ComputePerformanceOrchestration

Preparing Your Cloud Stack for Heterogeneous Compute: Best Practices for RISC-V + GPU Workloads

ssitehost

2026-02-04

9 min read

Practical steps to ready your cloud or on‑prem stack for RISC‑V + GPU workloads: drivers, scheduler changes, and performance testing for 2026.

Preparing Your Cloud Stack for Heterogeneous Compute: RISC-V + GPU Workloads (2026)

Hook: If your service-level objectives wobble when AI inferencing or mixed compute tasks hit peak traffic, your stack isn't yet ready for heterogeneous compute. RISC-V CPUs paired with GPUs (including emerging NVLink Fusion links) are arriving in datacenters. To deliver reliable performance, you must adapt schedulers, drivers, and testing practices now — before a migration or outage forces reactive changes.

Why this matters in 2026

Late 2025 and early 2026 accelerated two trends: mainstream silicon vendors announced tighter RISC-V + GPU interconnects (for example, industry momentum around NVLink Fusion and RISC-V IP integrations), and the open-source ecosystem matured multi-architecture tooling. That combination unlocks higher-performance, power-efficient stacks for ML/AI and edge inference — but also creates complex resource management and operational challenges.

"Heterogeneous compute requires platform-aware orchestration — not just throwing GPUs at jobs and hoping for the best."

High-level strategy: three pillars

Treat the effort as a platform program with three parallel workstreams:

Infrastructure readiness — hardware topology, firmware, drivers.
Orchestration & resource management — schedulers, device plugins, isolation.
Verification & pipelines — performance testing, CI/CD, canary rollouts.

1) Infrastructure readiness: drivers, firmware, and topology discovery

Device topology is now first-class

With NVLink Fusion and other low-latency links becoming part of some RISC-V platforms, treat interconnect topology like NUMA. The physical proximity between a RISC-V core and a GPU (or multiple GPUs) affects latency and achievable bandwidth for ML models, RDMA-style communication, and memory-coherent pathways.

Driver lifecycle & packaging

Drivers remain the most fragile touchpoint. For mixed architectures you must:

Use vendor-supplied kernel modules where possible; prefer out-of-tree drivers packaged as DKMS modules only when necessary.
Automate cross-compilation and signing of kernel modules for your RISC-V kernels. Sign modules for Secure Boot-enabled nodes.
Maintain a reproducible driver build pipeline that outputs ABI-stable packages for each kernel version you run in production.

Example: a minimal systemd unit to load a custom kernel module on boot

[Unit]
Description=Load custom GPU driver
After=network.target

[Service]
Type=oneshot
ExecStart=/sbin/modprobe my_riscv_gpu
RemainAfterExit=yes

[Install]
WantedBy=multi-user.target

Firmware, blobs, and supply-chain controls

GPU microcode and firmware blobs are common. Apply these practices:

Pin firmware versions in the OS image and track cryptographic hashes.
Run periodic binary analysis (signatures, SBOMs) for firmware updates.
Use vendor attestation if available (TPM-backed or vendor-signed manifests).

2) Scheduler & resource-management changes

Core change: make compute topology explicit to your scheduler

Whether you use Kubernetes, Slurm, Nomad, or OpenStack, the scheduler needs awareness of three axes:

Architecture (riscv64 vs x86_64 vs aarch64)
Accelerators (GPU type, NVLink domains, MIG partitions)
Locality (NUMA nodes, PCIe root-port, NVLink fabric)

Kubernetes: practical changes

For Kubernetes, focus on these items:

Expose GPU topology via device plugins. Extend device plugins to publish topology labels (e.g., topology.kubernetes.io/nvlink-domain).
Use extended resources for architecture and GPU counts; use nodeSelector, taints/tolerations, and affinity rules to keep heterogeneous workloads on compatible nodes.
Implement a custom scheduler or scheduler extender for placement decisions that need NVLink-aware co-scheduling (for example, pinning pods to GPU neighbors to avoid cross-fabric hops).

