benchmarkGPUhardware

Benchmarking GPU-Accelerated OLAP Engines on NVLink-Connected RISC-V Platforms

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2026-02-10

11 min read

Reproducible benchmark for GPU-accelerated OLAP on NVLink Fusion + RISC-V: measurable throughput, latency, profiling, and tuning guidance for 2026.

Why your OLAP benchmarks are lying to you — and how NVLink Fusion + RISC-V changes the rules

Slow or noisy analytics queries, unpredictable cloud bills, and brittle profiling workflows are core pain points for engineering teams running large-scale GPU-accelerated analytics. As GPU-accelerated analytics move from experiments into production, a new combination — NVLink Fusion and RISC-V CPU nodes — shifts where bottlenecks appear and how you measure end-to-end performance. This article presents a reproducible benchmark for measuring throughput and latency of GPU-accelerated OLAP engines on NVLink-connected RISC-V platforms. It’s built for ops and engineering teams who must validate claims, tune deployments, and predict cost/perf at scale.

What’s new in 2026: why benchmark NVLink on RISC-V now

Two 2025–2026 developments changed the benchmarking landscape:

SiFive and other RISC-V IP vendors announced integration with NVIDIA’s NVLink Fusion, enabling coherent, low-latency, high-bandwidth links between RISC-V hosts and NVIDIA GPUs (announced late 2025 / public references in early 2026).
Commercial OLAP engines with GPU-native execution became mainstream — enterprise investment (for example, big funding rounds for OLAP challengers) shows the trend: vendors are optimizing query engines for GPU memory models and vectorized kernels.

The combination matters because NVLink Fusion changes the performance model: host-to-device transfers are no longer the dominant cost for many workloads when coherent memory and zero-copy semantics are available. Benchmarks that assume CPU-bound transfer stages or x86-specific tuning will mislead teams evaluating RISC-V + GPU stacks.

Benchmark goals and success metrics

Design your benchmark to answer practical procurement and tuning questions. This reproducible benchmark evaluates:

End-to-end latency of representative OLAP queries (parse → plan → execute → materialize).
Sustained throughput (concurrent queries per second at target SLA latency percentiles).
Resource efficiency — GPU memory utilization, host CPU cycles on RISC-V, NVLink usage, and network/disk I/O.
Cost-performance estimate (cloud or TCO proxy): $/Qph (queries per hour) or $/TB scanned — tie this to procurement signals such as hardware pricing and storage cost volatility documented in reports like Preparing for Hardware Price Shocks.
Observability fidelity — how actionable are traces and profiles across RISC-V CPU and GPU execution?

Hardware and software stack (reproducible baseline)

Use these baseline components so other teams can reproduce and compare results.

Hardware

RISC-V server node (SiFive-based or equivalent) with Linux kernel 6.5+ and NVLink Fusion support.
NVIDIA GPUs with NVLink Fusion-capable interconnect (A100/NextGen or equivalent supporting Fusion semantics).
NVLink Fusion fabric configured in coherent mode (enable peer-to-peer and BAR mappings per vendor docs).
NVMe-local storage for dataset host staging; optionally an S3-compatible object store for cloud-like IO patterns.

Software

GPU-accelerated OLAP engine(s) to test: e.g., OmniSci/MapD, ClickHouse with GPU plugins, BlazingSQL / RAPIDS-based engines, and GPU-accelerated DuckDB builds. Include at least one engine that natively supports GPU execution and one hybrid engine.
CUDA toolkit and drivers matching GPU version; NVLink Fusion support library from NVIDIA.
Profiling tools: NVIDIA Nsight Systems (nsys), Nsight Compute (ncu), DCGM for telemetry, and CUPTI for custom tracing.
Host profiling: Linux perf (RISC-V support), eBPF (bpftrace/bcc) for syscall and scheduler traces.
Data generation: TPC-H or TPC-DS dataset generator (scale factor adjustable), parquet/ORC writers with vectored output for columnar layout.
Repro scripts: Dockerfile (or container image), Ansible/Terraform for provisioning, and a top-level orchestrator script (benchmark.sh) that runs scenarios deterministically.

Benchmark design: queries, datasets, and scenarios

To be meaningful and reproducible, define datasets, query mixes, and scenarios clearly.

Datasets

Use TPC-DS scale 100–1000 for interactive throughput and larger for stress testing. Generate parquet files with controlled row group sizes (256MB default) and compression (ZSTD level 3–6).
Include a real-world sample dataset (e.g., e-commerce clickstream or event store) to cover wide-schema, many small columns, and wide-table join patterns.

Query mix

Analytical heavy: complex aggregations, GROUP BY with high-cardinality keys, and window functions.
Join-heavy: multi-way joins that force shuffle or GPU memory spill scenarios.
Ad-hoc: low-latency point lookups and selective predicates (to test predicate pushdown and index efficacy).
Mixed-concurrency: simulates 1–128 concurrent sessions with varying QPS profiles.

