Integrating RISC-V + NVLink GPU Workloads into Your CI for Accurate Performance Testing

Hook: Why your CI pipelines must run real RISC-V + NVLink Fusion workloads

Slow CI feedback, flaky results, and mysterious performance regressions are endemic when developers validate advanced AI/accelerated workloads on simulated hardware or mismatched testbeds. In 2026, teams adopting RISC-V CPUs with NVIDIA's NVLink Fusion fabric need CI pipelines that run true hardware-in-the-loop (HIL) benchmarks to measure latency, bandwidth, and scaling behavior accurately. This article shows how to design CI pipelines that provision mixed RISC-V + GPU topologies, schedule topology-aware jobs, run repeatable benchmarks, and collect trustworthy performance data for automated gating and release decisions.

The 2026 context: Why this matters now

Late 2024 through 2025 saw two converging trends: broader RISC-V silicon adoption (SiFive and others expanding IP stacks) and NVIDIA's push of NVLink Fusion as a high-bandwidth interconnect for GPU-centric fabrics. By late 2025, SiFive announced integration plans with NVLink Fusion, making heterogeneous RISC-V + GPU platforms a realistic production target for AI datacenters and edge AI appliances.

That convergence raises three challenges for CI teams in 2026:

• Hardware topology matters — NUMA, NVLink fabric, and PCIe pathways change performance characteristics.
• Emulation/simulation is insufficient for accurate latency and fabric-level behavior; you need HIL.
• Provisioning, scheduling, and orchestration must become topology-aware and cost-conscious to keep CI feedback fast.

High-level architecture: What a CI pipeline for RISC-V + NVLink Fusion looks like

Design pipelines around three layers: infrastructure provisioning, orchestration & scheduling, and benchmark execution & telemetry. Each requires tooling that understands heterogeneous hardware and fabric topologies.

1. Provisioning — bare-metal provisioning (Ironic, MAAS), firmware/BIOS and driver tooling, and fabric configuration (NVLink fabric manager or vendor APIs).
2. Orchestration & Scheduling — Kubernetes with device plugins and Topology Manager, or batch schedulers like SLURM/Volcano for high-throughput HIL tests.
3. Benchmark Execution & Telemetry — benchmark suites (micro and macro), telemetry collectors (Prometheus, NVIDIA DCGM, perf), trace collection (Nsight Systems), and result normalization/analysis pipelines.

Required capabilities

• Topology discovery: tools to map NVLink and CPU/GPU relationships (nvidia-smi topo -m, DCGM topology APIs).
• Driver & firmware management: reproducible driver installs, kernel modules for RISC-V boards, and NVLink firmware images.
• Non-invasive measurement: low-overhead counters from DCGM, perf, and hardware PMUs to avoid perturbing results.

Provisioning the testbed: Metal-as-a-Service + Fabric setup

To run exact RISC-V + NVLink workloads you must provision real hardware on demand and ensure the NVLink fabric is configured correctly. Treat your lab like cloud infrastructure:

• Use MAAS or OpenStack Ironic to provision bare-metal nodes (RISC-V hosts and GPU nodes).
• Automate firmware/BIOS and driver flash using vendor toolchains and reproducible images (PXE + cloud-init or a pre-baked OS image).
• Expose an API layer (REST or gRPC) your CI system can call to claim and release nodes.

Example: an API flow your CI must support

1. claim nodes with topology hints (GPU count, NVLink ports)
2. flash kernel/firmware image for RISC-V nodes
3. install NVIDIA driver + DCGM + Fusion fabric manager on GPU nodes
4. validate topology (nvidia-smi topo -m) and mark hosts ready

Sample provisioning script (pseudo)

# request nodes with MAAS
curl -X POST https://maas.example/api/1.0/nodes/claim -d '{"roles": ["riscv-host","nvlink-gpu"], "topology_hint": {"nvlink_ports":2}}'
# wait for node states
# flash images
maas-cli flash --node-id $NODE --image my-riscv-2026.img
# install NVIDIA drivers
ssh root@$GPU_NODE 'apt-get update && apt-get install -y nvidia-driver dcgm nvlink-fusion-manager'
# validate
ssh root@$GPU_NODE 'nvidia-smi topo -m'

Orchestration patterns: Kubernetes, device plugins, and topology-aware scheduling

Kubernetes is the natural choice for packaging and running CI workloads, but it needs extensions for HIL:

• Install the NVIDIA Device Plugin and GPU Operator. The operator manages GPU driver lifecycle and DCGM services.
• Enable Kubernetes Topology Manager to coordinate CPU, memory, and device alignment on each node.
• Use node labels (e.g., hw.riscv=true, fabric.nvlink.zone=zone-a) and taints/tolerations to reserve mixed nodes for test jobs.

