DAQIRI - Data Acquisition for Integrated Real-time Instruments

Send and receive Ethernet packets into CPU and GPU memory at hundreds of Gbps with a simple API. DAQIRI (Data Acquisition for Integrated Real-time Instruments) connects data acquisition systems to NVIDIA GPUs for real-time processing and AI, paving the way for autonomy of the next generation of scientific and industrial instruments.

Warning

The library is undergoing large improvements as we aim to better support it as an NVIDIA product. API breakages might be more frequent until we reach version 1.0.

DAQIRI provides direct NIC hardware access in userspace, bypassing the Linux kernel network stack to achieve the highest possible throughput and lowest latency for Ethernet frame transmission and reception. It targets NVIDIA ConnectX-6 Dx and later NICs and supports GPU direct memory access (GPUDirect) for zero-copy data paths between the NIC and GPU.

📖 Live documentation: nvidia.github.io/daqiri

Requires an NVIDIA SmartNIC (ConnectX-6 Dx or later) and a GPUDirect-capable NVIDIA GPU. Tested on the NVIDIA DGX Spark, NVIDIA IGX platform, and an x86_64 RTX Pro server. See Getting Started for the full requirements list.

Features

• High Throughput — Sustained line rate with proper hardware and tuning.
• Low Latency — Direct access to NIC ring buffers; most latency is PCIe transit only.
• GPUDirect — Receive data directly into GPU memory via two modes:
  • Header-Data Split: Headers to CPU, payload to GPU (recommended for most workloads).
  • Batched GPU: Entire packets to GPU memory (maximum bandwidth, GPU-side parsing required).
• Burst file writes — Write received bursts as raw packet files or appendable PCAP captures. Host-backed buffers use POSIX writes; CUDA device-backed buffers can use cuFile/GDS.
• Flow Steering — Configure the NIC's hardware flow engine to route packets by UDP source/destination port.
• RDMA — RDMA verbs (READ, WRITE, SEND) over RoCE on Ethernet NICs or InfiniBand.

Backends

Backend	Config value	Description
DPDK	dpdk	Userspace packet processing with DPDK mbufs and rings.
RDMA	rdma	RDMA verbs via libibverbs over RoCE or InfiniBand (client/server model).
Socket	socket	Linux kernel sockets (UDP/TCP), plus a RoCE path that delegates to the RDMA backend. Selecting socket automatically builds rdma.

Limitations

• TX header-fill helpers currently support UDP only.

Quick Start

Pick one of the two build paths below.

Container build (recommended) — bundles all user-space dependencies, including a patched DPDK with dmabuf support, so no host-side dependency setup is required:

BASE_TARGET=dpdk DAQIRI_MGR="dpdk socket rdma" scripts/build-container.sh

Set BASE_IMAGE=torch to build on top of NGC PyTorch instead of the default CUDA base — useful for Torch / TensorRT inference workflows that ingest packets directly into GPU memory.

Bare-metal CMake build — use if you have all dependencies installed on the host (see the Dockerfile for the full list):

cmake -S . -B build -DBUILD_SHARED_LIBS=ON -DDAQIRI_BUILD_PYTHON=OFF -DDAQIRI_MGR="dpdk socket rdma"
cmake --build build -j
cmake --install build --prefix /opt/daqiri

Host-memory burst file writes do not require GPUDirect Storage. Enable cuFile support for CUDA device-memory file writes with -DDAQIRI_ENABLE_GDS=ON; this requires cufile.h and libcufile in the build environment. At runtime, regular GDS writes through NVIDIA's nvidia-fs path require the nvidia-fs kernel module to be loaded and the target storage stack to be reported as supported by gdscheck.py -p.

Container build:

BASE_TARGET=dpdk DAQIRI_MGR="dpdk rdma" scripts/build-container.sh

See Getting Started for requirements, CMake options, and running the benchmarks.

Documentation

Reference material for the DAQIRI codebase:

• Getting Started — System requirements, build/install instructions, and CMake options
• Configuration Reference — Full YAML config reference for all backends
• API Guide — BurstParams, RX/TX workflows, buffer lifecycle, status codes
• Contributing — Contribution guidelines, coding standards, DCO sign-off

Tutorials

Step-by-step walkthroughs to get hands-on:

• Background — Kernel-bypass and GPUDirect concepts
• System Configuration — NIC drivers, link layers, GPUDirect, hugepages, CPU isolation, GPU clocks
• Benchmarking Examples — run daqiri_bench_raw_gpudirect with a loopback test
• Understanding the Configuration File — annotated YAML walkthrough

License

Apache 2.0 — see LICENSE for details.