CliBench Mk III SMP: The Ultimate Benchmarking Toolkit for SMP Systems

• Parse config file
• Reserve CPUs using taskset or numactl
• Run CliBench Mk III SMP with specified flags
• Capture stdout/stderr and return code
• Store artifacts (logs, raw outputs, metadata)

Minimal shell example:

#!/bin/bash config="$1" for bench in $(yq e '.benchmarks[].name' "$config"); do   cmd=$(yq e ".benchmarks[] | select(.name=="$bench") | .command" "$config")   affinity=$(yq e ".benchmarks[] | select(.name=="$bench") | .cpu_affinity" "$config")   taskset -c "$affinity" bash -c "$cmd" > "results/$bench.out" 2>&1 done 

Cluster orchestration with job schedulers

For larger scale, integrate with Slurm, HTCondor, or Kubernetes:

• Slurm: submit sbatch jobs that reserve cores and NUMA nodes; collect output in a results bucket.
• Kubernetes: use DaemonSets or Jobs with hostPID and cpuManagerPolicy set for exclusive CPU allocation; use node selectors for topology-aware placement.

Slurm job script example:

#!/bin/bash #SBATCH --cpus-per-task=32 #SBATCH --ntasks=1 #SBATCH --hint=nomultithread srun taskset -c 0-31 ./clibench --mode compute --threads 32 --duration 60 

CI/CD integration

Add benchmarks to CI carefully:

• Run short, representative benchmarks on every pull request for fast feedback.
• Schedule longer, more expensive full-suite runs nightly or on merges to main.
• Use containers in CI to standardize environments and cache compiled artifacts for speed.

Store artifacts as build outputs and attach metadata (commit hash, branch, config).

---

Data collection and storage

Structured outputs

CliBench Mk III SMP should be run with machine-readable output flags (JSON/CSV). If it lacks them, wrap/parsers should extract metrics and timestamps.

Essential fields to capture:

• benchmark name and parameters
• start/stop timestamps
• raw measurements per iteration
• system metadata (topology, kernel, tuning)
• exit codes and logs

Centralized storage

Use object storage (S3-compatible), time-series DBs (InfluxDB, Prometheus + long-term storage), or relational DBs for indexed queries. Keep raw outputs plus a parsed, normalized record for fast queries.

Suggested layout in object storage:

• results/
  • {date}/{node}/{commit}/{bench_name}/raw.log
  • {date}/{node}/{commit}/{bench_name}/results.json

---

Analysis and visualization

Statistical processing

Automate calculation of:

• mean, median, stddev
• percentiles (50th, 90th, 95th)
• confidence intervals (use bootstrapping for non-normal data)

Example Python snippet for percentile and bootstrap CI:

import numpy as np from sklearn.utils import resample data = np.array(measurements) median = np.median(data) p95 = np.percentile(data, 95) # bootstrap 95% CI for median medians = [np.median(resample(data)) for _ in range(1000)] ci_low, ci_high = np.percentile(medians, [2.5, 97.5]) 

Visualizations

Automate generation of charts:

• time series for long-term trends
• boxplots per configuration
• heatmaps for topology vs. throughput Use Grafana for dashboards (ingest metrics into Prometheus/Influx) or generate static plots with matplotlib/Plotly for reports.

---

Regression detection and alerting

Integrate benchmark results into a regression detection pipeline:

• Define baselines per benchmark (median over last N stable runs).
• Compute relative change and flag if over threshold (e.g., >5% regression).
• Use anomaly detection (z-score, EWMA) to detect sudden deviations.

Alerting:

• Short failures on PR: post comment with summarized metrics and link to logs.
• Major regressions: send alerts to Slack/email and open a ticket with artifacts attached.

---

Reproducibility, provenance, and traceability

Record provenance for every run:

• Commit hash of code under test
• CliBench Mk III SMP version and compile flags
• OS image or container digest
• Exact command line and config file
• Hardware serials or node identifiers

Store a provenance manifest alongside results.json. This enables later reproduction or forensic analysis.

---

Scaling: fleet-wide orchestration and heterogeneity

When benchmarking many nodes:

• Use inventory management (Ansible, Salt, or CMDB) to track node capabilities and labels.
• Group nodes by similar topologies (same NUMA layout, CPU model) to make comparisons fair.
• Run tests in rolling waves to avoid overloading shared infrastructure (power, cooling).

Handle heterogeneity by normalizing results (per-core, per-socket) and comparing like-for-like configurations.

---

Common pitfalls and mitigations

• Noise from system daemons: isolate nodes or use minimal images.
• Thermal throttling: monitor temperatures and throttle-aware scheduling.
• Non-deterministic workloads: prefer deterministic inputs or seed RNGs.
• Incomplete metadata: always capture topology and kernel command line.

---

Example end-to-end automated pipeline

1. Commit triggers CI. Short smoke benchmarks run in container on triggered runners.
2. Merge to main schedules nightly full-suite run across a labeled cluster via Slurm.
3. Each Slurm job:
   • pulls container image
   • gathers hardware/topology metadata
   • runs warmup + 10 iterations
   • uploads raw logs and results.json to S3
4. An ingest service parses results into InfluxDB and computes aggregates.
5. Grafana dashboards show trends; regression detector compares against baselines.
6. Alerts created automatically for regressions; failures create issue with attached artifacts.

---

Conclusion

Automating benchmarks with CliBench Mk III SMP unlocks reproducible, scalable, and actionable performance testing for SMP systems. The key elements are environment standardization, topology-aware scheduling, structured data capture, statistical analysis, and integration with CI/CD and alerting. With an automated pipeline, teams can detect regressions early, validate optimizations, and maintain performance over time.