For multi-node NVLink Fusion jobs that span fabric-connected GPUs, you will need a scheduler that understands the NVLink fabric topology. Two approaches work well:

1. Topology-aware Kubernetes scheduler extension (custom scheduler or scheduler extender) that receives topology hints and places pods onto nodes where GPUs are NVLinked.
2. Batch scheduler (SLURM, Volcano) integrated with bare-metal provisioning for larger, multi-node runs that demand tight fabric locality.

Topology-aware scheduling example (Kubernetes)

Key elements:

• Device plugin reports GPUs with topology IDs
• Scheduler extender queries fabric manager for NVLink groups
• Pods request a GPU placement hint (e.g., nvlink-group: g-12)

# pod manifest fragment
apiVersion: v1
kind: Pod
metadata:
  name: nvlink-benchmark
spec:
  nodeSelector:
    fabric.nvlink.zone: "zone-a"
  containers:
  - name: bench
    image: mytest/bench:v2026
    resources:
      limits:
        nvidia.com/gpu: 4
    env:
      - name: NVLINK_GROUP
        value: "g-12"

CI pipeline design: build -> provision -> run -> collect -> analyze -> teardown

Design the pipeline with explicit, reproducible steps and gates. Use declarative pipelines (Tekton, GitLab CI, or Jenkins Pipeline) so tests are auditable.

Typical pipeline stages

1. Checkout & Build — build artifacts and container images; record exact commit and image digest.
2. Claim Hardware — request HIL nodes with topology hints from your provisioning API.
3. Boot & Validate — install drivers, validate NVLink topology, run smoke tests (microbenchmarks).
4. Run Benchmarks — micro (bandwidth, latency) and macro (training/serving) with controlled repetitions.
5. Collect Telemetry — DCGM, perf, Nsight traces; store raw traces and derived metrics.
6. Analyze & Gate — compare to baseline; pass/fail or degrade with thresholds, generate artifacts.
7. Teardown — release nodes and archive artifacts.

GitLab CI example (snippet)

stages:
  - build
  - claim
  - validate
  - bench
  - collect
  - teardown

claim_nodes:
  stage: claim
  script:
    - curl -X POST https://maas.example/api/claim -d '{"roles":["nvlink-gpu","riscv-host"],"count":2}' > claim.json
    - export NODE_IDS=$(jq -r '.nodes|join(",")' claim.json)
  when: manual

Benchmarking: what to measure and how to normalize results

You must measure fabric-level metrics (NVLink bandwidth/latency), GPU counters, CPU-side behavior on RISC-V hosts, and system-level effects (PCIe, memory bandwidth). Recommended metrics:

• Fabric: NVLink bandwidth, link utilization, topology map.
• GPU: SM utilization, memory throughput, DRAM bandwidth, power.
• RISC-V host: syscall latency, interconnect throughput, CPU stall cycles, cache miss rates.
• End-to-end: time-to-first-byte, epoch time (for training), throughput (samples/sec), tail latencies.

Use DCGM for GPU counters, Nsight Systems for end-to-end traces, and Linux perf (or vendor PMUs on RISC-V) for CPU events. Export all metrics to Prometheus and store traces in an object store with unique run IDs. For end-to-end trace discoverability and artifact hygiene, borrow practices from portable edge kit playbooks and edge-first architectural guides.

Sample commands

# NVLink topology
ssh root@$GPU_NODE 'nvidia-smi topo -m'
# DCGM sample collection
ssh root@$GPU_NODE 'dcgmi discovery -l && dcgmi stats -p -i 10 -d 60 --output /tmp/dcgm.json'
# RISC-V perf example
ssh root@$RISCV_NODE 'perf stat -e cycles,instructions,cache-misses -p $(pidof myapp) sleep 60'

Result collection, baselining, and automated gating

Collect raw traces and metrics, then derive stable indicators for regression detection. Key practices:

• Store raw artifacts (traces, logs, DCGM exports) with immutable identifiers.
• Derive normalized metrics (e.g., bandwidth per GPU, cycles per sample) so different runs are comparable.
• Baseline management: keep a rolling baseline per topology and software stack; when drivers or firmware change, create a new baseline. Tie baseline processes into your monitoring and observability workflows to detect drift early.
• Statistical gating: don't gate on single-run variance; require significance over N runs (commonly N=5–10) and use confidence intervals.

Automate gating rules in CI: a job fails if the mean metric deviates more than X% and is statistically significant. Add a manual override with a human review for edge cases.