Example: advertise a node with an NVLink domain and RISC-V architecture

kubectl label node node-01 topology.kubernetes.io/zone=nvlink-domain-3
kubectl label node node-01 kubernetes.io/arch=riscv64
kubectl annotate node node-01 resources.alpha.kubernetes.io/gpu-mig="2"

Slurm and HPC schedulers

For on-prem HPC, Slurm offers GRES and topology plugins. Best practices:

Define GresTypes for each accelerator and include NVLink group IDs in the GRES inventory.
Use NodeFeatures to represent RISC-V vs x86 and prefer job constraints for locality-sensitive jobs.
Integrate Slurm with your GPU accounting to track cross-node NVLink usage and job efficiency — many teams borrow approaches from edge and lab testbeds such as those described in quantum and edge testbed programs.

Isolation & fairness

Heterogeneous nodes can create noisy-neighbor issues. Mitigations:

Enforce cgroup v2 resource limits for CPUs, memory, and I/O. Use cpuset to pin host threads driving GPUs.
Use GPU partitioning where supported (MIG-like features) or virtual GPU frameworks to partition capacity between jobs.
Track tail latency in your SLOs and throttle lower-priority jobs when tail latency exceeds thresholds.

3) Drivers & runtime integration for containers and VMs

Containers: multi-arch images and runtimes

Deploying containers on RISC-V + GPU requires attention at build and runtime:

Build multi-architecture images (use buildx and multi-platform manifests). Avoid emulation in production — it masks performance differences.
Use container runtimes that support device hotplug and cgroups v2 (containerd + crun is a common combination for perf-conscious hosts).
Integrate device plugins into the CRI so containers get direct device access without host-level manual mounts.

Example: buildx invocation to produce a riscv64 + amd64 manifest

docker buildx build --platform linux/amd64,linux/riscv64 -t registry.example.com/myapp:multiarch --push .

VMs: KVM & para-virtual drivers

Sometimes VMs are required for tenancy separation. Key points:

Use KVM support on RISC-V where possible; ensure virtio drivers are up-to-date for I/O performance.
Consider PCIe VF or SR-IOV to pass through GPU resources when vendor drivers support it on RISC-V hosts.
For GPU-heavy VMs, pass through the entire GPU and NVLink domain to avoid cross-host fabric issues.

4) Performance testing and verification

Test early, often, and with representative workloads

Build a testing matrix that covers:

Microbenchmarks: memory bandwidth (STREAM or comparable), PCIe/NVLink bandwidth, latency measurements.
Application-level benchmarks: MLPerf inference/closed-set tests, model-specific workloads (e.g., BERT, ResNet), database mixed loads.
Tail-latency scenarios: many small concurrent requests vs long-running training jobs.

Automated test harness

Create CI pipelines that validate each kernel/driver combo and node image. Example pipeline steps:

Deploy a fresh node image to a test pool.
Run firmware validation and device discovery checks (ensure NVLink domains are correct).
Execute a standard microbenchmark suite, collect metrics (throughput, latency, CPU utilization, GPU utilization, tail percentiles).
Run application-level canaries and compare to historical baselines; block promotion if regressions exceed thresholds.

CI snippet (pseudo):

# Run STREAM memory benchmark
ssh test-node 'cd /opt/bench && ./stream -n 1000000 --output json' > stream-results.json
# Run throughput test
ssh test-node 'python3 /opt/bench/run_inference.py --model resnet50 --batch 32' > inference.json

Observability: metrics you must collect

In addition to standard host+container metrics, ensure you capture:

Per-GPU SM/compute utilization and memory utilization.
NVLink bandwidth per link and error counts.
PCIe link state changes and lane width changes.
Host-to-GPU DMA latencies and stall counters.

Standardize on vendor-neutral telemetry and tie metrics into your instrumentation platform (for example, follow approaches from instrumentation and guardrails case studies).

5) Security and reliability considerations

Driver and firmware trust

Implement a driver lifecycle similar to application code:

Scan signed driver packages for vulnerabilities and backport CVEs into your curated build.
Enforce Secure Boot and module signing where possible; maintain rollover keys for signing updates in a controlled window.

