kernelrisc-vdrivers

Kernel and Driver Workflows for NVLink on RISC-V: Practical Guide for Systems Engineers

wwebdecodes

2026-02-11

11 min read

Practical how‑to for integrating NVLink into RISC‑V: kernel drivers, DMA, PCIe bridging, firmware and repeatable testing for system integrators.

Hook: Why integrating NVLink into RISC-V matters — and where teams get stuck

System integrators building GPU-connected RISC-V silicon hit a recurring set of pain points: missing kernel hooks for DMA across new fabrics, PCIe<>NVLink bridging complexity, opaque firmware/device-tree handoffs, and testing strategies that don't scale to hardware variants. The rise of RISC‑V in AI and edge compute in 2025–2026 — and vendor moves to expose NVLink Fusion on RISC‑V platforms — makes solving these issues urgent.

Quick summary and what you’ll get from this guide

This article gives a practical, step‑by‑step workflow for integrating NVLink into RISC‑V platforms: kernel driver architecture, DMA management, PCIe bridging patterns, firmware and device‑tree tips, and repeatable testing strategies for CI and hardware‑in‑the‑loop (HIL) validation. Expect code snippets, DT overlays, kernel probe patterns, and realistic debugging/playbook steps you can take to ship silicon fast.

Context: Why 2026 is different (short trends)

In late 2025 and into 2026, ecosystem moves — including partnerships to add NVLink Fusion support on RISC‑V IP platforms — accelerated demand for native NVLink support on non‑x86 hosts. That creates two new realities:

NVLink is now being treated as a first‑class interconnect for RISC‑V SoCs targeting AI and HPC workloads.
Teams need production‑grade kernel and firmware workflows, not experimental scripts: DMA coherency, IOMMU interactions, secure boot signing, and driver stability matter.

SiFive and others announced vendor announcements (2025–2026) about NVLink Fusion integration with RISC‑V platforms in 2025–2026, turning NVLink from a GPU‑only host feature into a broad interconnect that RISC‑V silicon must support.

High‑level architecture patterns

Before you write code, choose one of three topology patterns; each has different kernel/firmware responsibilities.

1) Direct PCIe Root Complex on RISC‑V SoC + GPU with NVLink

SoC provides PCIe root(s). GPU connects via PCIe, and NVLink sits between GPUs or between GPU and host bridge (NVLink Fusion chipset). Responsibilities:

Linux kernel on RISC‑V acts as PCIe host; drivers enumerate PCIe device, bind NVIDIA kernel modules for GPU.
Driver must handle DMA mapping across IOMMU (if present) and ensure NVLink peer link setup via vendor firmware if needed.

2) NVLink Fabric with a dedicated NVLink‑to‑PCIe bridge

Some designs use a bridge chip that exposes NVLink endpoints as PCIe endpoints. Kernel sees bridge + virtual PCIe devices. Responsibilities:

Bridge driver implements endpoint enumeration and error handling.
DMA flows may cross the bridge and require target‑side IOMMU/DMA mask alignment.

3) Tight NVLink Fusion integration with RISC‑V fabric (native NVLink)

NVLink appears as a fabric interconnect exposed directly to the SoC interconnect (e.g., via a CCIX/NVLink fabric block). This is the most modern approach emerging in 2026: low‑latency peer access and hardware coherency primitives. Responsibilities:

Kernel must expose a proper device binding and runtime PM for the NVLink fabric node.
DMA and cache coherence across the fabric need careful firmware and kernel coordination.

Kernel driver architecture: recommended blueprint

Design drivers using the Linux kernel subsystems that reduce maintenance and reuse stable interfaces.

PCI core & bus_probe: Use standard pci_driver probe/remove patterns for PCIe‑exposed GPUs and bridges.
DMA API: Use dma_map_* and dma_alloc_attrs for coherent/streaming buffers; set proper DMA masks.
IOMMU: Integrate with iommu_domain APIs; accept platform IOMMU ops where present.
VFIO / UIO for passthrough: If exposing GPUs to guest VMs, support VFIO binding and proper iommu groups.
Platform device + DT/ACPI: For native NVLink fabric, register a platform_device using Device Tree bindings so firmware can manage power and topology.

