# Bots Dvfs Eval

> Evaluates a hierarchical multi-agent reinforcement learning scheduler's ability to optimize task allocation, frequency scaling, and core selection for OpenMP DAG workloads on embedded systems. It probes the trade-off between makespan, energy consumption, and thermal constraints under real-time profiling feedback. Use when the user wants to benchmark on Barcelona OpenMP Tasks Suite (BOTS), or asks about evaluating this task. Reports makespan.

- Skill: `qhjqhj00/bots-dvfs-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/bots-dvfs-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/bots-dvfs-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/bots-dvfs-eval

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# bots-dvfs-eval

> HiDVFS: A Hierarchical Multi-Agent DVFS Scheduler for OpenMP DAG Workloads — Pivezhandi et al. (2026) (arXiv:2601.06425, 2026)

## What this evaluates

Evaluates a hierarchical multi-agent reinforcement learning scheduler's ability to optimize task allocation, frequency scaling, and core selection for OpenMP DAG workloads on embedded systems. It probes the trade-off between makespan, energy consumption, and thermal constraints under real-time profiling feedback.

## Datasets

- **Barcelona OpenMP Tasks Suite (BOTS)** — total 12; splits: test (12)

## Metrics

- `makespan` **(primary)** — range: other
  - Wall-clock time to complete all tasks in the OpenMP DAG workload. Minimized as the primary objective.
- `energy` — range: other
  - Total power consumption integrated over the execution time of the workload. Secondary objective.

## Input / output format

**Input**: Profiling data per execution epoch: makespan, energy, temperature, cache misses, branch misses, current core states, and task priority/affinity constraints.

**Output**: Scheduling decisions: core selection mask, frequency level (0–11), and task priority assignment.

## Scoring recipe

```python
def evaluate_rl_scheduler(execution_log, window=10):
    makespans = [e['makespan'] for e in execution_log]
    energies = [e['energy'] for e in execution_log]
    l10_makespan = sum(makespans[-window:]) / window
    l10_energy = sum(energies[-window:]) / window
    return {'makespan': l10_makespan, 'energy': l10_energy}
```

## Common pitfalls

- Confusing 'sequential mode' (single application running in parallel across multiple cores) with single-threaded execution.
- Assuming energy and makespan are independent optimization targets; the paper notes that minimizing makespan indirectly reduces energy due to shorter computation times.
- Ignoring the 2 ms round-trip scheduling overhead, which is negligible compared to benchmark execution times but part of the deployment cost.

## Evidence (verbatim from paper)

> This subsection outlines the evaluation methodology for assessing single-agent and multi-agent Reinforcement Learning (RL) approaches, focusing on key performance metrics—makespan, energy consumption, average temperature, branch misses, and cache misses—while integrating statistical analyses to quantify the impact of critical variables: task priority, number of cores, and average frequency.

## Citation

```bibtex
@misc{pivezhandi2026hidvfs,
  title={HiDVFS: A Hierarchical Multi-Agent DVFS Scheduler for OpenMP DAG Workloads},
  author={Pivezhandi et al. (2026)},
  year={2026},
  note={arXiv:2601.06425}
}
```

- arXiv: 2601.06425