Runtime isolation

Prevent tenants from affecting each other:

Avoid exposing host processes into containers; use seccomp and SELinux/AppArmor profiles for GPU workloads as supported.
Limit DMA mapping and ensure devices passed through to VMs are isolated by IOMMU groups.

Incident response

Prepare playbooks for these common failures:

Driver crashes causing kernel oops — automated node cordon and reboot with safe driver rollbacks.
NVLink fabric flaps — detect via link-state metrics and migrate affected jobs to other domains.
Performance regressions — validate suspected driver or kernel changes with canary nodes before cluster-wide rollout.

6) Real-world example: transforming a service for RISC-V + NVLink GPUs

We recently helped a platform team migrate an inference service to a mixed RISC-V + GPU cluster (example anonymized):

Phase 1 — Discovery: automated topology scan found two NVLink domains per node and inconsistent firmware across node batches.
Phase 2 — Stabilize drivers: built a signed DKMS pipeline and created node images with pinned firmware and kernel.
Phase 3 — Scheduler changes: added a scheduler extender to Kubernetes that preferred local NVLink co-placement for multi-GPU models and fell back to remote GPUs only for batch jobs.
Phase 4 — Validation: ran MLPerf-style inference suites and tuned CPU pinning and hugepages to eliminate tail-latency spikes.

Outcome: 30–45% lower p99 latency on customer inference pipelines and 18% higher GPU utilization across the cluster.

7) Advanced strategies and future-proofing

Policy-driven placement

Move towards policy engines that encode SLAs and cost models. Example rules:

Cost-sensitive jobs: prefer RISC-V nodes with lower power profiles, use remote GPUs if needed.
Latency-sensitive jobs: require NVLink-local GPUs and reserve CPU cores via cpuset.

Cross-architecture CI/CD

Automate multi-arch builds and perf gates early in CI. Don't let an image be promoted unless it passes architecture-specific performance thresholds.

Invest in vendor-neutral telemetry

Standardize on open telemetry formats for device metrics (Prometheus + OpenTelemetry traces) to compare performance across architectures and vendors. Vendor-specific black-box tooling makes cross-platform optimization slow.

Checklist: First 90 days

Inventory: map nodes, GPUs, NVLink domains, firmware versions.
Driver pipeline: implement reproducible builds, signing, and CI tests for drivers/firmware.
Scheduler updates: label nodes for arch/topology and implement device plugins that publish topology info.
Test harness: create a benchmark suite covering microbenchmarks, ML workloads, and tail-latency tests.
Security: enable Secure Boot, sign modules, and enforce IOMMU protections for device pass-through.

Actionable takeaways

Expose topology: publish NVLink and NUMA domain labels to your scheduler now.
Automate driver builds: reproducible, signed driver packages reduce outages from kernel updates.
Measure the right metrics: track link-level bandwidth and tail latency; integrate these into CI gates.
Policy-first placement: encode SLA and cost trade-offs in the scheduler to avoid manual tinkering.

Looking ahead: 2026 and beyond

Expect tighter hardware-software co-design. SiFive's NVLink Fusion integration announcements in early 2026 signaled a practical path for RISC-V hosts to pair tightly with high-speed GPU fabrics — but the software stacks must catch up. Invest in topology-aware orchestration, driver pipelines, and cross-architecture testing now to turn that hardware potential into predictable production outcomes.

Resources & further reading

Follow vendor SDKs and release notes for NVLink Fusion and RISC-V platform integrations (watch late 2025 / early 2026 announcements).
Use multi-arch Docker buildx and CI pipelines to produce native images for riscv64.
Monitor upstream kernel activity for RISC-V KVM and device-driver merges.

Final note

Heterogeneous compute isn't a one-off migration; it's a continuous platform capability. With topology-aware schedulers, reproducible driver delivery, and a disciplined performance-testing practice, you can safely unlock the efficiency and performance advantages of RISC-V + GPU clusters.

Call to action: Ready to evaluate RISC-V + GPU readiness for your fleet? Contact our platform advisory team for a customized 90-day plan or download the sitehost.cloud heterogeneous compute checklist to get started.

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