIO call — in
particular disambiguating *DOCA capability missing* from *CUDA
device too old* from *`nvidia_peermem` not loaded* from *CUDA
driver + DOCA version skew*.
- Designing host-side bindings for non-C languages that drive a
CUDA kernel they built separately — the env-precondition and
capability-discovery rules in this skill still apply.
Do **not** load this skill for general DOCA orientation, install
of DOCA or the CUDA toolkit, the underlying DOCA Ethernet queue
setup, or non-GPUNetIO library questions. For those, route
through [`doca-public-knowledge-map`](../../doca-public-knowledge-map/SKILL.md)
to the matching upstream guide.
## What this skill provides
This is a **thin loader**. The body keeps only the orientation
needed to pick the right next file. The substantive
GPUNetIO-specific material lives in two companion files:
- `CAPABILITIES.md` — what GPUNetIO can express on this version
+ this GPU: the `doca_gpu` per-device context, the GPU-visible
RX / TX queue handles layered on doca-eth, the persistent
CUDA-kernel pattern as the default usage shape, the
capability-query surface (the doca-eth
`doca_eth_rxq_cap_is_type_supported` / `doca_eth_rxq_cap_get_*`
family in `doca_eth_rxq.h`, plus the matching
`doca_eth_txq_cap_*` family, on the DOCA side, plus
`cudaGetDeviceProperties` on the CUDA side), the GPUNetIO error taxonomy mapped onto the cross-library
`DOCA_ERROR_*` set, the observability surface (CUDA-side
counters + DOCA-side per-task completion), and the safety
policy that gates env preconditions (CUDA + DOCA version
match, `nvidia_peermem`, CUDA buffer registration).
- `TASKS.md` — step-by-step workflows for the six in-scope
GPUNetIO verbs: `configure`, `build`, `modify`, `run`, `test`,
`debug`. Plus a `## rollback` overlay (GPUNetIO-specific
five-step teardown that signals the persistent kernel to
drain, unregisters GPU buffers in reverse-register order, and
leaves the parent doca-eth queue intact) and the 5-phase
universal debug-loop instantiation appended to `## debug`.
Plus a `Deferred task verbs` block that points out-of-scope
questions at the right next skill.
The skill assumes a host where DOCA is already installed at the
standard location, an NVIDIA GPU is physically present, the CUDA
toolkit is installed and its version is matched to the DOCA
install per the DOCA Compatibility Policy, and the underlying
DOCA Ethernet RX/TX queues are *already* configured (this skill
sits on top of doca-eth, not below it). It does not cover
installing DOCA or the CUDA toolkit — that path goes through
[`doca-setup`](../../doca-setup/SKILL.md).
## What this skill deliberately does not ship
This skill is **agent guidance**, not a samples or templates
bundle. To keep the boundary clean, it deliberately does not
contain — and pull requests should not add:
- **Pre-written DOCA GPUNetIO application source code or CUDA
kernel source, in any language.** The verified GPUNetIO
source is the shipped C + CUDA samples at
`/opt/mellanox/doca/samples/doca_gpunetio/` and the GPU Packet
Processing reference application. The agent's job is to route
the user to those files and prescribe a minimum-diff
modification on them via the universal modify-a-sample
workflow in
[`doca-programming-guide`](../../doca-programming-guide/SKILL.md),
layered with the GPUNetIO-specific overrides in
[`TASKS.md ## modify`](TASKS.md#modify).
- **Standalone build manifests** (`meson.build`,
`CMakeLists.txt`, …) parked inside the skill. The agent
constructs the build manifest *in the user's project
directory* against the user's installed DOCA + CUDA toolkit,
where `pkg-config --modversion doca-gpunetio` and `nvcc
--version` are the two sources of truth.
- **A `samples/`, `bindings/`, or `reference/` subtree** of any
kind. A mock or incomplete artifact in this skill's tree, even
one labeled "reference", is misleading: users will read it as
buildable.
## Loading order
1. Read this `SKILL.md` first to confirm the user's question is
in scope.
2. **For the GPUNetIO capability matrix, the `doca_gpu` per-device
context, the persistent-kernel pattern, the dual capability
query, the env-precondition policy, the error taxonomy, the
observability surface, and the safety policy, see
[CAPABILITIES.md](CAPABILITIES.md).**
3. **For step-by-step workflows — configure, build, modify, run,
test, debug — see [TASKS.md](TASKS.md).**
Both companion files cross-link to each other,
[`doca-version`](../../doca-version/SKILL.md) for the canonical
DOCA version-handling rules (with the GPUNetIO overlay that DOCA
must match CUDA), and
[`doca-public-knowledge-map`](../../doca-public-knowledge-map/SKILL.md)
whenever the right answer is "look it up in the public DOCA
GPUNetIO guide, the DOCA Compatibility Policy, the CUDA toolkit
docs, or the on-disk install layout" rather than
"GPUNetIO-specific guidance".
## Related skills
- [`doca-public-knowledge-map`](../../doca-public-knowledge-map/SKILL.md) —
the routing table for every public DOCA documentation source
and the on-disk layout of an installed DOCA package. The
GPUNetIO public guide is at
<https://docs.nvidia.com/doca/sdk/DOCA-GPUNetIO/index.html>;
the GPU Packet Processing reference application is reachable
from there. The CUDA toolkit and DOCA Compatibility Policy
links live in the same routing table.
- [`doca-setup`](../../doca-setup/SKILL.md) — env preparation,
install verification, CUDA toolkit install / verification, and
the *I have no install yet* path with the public NGC DOCA
container. This skill assumes its preconditions are satisfied
AND that CUDA is installed at a version that matches DOCA.
- [`doca-version`](../../doca-version/SKILL.md) — canonical DOCA
version-handling rules. This skill's `## Version
compatibility` cross-links the four-way match rule and adds
the GPUNetIO-specific *DOCA-and-CUDA must match* overlay per
the DOCA Compatibility Policy.
- [`doca-structured-tools-contract`](../../doca-structured-tools-contract/SKILL.md) —
the bundle's structured-tools precedence rule (detect / prefer
/ fall back / report). The Command appendix in
[TASKS.md](TASKS.md) honors this contract.
- [`doca-programming-guide`](../../doca-programming-guide/SKILL.md) —
general DOCA programming patterns shared by every library:
the canonical `pkg-config` + meson build pattern, the
universal modify-a-shipped-sample first-app workflow, the
universal lifecycle, the cross-library `DOCA_ERROR_*`
taxonomy, and the program-side debug order. This skill
layers GPUNetIO specifics on top.
- [`doca-debug`](../../doca-debug/SKILL.md) — the cross-cutting
debug ladder (install / version / build / link / runtime /
program / driver). GPUNetIO-specific debug (CUDA + DOCA
version skew, `nvidia_peermem` missing, persistent-kernel
silent hangs, CUDA-allocator + DOCA-registration mismatches)
overlays on top of that ladder.
DOCA Ethernet is GPUNetIO's mandatory companion library: GPU-visible
RX / TX queue handles are layered on top of `doca_eth_rxq` /
`doca_eth_txq` from DOCA Ethernet. No `libs/doca-eth/` skill
ships in this bundle yet; for the underlying Ethernet queue
setup, route via
[`doca-public-knowledge-map`](../../doca-public-knowledge-map/SKILL.md)
to the public *DOCA Ethernet* guide and to the shipped
`/opt/mellanox/doca/samples/doca_eth/` samples.