Skeleton PCIe probe for NVLink‑connected device (simplified)

static int nvlink_pci_probe(struct pci_dev *pdev, const struct pci_device_id *id)
{
    int err;

    err = pci_enable_device_mem(pdev);
    if (err) return err;

    /* Set DMA mask (try 64-bit, fallback to 32-bit) */
    if (dma_set_mask_and_coherent(&pdev->dev, DMA_BIT_MASK(64))) {
        if (dma_set_mask_and_coherent(&pdev->dev, DMA_BIT_MASK(32))) {
            pci_disable_device(pdev);
            return -EIO;
        }
    }

    /* Request BARs and map registers */
    err = pci_request_regions(pdev, "nvlink_pdev");
    if (err) goto err_disable;

    /* Map registers and set up DMA engines, interrupts */
    // ioremap() or devm_ioremap_resource()

    /* Initialize fabric semantics: call vendor ops to bring link up */

    return 0;
err_disable:
    pci_disable_device(pdev);
    return err;
}

Key kernel APIs and their roles

pci_enable_device_mem: Reserve PCI regions and prepare device memory access.
dma_set_mask_and_coherent: Configure DMA addressing capabilities; critical for cross‑device DMA over NVLink.
pci_request_regions: Protect BARs and avoid collisions with other subsystems.
iommu_mapping_ops: Map/unmap for devices behind IOMMU when peer access is required.

DMA across NVLink: practical advice

DMA is where most cross‑vendor bugs show up. NVLink extends high‑speed memory semantics — but kernel code must be explicit about mapping/permissions.

Common pitfalls

Assuming 64‑bit DMA available: always check dma_set_mask_and_coherent return.
Inconsistent cache management on RISC‑V: ensure your platform implements coherent DMA or apply explicit dma_sync_* calls.
Not aligning DMA buffer attributes between host and GPU driver (attributes like streaming/coherent matter for NVLink).

Practical DMA sequence

On probe: call dma_set_mask_and_coherent and check result.
Allocate with dma_alloc_attrs if you need special attributes (e.g., DMA_ATTR_SKIP_CPU_SYNC on some pipelines).
For mapping existing kernel buffers, use dma_map_single or dma_map_sg and check for dma_mapping_error.
Call dma_unmap_single after transmission and use explicit sync for non‑coherent domains.

Example: allocating a DMA buffer usable by GPU across NVLink

void *buf = dma_alloc_attrs(&pdev->dev, size, &dma_handle, GFP_KERNEL, DMA_ATTR_ALLOC_SINGLE_PAGES);
if (!buf) return -ENOMEM;
/* Use dma_handle as physical address to program GPU DMA descriptor */

PCIe bridging patterns and tips

Whether NVLink uses a bridge or fabric, your kernel must manage hotplug, error recovery, and endpoint reset behaviors correctly.

Things to validate in bring‑up

PCIe link width and speed reported by lspci -vv and kernel logs.
Correct BARs exposed and decoded by the root complex.
Hotplug and surprise removal callbacks are tested under stress.

Example Device Tree snippet for a PCIe root and NVLink bridge

pcie@100000 {
    compatible = "riscv,pci-host-ecam";
    reg = <0x100000 0x0 0x0 0x100000>; /* example */
    #address-cells = <3>;
    #size-cells = <2>;

    nvlink-bridge@0,0 {
        compatible = "vendor,nvlink-bridge";
        reg = <0 0 0>;
        interrupts = <1>;
    };
};

Firmware and device tree: the handoff you must get right

Firmware (OpenSBI or a vendor firmware) must publish topology: PCIe root, IOMMU identity, and NVLink fabric details. If you use ACPI instead, expose _ADR and _BBN correctly for PCI enumeration.

Firmware checklist

Publish IOMMU domain and bus numbers for devices that cross NVLink.
Expose secure firmware blobs/calls needed by vendor NVLink init (if any) via firmware load interface or device firmware management (DFW).
Ensure memory map for GPU shared buffers is consistent with kernel DMA masks.

Interop with NVIDIA driver stack

NVIDIA's kernel modules expect certain PCIe semantics and DMA behavior. While you won’t modify their modules often, you must ensure your platform meets expectations.

Practical steps

Ensure PCIe device IDs are visible; check with lspci and dmesg for NVIDIA probe logs.
Confirm DMA mask and IOMMU translations so vendor driver can map memory for peer access.
Provide required firmware or use vendor‑supplied firmware blobs via the firmware_class interface (put in /lib/firmware).

Testing strategies: unit, integration, and HIL

Reliable NVLink integration requires a layered test strategy. Automate everything you can.

1) Kernel unit and static tests

Use kselftest to exercise DMA mapping and IOMMU mapping logic.
Use sparse and clang sanitizers for catching RISC‑V specific pointer/unaligned issues.

2) Integration tests (Linux + GPU drivers)

Automate GPU probe and basic functionality tests: nvidia-smi (or vendor equivalent), memory allocation, simple kernel launches.
Run PCIe error recovery tests: inject poison TLPs (where supported) and validate reset sequences.

3) Hardware‑in‑the‑loop (HIL) and CI

Because NVLink and PCIe are timing sensitive, simulation-only tests are not enough. Use one of these approaches:

FPGA prototyping boards that mirror NVLink timing; these are great for early bring‑up.
Small HIL racks with representative GPUs; run nightly regression sets that include heavy DMA traffic, stress tests and thermal cycles.
Edge case: QEMU and Spike do not emulate NVLink; use vendor emulators or physical test gear.

