yogsoth-ai Compute Normalization
Purpose
Analyze the performance-compute tradeoff across methods. Identify Pareto-optimal methods (best performance for a given compute budget), compute-normalized rankings, and efficiency frontiers. Essential for practical method selection under resource constraints.
Input Schema
| Field | Type | Description |
|---|---|---|
| method_scores | object[] | Array of {method, dataset, metric, score} |
| compute_costs | object[] | Array of {method, flops, gpu_hours, params, training_cost_usd} |
Output Schema
{
"pareto_frontier": [
{
"method": "string",
"score": 0.0,
"compute_metric": "string",
"compute_value": 0.0,
"is_pareto_optimal": true
}
],
"efficiency_rankings": [
{
"method": "string",
"score_per_flop": 0.0,
"score_per_gpu_hour": 0.0,
"score_per_param": 0.0
}
],
"compute_normalized_scores": [
{
"method": "string",
"raw_score": 0.0,
"normalized_score": 0.0,
"normalization_method": "string"
}
],
"practical_recommendations": {
"budget_low": {"method": "string", "score": 0.0, "cost": "string"},
"budget_medium": {"method": "string", "score": 0.0, "cost": "string"},
"budget_high": {"method": "string", "score": 0.0, "cost": "string"}
}
}