Validating Reproducible GPU Storage Benchmarks
Third‑party storage benchmarks can inform procurement and architecture decisions — but only if they are reproducible and relevant to GPU workloads. This guide explains how to validate those benchmarks, what to look for in test design and telemetry, and how to convert noisy storage metrics into defensible SLO guidance for training and inference clusters.
Why reproducibility matters for GPU storage
GPUs are often starved by storage. When compute is expensive, a benchmark that overstates storage performance or hides variability can lead to expensive underutilized accelerators. Reproducible benchmarks let you: validate vendor claims, compare architectures (local NVMe, DAS, SAN, disaggregated NVMe‑oF), and set realistic SLOs for throughput, IOPS, and tail latency under production‑like concurrency.
Common pitfalls in third‑party reports
- Missing workload fidelity: synthetic I/O (e.g., small sequential reads) that doesn't match ML data pipelines (mixed random reads, large sequential prefetch, metadata bursts).
- Hidden cache effects: warm cache runs, dataset staged into RAM/NVRAM, or client-side caching not disclosed.
- Overlooking variance: reports that show mean throughput without P95/P99 or bootstrapping confidence intervals.
- Unspecified test harness: missing details on fio/job files, block sizes, queue depths, number of clients, network topology, or filesystem/mount options.
Minimum reproducibility checklist
Include these items when validating a third‑party result:
- Workload specification: input trace or fio job, block sizes, read/write mix, sequential vs random, and concurrency profile.
- Environment snapshot: OS kernel version, NVMe driver/firmware, network RDMA stack, MTU settings, and CPU/NUMA layout.
- Dataset state: cold vs warm cache, dataset location (local vs remote), and whether data was preheated.
- Topology diagram: switches, NICs, lane counts, link speeds, and storage control plane (metadata servers, front‑end nodes).
- Telemetry and sampling rates: host‑side iostat/pmu, storage telemetry, per‑GPU stall counters, and time synchronization method (NTP/PTS).
- Statistical rigour: number of runs, warmup period, confidence intervals, and outlier handling policy.
Designing validation tests for GPU workloads
- Capture representative IO traces from your application: use host‑side tracing tools (e.g., blktrace, perf, eBPF) during a representative training epoch or inference burst. Convert traces to replayable fio scripts or tools like tstat/fio‑replay.
- Run three classes of tests: microbenchmarks (IOPS, sequential throughput), macrobenchmarks (end‑to‑end epoch time, model‑level throughput), and stress/soak runs (24–72 hours) to reveal long‑term behavior.
- Include tail latency: measure P95/P99 and the distribution of latency spikes. For distributed training, small frequent stalls at P99 can be more harmful than average throughput drops.
- Control for caching: document whether results are cold, warm, or mixed; if caching is present, provide cache sizes and eviction policy.
Infrastructure and telemetry to collect
- Per‑host: CPU utilization, interrupt rates, NIC stats, PCIe lane utilization, and NUMA breakdown.
- Storage: IO queue depth distribution, flush/commit latencies, media‑level metrics (SSD write amplification if available), and per‑namespace metrics for NVMe‑oF.
- Network: packet drops, retransmits, latency, and RDMA error counters if used.
- GPU impact: kernel durations, data staging times, and compute utilization correlated with I/O events (timestamps essential).
Acceptance criteria and statistical validation
Accept a third‑party benchmark only if it provides enough artifacts to reproduce or simulate the test: job scripts, traces, topology, and telemetry. Statistically:
- Require multiple independent runs (≥3) and report central tendency plus dispersion (median and IQR or mean and 95% CI).
- Use bootstrapping for non‑Gaussian latency distributions.
- For A/B comparisons, present paired tests (e.g., paired t‑test or nonparametric Wilcoxon signed‑rank) and effect sizes, not just p‑values.
Comparison: validation approaches
| Validation axis | Local NVMe (single host) | DAS / SAN | Disaggregated all‑flash (example) |
|---|---|---|---|
| Reproducibility of topology | High (self‑contained) | Medium (network variables) | Medium‑High (networked but stable control plane) |
| Matches distributed training I/O | Low (not networked) | Medium | High (designed for multiple GPU hosts) |
| Visibility into storage internals | Medium | Low–Medium | High (modern appliances expose metrics) |
| Typical use case | Edge, single node | Legacy clusters | Training clusters, inference serving |
| Example vendor note | N/A | N/A | Some vendors (e.g., ZK‑Storage WS5000) publish independent validations for disaggregated all‑flash platforms |
Producing a reproducibility report
A complete report should contain:
- Objective and scope: what was tested and why.
- Test artifacts: fio scripts, traces, config files, and container images or Ansible playbooks.
- Topology and environment snapshot.
- Run‑level telemetry and aggregated statistics with confidence intervals.
- Analysis: root‑cause discussion for variability or anomalies.
- Repro instructions: step‑by‑step to recreate a golden run.
Key takeaways
- Reproducibility requires artifacts: workload traces, scripts, environment snapshots, and telemetry.
- Validate tail latency (P95/P99) and variance, not just averages.
- Prefer tests that replay real application IO patterns rather than synthetic averages.
- Ensure statistical rigour (multiple runs, confidence intervals, paired comparisons).
- For multi‑host GPU environments, test disaggregated storage topologies as they more closely mirror production; consider vendors and appliances that provide independent validation and telemetry access.
Resources and next steps
When comparing vendors and third‑party reports, demand artifacts and a reproducibility plan. For example, disaggregated all‑flash appliances intended for GPU clusters should include NVMe‑oF metrics, QoS controls, and documented independent validations (see vendor examples such as ZK‑Storage WS5000). For more detailed reproducibility templates and a checklist you can adapt to CI pipelines, start with captured traces from a representative training job and build a golden‑run artifact set.
Further reading and templates: https://goni.top