Delivery Approach Guide
Knowledge Guides /How do you define performance metrics for simulations and digital twins?
Last updated: · New Horizon Digital Outsourcing Team
Tie performance metrics to usage scenarios, defining display smoothness, data latency, computation speed, and resource consumption separately. A high frame rate does not mean state data is current, and fast simulation does not mean numerical results are reliable. Measure these metrics separately.
Define the target environment and load
- Agree on hardware, resolution, object count, data frequency, and concurrent users. Do not promise performance across all field environments based on an empty scene or the developer's high-end equipment. Prepare repeatable inputs for both typical and high loads.
Break down end-to-end latency
- Distinguish acquisition, transmission, processing, and display times, and state whether measurement clocks are synchronized. Averages can hide noticeable stutters, so also inspect percentiles and maximum outliers. For offline simulation, separately record computation time relative to simulated time.
Keep quality conditions consistent before and after optimization
- Reducing mesh detail, sampling rates, or model complexity may improve speed, but its impact on acceptance metrics must be checked. Record each optimization and comparison results to avoid apparent gains achieved by removing essential calculations. If resource leaks occur, perform sustained-operation checks.
How should performance be accepted?
- Run a fixed load in the agreed environment and retain logs and sampling methods. Report warm-up, cache, and background-task conditions, and identify areas not covered. Passing one short test does not demonstrate long-term stability; arrange observation according to actual usage duration.