When presenting benchmark results, focus on clarity, comparison, and context to make performance metrics actionable. Use visualizations that directly highlight differences between systems, configurations, or versions, and ensure the data ties back to business or technical goals. Avoid cluttered charts and prioritize simplicity to help decision-makers quickly identify trends, bottlenecks, or trade-offs.
Start with comparative bar charts for throughput and latency across tested scenarios. For example, a grouped bar chart can show throughput (requests/sec) for System A vs. System B under varying load levels (e.g., 100, 1k, 10k concurrent users). Pair this with line charts to display latency percentiles (p50, p90, p99) over time or load, which reveals how performance degrades as demand increases. For recall in search or ML systems, use line plots with precision-recall curves or bar charts comparing recall rates across different algorithms or parameter sets. Always normalize results against a baseline (e.g., "System X achieves 120% of the baseline throughput") to simplify interpretation.
Next, highlight trade-offs between metrics. For example, a scatter plot with throughput on the X-axis and latency on the Y-axis can show which configurations strike the best balance. Annotate inflection points where latency spikes or throughput plateaus. For recall, overlay it with latency or resource usage to illustrate the cost of higher accuracy. If the goal is cost efficiency, include a table with normalized metrics (e.g., "throughput per dollar" or "recall per GPU-hour") to compare solutions holistically. Avoid overwhelming the audience—limit visuals to 2–3 key charts that directly align with the decision criteria (e.g., "Which system meets our 200ms latency SLA at peak load?").
Finally, contextualize results with annotations, thresholds, and real-world implications. Add horizontal lines to charts to mark performance targets (e.g., "latency must be <300ms") and color-code bars (green/yellow/red) to indicate pass/fail status. Include a brief summary table that ranks systems by priority metrics, such as "Best Throughput: System A (1.5k/sec), Best Recall: System B (98%), Most Cost-Effective: System C." Pair this with a narrative explaining trade-offs (e.g., "System B’s higher recall requires 2x more hardware"). Decision-makers need to see not just the data, but how it impacts operational costs, user experience, or scalability—so explicitly link metrics to outcomes like "choosing System A reduces server costs by 30% but risks missing recall targets during peak traffic."