Scenarios

Cold run: populate caches empty, run queries to measure cold latencies (data load + execution).
Warm run: after two complete dataset scans, measure steady-state latency and throughput.
Zero-copy mode vs copy-mode: compare NVLink Fusion zero-copy host-GPU memory mapping vs explicit PCIe copy (if supported) to highlight Fusion benefits.
Spill and OOM scenarios: reduce GPU memory to force spills and measure impact on latency and I/O.

Reproducible execution: scripts, containers, and seeds

A benchmark is only trustworthy if others can run it and get consistent signals. Follow these rules:

Publish a Git repository containing Dockerfiles, dataset generation scripts, and an orchestrator (benchmark.sh). Tag a release for each benchmark run.
Fix seeds for data generation and randomized planner choices where possible. E.g., export BENCH_SEED=42 when invoking dataset generator and query runner to match reproducibility advice in modern data pipeline playbooks.
Provide a single YAML file that describes hardware, driver versions, engine versions, and kernel settings. Example: bench-manifest.yaml
Include a verification step: checksums of the generated parquet files and a small QA query that asserts result counts against expected values.

Example orchestrator snippet (conceptual):

./benchmark.sh --manifest bench-manifest.yaml --scenario warm --concurrency 32 --runs 5

Profiling and observability: what to collect and why

Collect both macro and micro signals. NVLink-connected systems require correlating host and device traces to find cross-boundary stalls.

Essential telemetry

Query-level traces: timestamped lifecycle events (submit, plan, start-exec, end-exec, materialize).
GPU metrics: utilization, memory allocation footprints, NVLink link utilization, PCIe usage (for comparison), SM occupancy. Use DCGM and nsys for time-correlated traces.
Host metrics (RISC-V): CPU cycles by process/thread, context-switch rate, scheduler latencies, memory bandwidth, and page faults. Capture with perf + eBPF scripts.
IO metrics: NVMe throughput, disk latency, and object-store tail latencies if applicable.

Profiling tips

Use Nsight Systems (nsys) to capture end-to-end traces including CUDA API calls, kernel launches, and host thread activity. Correlate timestamps with host perf logs — teams that document low-latency capture and tracing workflows (see Hybrid low-latency capture guides) will find the correlation step essential.
Run Nsight Compute (ncu) on representative kernels to find memory-bound vs compute-bound hotspots.
Collect CUPTI counters for copy vs kernel time; when NVLink Fusion zero-copy is enabled, validate that memcpy overheads drop sharply.
On the RISC-V side, use perf record and flamegraphs to identify planner and I/O bottlenecks. eBPF tools (bcc or bpftrace) are invaluable for syscall hot paths.

Interpreting results: what to watch for

When you run the benchmark, don’t just report QPS. Answer these questions:

Does enabling NVLink Fusion reduce host copy time? If so, by how much at 95th percentile latency?
Are kernels compute-bound or memory-bound? ncu will show % of time in memory-bound stages.
Does RISC-V host CPU time become the new bottleneck for small selective queries (planning, deserialization)?
At what concurrency does GPU memory pressure cause spills and trigger disk IO? Measure the knee point where throughput flattens or latency explodes — understanding disk and power constraints (see micro-datacentre orchestration patterns) helps interpret spill impact (Micro-DC PDU & UPS).
How reproducible are the measurements across runs (standard deviation) and across different machines with the same manifest?

Tuning knobs and practical optimizations

Here are actionable tuning steps that proved effective in cross-platform testing and can be applied as part of the benchmark pipeline.

Data format and layout

Use columnar formats (Parquet/ORC) with tuned row-group sizes (128–512MB) and ZSTD compression for a balance between IO and CPU decompression on RISC-V hosts.
Enable column pruning and predicate pushdown in the engine so early filters reduce GPU work.

Memory management

Enable NVLink Fusion zero-copy where supported to avoid explicit host-to-device memcpy for hot datasets.
Reserve a GPU memory pool to avoid fragmentation; tune spill thresholds conservatively to prevent sudden OOMs — vendor notes and GPU lifecycle guidance (see discussions about GPU end-of-life and replacement planning) inform long-term upgrade strategies.

Execution and parallelism

Push heavy filters and aggregations to the GPU. Keep control-plane (planning, metadata ops) on the RISC-V host.
Match engine thread pools to the hardware: leave headroom for OS and NVLink background work; on RISC-V hosts target 70–80% of logical cores for engine threads.

NVLink and system settings

Confirm BAR and P2P settings in firmware/BIOS and enable IOMMU mappings recommended by vendor docs for NVLink Fusion.
Disable unnecessary kernel-level power management that can introduce latency spikes; use conservative CPU governor settings for consistent results.

