You are part of a team investigating the performance variability of an AI model across different hardware configurations. The model is deployed on various servers with differing GPU types, memory sizes, and CPU clock speeds. Your task is to identify which hardware factors most significantly impact the model's inference time. Which analysis approach would be most effective in identifying the hardware factors that significantly impact the model's inference time?
Select an answer to reveal the explanation.
Short Explanation
If your model's response time jumps around across servers, you need to see how GPU type, memory, and CPU speed affect it together. Multiple regression treats inference time as the dependent variable and the hardware specs as predictors, giving coefficients that show which factor is the real bottleneck. That's how you get actionable data.
Full Explanation
When analyzing how multiple independent hardware parameters (such as GPU architecture, VRAM size, and host CPU frequency) simultaneously influence a single performance metric like model inference latency (the dependent variable), multiple linear regression is the most appropriate statistical method. Multiple regression allows researchers to model the relationship between several independent variables and a single continuous dependent variable. By fitting a regression model, you can determine the overall statistical significance of the predictors, evaluate the strength of the relationship (using the R-squared value), and look at individual coefficients to isolate the effect of each hardware parameter while holding the others constant.
Applying clustering algorithms to group hardware configurations by inference latency is an unsupervised learning technique used to group data points based on feature similarity. While it might group similar configurations together, it does not output statistical significance or isolate which specific hardware factors drive the performance variance.
Conducting a series of independent t-tests comparing inference times between pairs of different GPU types is limited to comparing the means of exactly two groups (e.g., comparing the speed of one GPU type versus another). It cannot handle multiple independent variables simultaneously, nor can it incorporate continuous variables like memory size and CPU clock speed into a single analysis.
Creating a bar chart comparing average inference times across various hardware configurations is a descriptive visualization tool. While helpful for initial data exploration, it cannot provide statistical proof, quantify individual variable coefficients, or account for confounding variables in complex hardware setups.