When presenting benchmark results, what are effective ways to visualize and report the performance (throughput, latency, recall) to make it actionable for decision makers?

To effectively visualize and report benchmark results for decision-makers, focus on clarity, context, and actionable comparisons. Start by using straightforward charts that highlight key metrics like throughput (requests processed per second), latency (time per operation), and recall (accuracy in retrieval tasks). For throughput, bar charts work well to compare systems or configurations side-by-side. Latency is best shown with line graphs or percentile plots (e.g., p50, p95) to expose tail behavior, which is critical for real-time systems. Recall can be visualized with bar charts for absolute values or heatmaps if comparing multiple parameters (e.g., varying dataset sizes). Avoid cluttering graphs with too many data points; instead, use annotations to highlight thresholds (e.g., “System X meets target latency of 100ms at 1k requests/sec”).

Next, contextualize the numbers by tying them to real-world scenarios. For example, if a system achieves 500 requests/sec throughput, explain what this means for expected user traffic (e.g., “Handles 10k users/hour”). For latency, specify whether the measured values align with user experience goals (e.g., “95% of requests under 200ms meets SLA requirements”). When reporting recall, clarify the trade-offs: “Model A achieves 92% recall but requires 50ms more latency than Model B.” Include baseline comparisons, such as previous system versions or industry standards, to show progress or gaps. For example, “Throughput improved by 40% over the last release, but still lags behind Competitor Y’s open-source benchmark.”

Finally, structure the report to prioritize actionable insights. Use dashboards that combine metrics (e.g., a table summarizing throughput, latency, and recall across configurations) and highlight the “best” option for specific goals. For instance, “Configuration C offers the best recall (98%) for batch processing, while Configuration D optimizes latency (75ms) for real-time use.” Include error margins or confidence intervals to indicate result reliability. If a trade-off exists—like higher throughput at the cost of recall—propose next steps: “Test Configuration E with a hybrid approach to balance throughput and recall.” End with clear recommendations, such as “Adopt Configuration C if accuracy is critical; optimize indexing for latency if speed is prioritized.” This approach helps decision-makers quickly identify trade-offs and align results with business needs.