Cost and TCO: measuring $/Qph and $/TB scanned

Benchmarking should feed cost models. Capture instance cost, storage cost, and GPU amortization assumptions. Two approaches work well:

Micro-cost model: compute CPU-hour and GPU-hour per query, convert to $ using your procurement pricing and storage sensitivity reports (see hardware price analysis).
Macro-cost model: use measured throughput under steady-state and derive $/Qph and $/TB scanned. This is practical for purchasing comparisons between x86+PCIe and RISC-V+NVLink systems.

Include sensitivity analysis: how does cost-per-query change if average latency target drops from 500ms to 100ms? Use these numbers to make procurement trade-offs and align with cloud migration plans such as those that consider sovereign cloud options (EU sovereign cloud migration).

Case study: interpreting a run (example patterns — adapt to your data)

In a representative warm-run of an analytic mix on a RISC-V + NVLink system you may see:

30–60% reduction in host-to-device copy time when NVLink Fusion zero-copy is enabled vs PCIe copy path.
GPU kernel time dominates on aggregation-heavy queries, but for highly selective ad-hoc queries RISC-V host planning time becomes significant — invest in faster planner code paths or offload parts of planning to lightweight GPU-friendly routines. Hiring practices that emphasize GPU-aware skills help here (hiring data engineers in a ClickHouse world).
Under high concurrency, GPU memory pressure leads to spill IO that increases 95th percentile latency non-linearly. Tuning spill thresholds and incrementally increasing GPU memory delivers better throughput per dollar than simply adding GPUs.

"NVLink Fusion moves the bottleneck from copy bandwidth to algorithmic GPU kernel efficiency and host planning latency."

Common pitfalls and how to avoid them

Comparing wall-clock QPS without controlling dataset layout and compression — normalize inputs first.
Ignoring reproducibility — publish manifests and seeds, and validate checksums on generated data.
Not correlating host and device traces — without correlation you’ll misattribute stalls to the wrong layer.
Assuming x86 optimizations directly map to RISC-V — instruction-level differences and kernel library performance matter (e.g., vectorized libraries tuned for x86 may behave differently on RISC-V).

Future predictions and what to watch in 2026+

Based on late-2025/early-2026 signals, expect these trends:

Broader RISC-V deployment in datacenter silicon as SiFive and others produce higher-performance IP with coherency features optimized for accelerator-attached architectures.
Stronger standards for GPU-OLAP profiling — expect vendor-agnostic tooling to better correlate host and device stacks and to include NVLink-level metrics in open telemetry standards. Operational dashboards and runbook integration will be important; see design patterns for resilient observability stacks (operational dashboards).
More GPU-native query engines and hybrid planners that split work optimally between RISC-V control-plane and GPU data-plane.
Cost models will include interconnect amortization — NVLink Fusion-enabled platforms will be evaluated not only on raw bandwidth but on end-to-end efficiency per dollar.

How to get the reproducible benchmark and start testing today

Follow these steps to run the benchmark in your environment:

Clone the public benchmark repo (example: your-org/gpu-olap-nvlink-bench). The repo contains: Dockerfiles, bench-manifest.yaml, dataset-gen scripts, and benchmark.sh.
Provision hardware matching bench-manifest.yaml or update the manifest to your available machines.
Generate datasets with fixed seeds: ./generate_data.sh --scale 100 --seed 42
Run warm and cold scenarios: ./benchmark.sh --scenario warm --concurrency 32 --runs 5
Collect profiles: ./collect_profiles.sh --nsys --perf --dcgm and upload artifacts to the repo-run directory for analysis.

We provide example analysis notebooks that parse nsys traces and perf flamegraphs to produce an executive summary (latency percentiles, resource usage, and cost estimates). For low-latency capture and tracing guidance see community notes on hybrid low-latency capture workflows (Hybrid Studio Ops).

Actionable takeaways

Do not trust synthetic microbenchmarks alone: always run end-to-end query mixes that include planner, IO, and materialization stages.
Use NVLink Fusion zero-copy where possible: it often shifts bottlenecks to algorithmic efficiency, which is easier to remediate than raw transfer bandwidth.
Make benchmarks reproducible: publish manifests, seeds, and verification checksums; automate runs and collect correlated traces.
Profile both host and GPU: correlated traces reveal cross-boundary stalls; use Nsight + perf/eBPF together.
Include cost models: translate throughput and latency into $/Qph for procurement decisions.

The marriage of NVLink Fusion and RISC-V CPUs changes the performance surface of GPU-accelerated OLAP engines. Benchmarks that measure only kernel throughput or PCIe copy times miss the new bottlenecks emerging in 2026: host planning latencies, coherent memory behavior, and new spill dynamics under NVLink. Use the reproducible benchmark design in this article to evaluate claims, tune deployments, and make procurement decisions with data instead of slides.

Ready to run the benchmark? Clone the reference repo, provision a test node with NVLink Fusion support, and run the warm and cold scenarios. If you want a guided run or a custom analysis for your fleet, contact our team or open an issue in the repo — we’ll help you interpret traces and produce a cost/perf recommendation for production readiness.

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