Energaizer Gpu Power Eval

---
name: energaizer-gpu-power-eval
description: Evaluates the accuracy of a lightweight analytical framework in predicting GPU latency and dynamic power consumption for AI workloads across different hardware architectures, operating frequencies, and algorithm configurations. Use when the user wants to benchmark on EnergAIzer Kernel Database & AI Workloads, or asks about evaluating this task. Reports MAPE.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2604.20105
  bibtex_key: lee2026energaizer
  confidence: high
---

# energaizer-gpu-power-eval

> EnergAIzer: Fast and Accurate GPU Power Estimation Framework for AI Workloads — Lee et al. (2026) (arXiv:2604.20105, 2026)

## What this evaluates

Evaluates the accuracy of a lightweight analytical framework in predicting GPU latency and dynamic power consumption for AI workloads across different hardware architectures, operating frequencies, and algorithm configurations.

## Datasets

- **EnergAIzer Kernel Database & AI Workloads** — total ?; splits: offline_database (-1), held_out_test (-1); repo https://github.com/kyungmi-lee/energaizer-ispass26-artifact

## Metrics

- `MAPE` **(primary)** — range: percent
  - Mean Absolute Percentage Error: (1/N) * Σ |(Actual - Predicted) / Actual| * 100%. Applied independently to latency and power predictions across workloads or kernels.

## Input / output format

**Input**: Kernel specifications (type, library, tensor shape, precision, operating frequency) or end-to-end workload configurations (model architecture, batch size, sequence length, precision, backend settings).

**Output**: Estimated latency (seconds) and estimated power consumption (watts).

## Scoring recipe

```python
def compute_mape(actual, predicted):
    mask = actual != 0
    return np.mean(np.abs((actual[mask] - predicted[mask]) / actual[mask])) * 100

latency_mape = compute_mape(measured_latency, estimated_latency)
power_mape = compute_mape(measured_power, estimated_power)
avg_latency_mape = np.mean(latency_mapes)
avg_power_mape = np.mean(power_mapes)
```

## Common pitfalls

- CPU kernel launch overhead significantly impacts latency estimation for small kernels, inflating end-to-end errors.
- The model assumes sequential kernel execution and consistent energy efficiency across architectures, failing for overlapping kernels or different memory technologies (e.g., GDDR6 vs HBM).
- Errors exceeding 40% are clipped in reported figures, potentially masking worst-case performance on specific configurations.

## Evidence (verbatim from paper)

> EnergAIzer achieves 11.0% latency and 8.0% power error on the server-grade A100-40GB-PCIE, and 8.8% latency and 8.2% power error on the workstation-grade A10, averaged across all workloads. Our framework captures distinct scaling behaviors of low-utilization (small batch/sequence, left panel) and power-capped (large batch/sequence, right panel) workloads, achieving 6-9% MAPE across frequencies.

## Citation

```bibtex
@misc{lee2026energaizer,
  title={EnergAIzer: Fast and Accurate GPU Power Estimation Framework for AI Workloads},
  author={Lee et al. (2026)},
  year={2026},
  note={arXiv:2604.20105}
}
```

- arXiv: 2604.20105