Cost, reliability, and speed trade-offs

HIL tests are expensive and slow if you don't manage them. Strategies practiced by leading teams in 2025–2026:

• Tier tests: run cheap smoke tests on push, full HIL benchmarks on merge to main or nightly schedules.
• Node pooling: maintain a hot pool of pre-provisioned, warmed GPU nodes for low-latency jobs; only flash RISC-V nodes when needed. Consider guidance from portable edge kits for keeping nodes warm and ready.
• Preemptible runs: allow longer, low-priority jobs to run on idle capacity with checkpointing support.
• Power & cost telemetry: capture chassis power via Redfish/IPMI and chargeback to projects. For vendor-neutral telemetry and edge power monitoring, study patterns from edge analytics buyers' guides.

Security and trustworthiness

Performance CI must be reproducible and auditable. Recommendations:

• Use signed firmware images and secure boot on RISC-V hosts.
• Attest hardware using TPM-based remote attestation where possible.
• Isolate CI runners and ensure logs/artifacts have immutability and access controls.

Trust in measurements comes from reproducibility: the same configuration must produce the same result within expected variance.

Advanced strategies and future-proofing (2026+)

As fabrics become denser and RISC-V feature sets expand, expect:

• Fabric-aware orchestration to be a standard part of cluster schedulers — expect upstream K8s topology APIs to evolve in 2026 to include NVLink Fusion hints.
• Hybrid simulation+HIL workflows where early-stage checks run in fast simulators, and final validation executes on a small set of representative HIL topologies. See discussions around serverless edge and low-latency testing patterns for inspiration on combining fast simulation with edge runs.
• Declarative hardware manifests (like Kubernetes manifests for hardware) to codify topology guarantees for CI jobs.

Example: end-to-end Tekton pipeline snippet

apiVersion: tekton.dev/v1beta1
kind: Pipeline
metadata:
  name: nvlink-bench-pipeline
spec:
  tasks:
  - name: claim-nodes
    taskRef:
      name: claim-nodes-task
  - name: validate
    taskRef:
      name: validate-topology-task
    runAfter:
    - claim-nodes
  - name: run-bench
    taskRef:
      name: run-bench-task
    runAfter:
    - validate
  - name: collect
    taskRef:
      name: collect-artifacts-task
    runAfter:
    - run-bench

Checklist: ready-to-run CI for RISC-V + NVLink Fusion

• Provisioning API (MAAS/Ironic) with node labeling for NVLink groups
• Reproducible OS/images with signed firmware and driver bundles
• Kubernetes with NVIDIA Device Plugin, GPU Operator, and Topology Manager
• Scheduler extension or batch scheduler aware of NVLink fabric
• Benchmark catalog (micro + macro) with repeatable harnesses
• Telemetry stack: DCGM, Nsight, perf, Prometheus, object store for traces
• Baseline database and statistical gating rules
• Cost telemetry (IPMI/Redfish) and job-level chargeback

Case study (hypothetical but representative)

Acme AI integrated a RISC-V + NVLink Fusion HIL pipeline into their CI in Q4 2025. They used MAAS for provisioning, Kubernetes with a custom scheduler extender, and Tekton pipelines for orchestration. Results:

• Median feedback loop for critical performance PRs went from 48 hours (manual lab runs) to under 6 hours.
• Regression detection improved — previously missed fabric-level regressions were caught before release.
• By tiering tests and using a hot pool of GPU nodes, infrastructure cost for CI decreased by a reported 30–40% over three months.

These outcomes mirror early adopter reports in late 2025 and are realistic targets for teams that invest in HIL pipelines.

Actionable takeaways

• Don't trust simulations alone — run critical fabric-sensitive tests on real hardware.
• Automate provisioning with MAAS/Ironic and expose that to pipelines via APIs.
• Use topology-aware scheduling (K8s Topology Manager + device plugins or batch schedulers) to guarantee placement.
• Collect both raw traces and normalized metrics, and enforce statistical gates to avoid noisy false positives.
• Tier tests to balance speed and cost; keep a hot pool for low-latency critical runs.

Where to go next

If you're evaluating adoption of RISC-V + NVLink Fusion topologies, start with a small, codified HIL pipeline: one representative topology, automated provisioning, and 3–5 repeatable benchmarks. From there you can expand the topology matrix and integrate statistical gating into your release process. For practical, hands-on guidance about keeping nodes warm and portable edge workflows, see our notes on portable edge kits and edge-first architecture patterns.

Call to action

Ready to stop guessing about real-world performance? Get a reproducible pipeline blueprint and sample Tekton/GitLab CI manifests tuned for RISC-V + NVLink Fusion. Contact mytest.cloud to access our reference implementations, benchmark catalog, and a 30-day lab trial. Ensure your releases are backed by trustworthy performance data — start building HIL-aware CI today.

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