Debugging playbook

When problems appear, follow a consistent flow to isolate the class of failure.

Observe dmesg and pci_scan logs for probe/training failures.
Confirm physical link (link width/speed) with lspci -vv and root complex link registers.
Use trace_printk, tracepoints and ftrace to capture driver probe paths.
Check IOMMU mappings: iommu_dma_show_mappings or vendor debugfs if available.
Run short DMA loopbacks and watch for data corruption using CRC or pattern tests.

Common error signatures and quick fixes

Link training failure: check PHY clocks, reference clocks, and PCIe PHY registers; verify firmware correctly configures PHY PLLs.
DMA mapping errors: ensure DMA mask alignment and that IOMMU map/unmap count matches.
GPU driver stalls during mmap: ensure reserved memory regions are not double‑claimed by other subsystems.

Performance validation and benchmarks

Validate both latency (RDMA-style small transfers) and throughput (large contiguous transfers). Tools and metrics:

Use custom microbenchmarks that perform small atomic reads and measure one‑way latency across NVLink.
Use bandwidth tests with cudaMemcpyPeer (or equivalent) and measure sustained throughput under contention.
Measure CPU overhead using perf and tracepoint sampling while DMA engines are active.

CI and regression: building a repeatable pipeline

For silicon teams, tests must run automatically against every kernel and firmware change. Key practices:

Create hardware lab targets for each SoC variant with known good configurations.
Automate smoke tests that run after firmware, kernel, or driver changes: probe, DMA, and small compute workload.
Capture hardware traces (PCIE/PHY, power rails) at failures for triage.
Maintain a matrix of kernel versions + NVIDIA driver versions and run compatibility tests.

Real‑world checklist: bring‑up to production

Route PCIe lanes and NVLink PHY correctly; verify on board bring‑up with loopback tests.
Confirm firmware exposes IOMMU and bus topology (use DT or ACPI as required).
Implement and test kernel pci_driver probe + DMA masks.
Validate vendor GPU drivers probe and can perform simple allocations and kernels.
Run stress and power/thermal cycling tests for at least 72 hours on hardware.
Automate nightly HIL regression and capture artifacts on failure.

Case study: short integration timeline (example)

Team X (hypothetical) integrated NVLink Fusion on a RISC‑V ML accelerator SoC in 12 weeks. Key steps that saved them time:

Week 1–2: Board and PHY bring‑up with PHY vendor; validated PCIe link stability.
Week 3–4: Firmware published DT exposing IOMMU and NVLink fabric nodes.
Week 5–8: Kernel module skeleton implemented using pci_driver patterns, DMA masks checked, and vendor firmware blobs loaded via firmware_request_nowarn.
Week 9–12: HIL regression and performance tuning with vendor GPU drivers; stabilized crash recovery and hotplug tests.

Future directions and 2026 predictions

As RISC‑V is adopted in AI SoCs through 2026, expect:

More first‑party NVLink support in RISC‑V IP stacks (faster bring‑up, standardized bindings).
Kernel improvements for heterogeneous fabric coherency (extensions to existing DMA/IOMMU APIs to better express fabric semantics).
Vendor toolchains and emulators that support NVLink at functional level to reduce early hardware dependency.

Actionable takeaways — what to do this week

Validate DMA masks early: add test to CI that calls dma_set_mask_and_coherent for all PCI devices and fails on mismatch.
Publish your firmware DT topology to a shared artifact repository so kernel and driver teams can iterate in parallel.
Add a minimal HIL target for nightly smoke tests: probe, allocate DMA buffer, and run a small GPU compute task.
Instrument your driver with ftrace tracepoints around DMA map/unmap and link state changes.

Further references and resources

Look at upstream kernel docs for Documentation/PCI/pci.txt, Documentation/driver-api/dma-mapping.rst, and the IOMMU framework docs. Track vendor announcements (2025–2026) about NVLink Fusion on RISC‑V for reference firmware blobs and device bindings.

Closing — the integration is a systems problem, not a single patch

Integrating NVLink into RISC‑V platforms touches hardware layout, PHY and firmware, kernel driver design, DMA/IOMMU semantics, and test automation. Treat it as a cross‑discipline project: schedule coordinated milestones between silicon, firmware, kernel, and validation teams. The work you do once — standardized DT/ACPI handoffs, DMA tests in CI, and robust probe/error flows — will pay continuous dividends as NVLink becomes a standard interconnect in RISC‑V AI and HPC platforms.

Practical next step: Add the DMA mask check and one probe smoke test to your CI pipeline this week. If you want a starter kernel module template or a device‑tree overlay adapted to your SoC, download the reference repo we maintain and adapt it to your platform.

webdecodes

Contributor

Senior editor and content strategist. Writing about technology, design, and the future of digital media. Follow along for deep dives into the industry's moving parts.