Magus Uncore Freq Eval

---
name: magus-uncore-freq-eval
description: Evaluates a model-free runtime system for dynamically scaling uncore frequencies in heterogeneous CPU-GPU architectures. It probes the system's ability to balance energy efficiency and performance across diverse HPC, molecular dynamics, and deep learning workloads. Use when the user wants to benchmark on Altis, ECP proxy applications, AI-enabled applications, MLPerf benchmarks, Altis-SYCL, or asks about evaluating this task. Reports Energy Delay Product (EDP).
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2502.03796
  bibtex_key: [REDACTED]
  confidence: high
---

# magus-uncore-freq-eval

> Exploring Uncore Frequency Scaling for Heterogeneous Computing — Zhong Zheng et al. (2025) (arXiv:2502.03796, 2025)

## What this evaluates

Evaluates a model-free runtime system for dynamically scaling uncore frequencies in heterogeneous CPU-GPU architectures. It probes the system's ability to balance energy efficiency and performance across diverse HPC, molecular dynamics, and deep learning workloads.

## Datasets

- **Altis** — total ?; splits: test (-1)
- **ECP proxy applications** — total ?; splits: test (-1)
- **AI-enabled applications** — total ?; splits: test (-1)
- **MLPerf benchmarks** — total ?; splits: test (-1)
- **Altis-SYCL** — total ?; splits: test (-1)

## Metrics

- `Performance Loss` — range: percent
  - Percentage increase in execution time compared to the baseline: (T_method - T_baseline) / T_baseline × 100%.
- `Package Power Saving` — range: percent
  - Average reduction in CPU package power consumption relative to the baseline: (P_baseline - P_method) / P_baseline × 100%.
- `Energy Saving` — range: percent
  - Total reduction in system energy consumption (CPU package + GPU core and memory) compared to the baseline: (E_baseline - E_method) / E_baseline × 100%.
- `Energy Delay Product (EDP)` **(primary)** — range: other
  - Composite metric capturing energy efficiency and performance impact, calculated as Energy × Execution Time. Lower values indicate better overall system efficiency.

## Input / output format

**Input**: Heterogeneous CPU-GPU system configurations running specific HPC, molecular dynamics, and deep learning benchmark applications under varying uncore frequency scaling strategies.

**Output**: Execution time, CPU package power, GPU energy, and total system energy per benchmark run, which are aggregated to compute the four evaluation metrics.

## Scoring recipe

```python
def compute_metrics(baseline, method):
    perf_loss = (method['time'] - baseline['time']) / baseline['time'] * 100
    pkg_power_saving = (baseline['pkg_power'] - method['pkg_power']) / baseline['pkg_power'] * 100
    energy_saving = (baseline['energy'] - method['energy']) / baseline['energy'] * 100
    edp = method['energy'] * method['time']
    return perf_loss, pkg_power_saving, energy_saving, edp
```

## Common pitfalls

- The baseline uses default hardware frequency scaling, which keeps uncore at maximum frequency because CPU power rarely reaches TDP, making it a potentially suboptimal reference point.
- The UPS comparator was custom-implemented from the paper's methodology due to lack of open-source code, introducing potential implementation variance.
- Different hardware platforms require different programming models (CUDA vs SYCL), which may confound direct performance comparisons across systems.

## Evidence (verbatim from paper)

> We evaluate each method using four key metrics: Performance Loss: Percentage increase in execution time compared to the baseline, measuring the runtime impact of uncore frequency scaling. Package Power Saving: Average reduction in CPU package power consumption relative to the baseline. Energy Saving: Total reduction in system energy consumption, including both CPU package and GPU (core and memory) energy, compared to the baseline. Energy Delay Product (EDP): A composite metric that captures both energy efficiency and performance impact.

## Citation

```bibtex
@misc{zheng2025exploring,
  title={Exploring Uncore Frequency Scaling for Heterogeneous Computing},
  author={Zhong Zheng et al. (2025)},
  year={2025},
  note={arXiv:2502.03796}
}
```

- arXiv: 2502.03796