# Comet Thermal Sim Eval

> Evaluates the accuracy and overhead of an integrated thermal simulation toolchain (CoMeT) for modeling processor-memory thermal dynamics across 2D, 2.5D, and 3D architectures. It probes the tool's ability to capture thermal coupling, leakage power effects, and DVFS/DTM interactions under diverse compute and memory-intensive workloads. Use when the user wants to benchmark on PARSEC 2.1, SPLASH-2, SPEC CPU2017, or asks about evaluating this task. Reports Temperature.

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

---


# comet-thermal-sim-eval

> CoMeT: An Integrated Interval Thermal Simulation Toolchain for 2D, 2.5D, and 3D Processor-Memory Systems — Lokesh Siddhu et al. (arXiv:2109.12405, 2021)

## What this evaluates

Evaluates the accuracy and overhead of an integrated thermal simulation toolchain (CoMeT) for modeling processor-memory thermal dynamics across 2D, 2.5D, and 3D architectures. It probes the tool's ability to capture thermal coupling, leakage power effects, and DVFS/DTM interactions under diverse compute and memory-intensive workloads.

## Datasets

- **PARSEC 2.1** — total ?; splits: test (-1)
- **SPLASH-2** — total ?; splits: test (-1)
- **SPEC CPU2017** — total ?; splits: test (-1)

## Metrics

- `Temperature` **(primary)** — range: other
  - Peak and average temperature recorded per core and memory layer over 1 ms simulation epochs. Computed from thermal simulation output.
- `Simulation Time Overhead` — range: percent
  - Percentage increase in total simulation runtime compared to the HotSniper baseline: ((CoMeT_time - HotSniper_time) / HotSniper_time) * 100.
- `Execution Time` — range: other
  - Wall-clock time to complete benchmark instructions or normalized speedup relative to baseline configurations.

## Input / output format

**Input**: Pre-compiled benchmark source code (PARSEC, SPLASH-2) or Pinballs traces (SPEC CPU2017, 100M instructions) mapped onto configurable core-memory floorplans (2D-ext, 3D-ext, 2.5D, 3D-stacked).

**Output**: Time-series temperature traces per core/memory layer, power dissipation values, execution times, and normalized simulation runtime.

## Scoring recipe

```python
1. Run benchmark on CoMeT with specified architecture config.
2. Record temperature, power, and execution time at each 1ms epoch.
3. Compute average/peak temperature across all layers.
4. Compare CoMeT runtime to HotSniper runtime: overhead = ((CoMeT_time - HotSniper_time) / HotSniper_time) * 100.
5. Report normalized speedup relative to 4-core baseline.
```

## Common pitfalls

- Assuming core temperature is always the system hotspot; memory layers can be significantly hotter due to stacking and leakage power.
- Confusing the four core-memory topologies (2D-ext, 3D-ext, 2.5D, 3D-stacked) which have distinct thermal coupling characteristics.
- Ignoring thermal hysteresis and delay in DVFS/DTM response when memory hotspots are far from core layers.

## Evidence (verbatim from paper)

> We use a diverse set of workloads namely, PARSEC 2.1 [4], SPLASH-2 [55] and SPEC CPU2017 [9] to study the performance, power, and thermal profiles for core and memory. ... Thermal simulation is invoked periodically with an epoch time of 1 ms. ... We observe that the cores have relatively lower temperatures than memory banks due to the memory-intensive nature of the lbm benchmark.

## Citation

```bibtex
@misc{siddhu2021comet,
  title={CoMeT: An Integrated Interval Thermal Simulation Toolchain for 2D, 2.5D, and 3D Processor-Memory Systems},
  author={Lokesh Siddhu et al.},
  year={2021},
  note={arXiv:2109.12405}
}
```

- arXiv: 2109.12405