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Doca Gpunetio

NVIDIAOfficialCreated by NVIDIA

  • Skill
  • Tool integration
  • Software development
  • Apache-2.0
  • Medium risk
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From the author

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.
— the description by NVIDIA

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides specialized support for developers building applications that use the DOCA GPUNetIO library. It focuses on the integration between host-side C/C++ code and device-side CUDA kernels, specifically for scenarios involving GPU-initiated networking on BlueField DPU or ConnectX NIC environments. The skill covers the lifecycle of GPU-visible network queues, persistent kernel design, memory registration, and capability verification across both DOCA and CUDA layers.

What it does

  • Assists in wiring CUDA kernels to doca_gpu_eth_rxq and doca_gpu_eth_txq handles.
  • Guides the setup of per-CUDA-device doca_gpu contexts.
  • Provides patterns for persistent CUDA kernels that process packets directly from NIC queues.
  • Supports dual capability checking using DOCA cap-query and cudaGetDeviceProperties.
  • Details the registration of cudaMalloc pools via doca_buf_arr_create_*.
  • Helps diagnose and debug DOCA_ERROR_* codes specific to the GPUNetIO API.

When to use it

  • When developing applications that require direct packet processing on an NVIDIA GPU.
  • When troubleshooting GPUNetIO setup failures like DOCA_ERROR_DRIVER or missing nvidia_peermem.
  • When verifying compatibility between specific DOCA versions and CUDA toolkit versions.
  • When implementing the GPU Packet Processing reference application pattern.
  • When needing to resolve errors during the initialization of GPU-visible network queues.

Advantages

  • Provides a clear, structured approach to complex GPU-NIC integration tasks.
  • Ensures adherence to the DOCA Compatibility Policy for hardware and software versions.
  • Offers a systematic debugging framework for layered DOCA and CUDA errors.
  • Maintains a clean separation between GPUNetIO logic and general DOCA or CUDA programming.

Usage examples

“How do I get a CUDA kernel to receive packets directly from the NIC?”
The agent provides the persistent-kernel pattern and the GPU-side bring-up workflow to link a doca_gpu_eth_rxq to your CUDA kernel.
“Why does my GPUNetIO setup fail with DOCA_ERROR_NOT_SUPPORTED?”
The agent guides you through checking environment preconditions, specifically verifying if nvidia_peermem is loaded and GPUDirect RDMA is enabled.
“Can I run GPUNetIO on this GPU?”
The agent instructs you to perform a dual capability check using DOCA cap-query and cudaGetDeviceProperties to confirm hardware support.
“How do I move data between CUDA-allocated buffers and a DOCA queue?”
The agent explains the process of using cudaMalloc for buffer pools and registering them with DOCA via doca_buf_arr_create_* before starting the context.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/doca-gpunetio/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip doca-gpunetio.zip -d .agents/skills

Documentation by NVIDIA

DOCA GPUNetIO

Examples

  • "CUDA kernel reading packets directly from the NIC." — this is in scope. Stay on doca_gpu_eth_rxq / _txq and the persistent kernel; do not rewrite it as host-CPU Ethernet.

  • "DOCA_ERROR_DRIVER on doca_gpu_create." — check CUDA device match and dmabuf / nvidia-peermem registration before changing queue type.

Instructions

Follow Loading order below. Open the tasks companion for configure / build / run. Open the capabilities companion for GPU vs host path selection. Do not open every companion file.

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 the tasks companion if the user wants to do something (configure / build / modify / run / test / debug); open the capabilities companion 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/SKILL.md first; if the user has not set up the underlying Ethernet RX/TX queues yet, that is a DOCA Ethernet question — route to doca-eth/SKILL.md.

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 and modes

    • the GPU-side bring-up workflow in

    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 and modes

    • the device-enumeration step in

    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 ## Safety policy

    • the env checklist in

    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

    Safety policy

    • the buffer-prep step in

    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

    Version compatibility

    which cross-links the canonical detection chain in 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

    Error taxonomy

    • the layered ladder in

    debug that escalates to

    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/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:

  • the capabilities companion — 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, dmabuf or nvidia-peermem registration).

  • the tasks companion — 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/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/SKILL.md, layered with the GPUNetIO-specific overrides in

    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, pkg-config --modversion doca-common, and nvcc --version form the version gate.

  • 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 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 the capabilities companion.

  3. For step-by-step workflows — configure, build, modify, run, test, debug — see the tasks companion.

Both companion files cross-link to each other, 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/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/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/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/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/SKILL.md — the bundle's structured-tools precedence rule (detect / prefer / fall back / report). The Command appendix in the tasks companion honors this contract.

  • 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/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. For the underlying queue setup, route to doca-eth/SKILL.md.

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

The author says: 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 GPU memory registered via dmabuf (or the legacy nvidia-peermem fallback) for GPUDirect RDMA; some samples need an InfiniBand-capable RNIC. The validation steps require a live DOCA install.

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Attribution and licence

Created by
NVIDIA
Publisher
NVIDIA (Official)
Source
NVIDIA/skills · skills/doca-gpunetio · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
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The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download doca-gpunetio.zip