Doca Gpunetio

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 if the user wants to do something (configure / build / modify / run / test / debug); open 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 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.

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
  • the GPU-side bring-up workflow in 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
  • the device-enumeration step in 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
  • the env checklist in 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
  • the buffer-prep step in 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 which cross-links the canonical detection chain in doca-version 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
  • the layered ladder in TASKS.md ## debug that escalates to doca-debug.

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 to the matching upstream guide.