# performance-profiling

Finds bottlenecks in materials simulations: parses timing logs, calculates MPI scaling efficiency, and estimates memory requirements.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · ★ 83
- Author: HeshamFS
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/HeshamFS/materials-simulation-skills/tree/HEAD/skills/simulation-workflow/performance-profiling/SKILL.md
- Updated: 2026-10-21
- HTML: https://affpapa.org/en/nevibecode/skills/heshamfs-performance-profiling

## What it does

The skill parses simulation logs and breaks runtime into phases—matrix assembly, solver, I/O. Flags fire when thresholds are crossed: solver and assembly phases above 50%, I/O above 30%. For scaling, it calculates efficiency using Amdahl's and Gustafson's laws; memory estimation is built from mesh size, field count, and sparse matrix storage. Runs on Python 3.10+ standard library with no external dependencies.

Poor scaling means efficiency below 0.70. If the solver takes over 70% of runtime, the skill suggests tuning the preconditioner; for I/O it recommends reducing output frequency or switching to parallel I/O.

Four scripts: `timing_analyzer.py` for logs, `scaling_analyzer.py` for strong/weak scaling, `memory_profiler.py` for RAM estimation, `bottleneck_detector.py` for summary and recommendations. Output is JSON with `inputs` and `results` keys.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add HeshamFS/materials-simulation-skills --skill performance-profiling -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/performance-profiling/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/performance-profiling/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add HeshamFS/materials-simulation-skills --skill performance-profiling -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/performance-profiling/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add HeshamFS/materials-simulation-skills --skill performance-profiling -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/performance-profiling/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install HeshamFS/materials-simulation-skills performance-profiling
```

2. **Manually: put the files in place**
- File: `.github/skills/performance-profiling/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add HeshamFS/materials-simulation-skills --skill performance-profiling -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/HeshamFS/materials-simulation-skills --path skills/simulation-workflow/performance-profiling
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/performance-profiling/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add HeshamFS/materials-simulation-skills --skill performance-profiling -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/HeshamFS/materials-simulation-skills/tree/HEAD/skills/simulation-workflow/performance-profiling/SKILL.md

## When to use it
- parse which simulation phase consumes time—solver, assembly, or I/O
- calculate MPI scaling efficiency and find where it drops below threshold
- estimate memory demand from mesh and field parameters
- get a list of concrete optimizations for identified bottlenecks

## Example prompts
- My simulation is too slow. What's the bottleneck? Here's the log: `simulation.log`
- Calculate scaling efficiency from `scaling_data.json`, strong scaling type
- How much memory does the problem need? Parameters in `params.json`, machine has 16 GB

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: ef313febfa0d9002.

BEGIN THIRD-PARTY FILE ef313febfa0d9002
````text
---
name: performance-profiling
description: >
  Identify computational bottlenecks, analyze parallel scaling, estimate memory
  requirements, and generate optimization recommendations for materials
  simulations — parse timing logs to find dominant phases (solver, assembly,
  I/O), evaluate strong and weak scaling efficiency, profile memory from mesh
  and field parameters, and detect bottlenecks with actionable fix suggestions.
  Use when a simulation is running slower than expected, investigating MPI
  scaling efficiency, planning HPC resource allocation, deciding whether to
  tune the preconditioner or reduce I/O frequency, or estimating if a problem
  fits in available RAM, even if the user only says "my simulation is too
  slow" or "how many nodes do I need."
allowed-tools: Read, Write, Grep, Glob
metadata:
  last_deterministic_eval: "2026-10-20"
  agent_evaluation_status: "not_revalidated"
  author: HeshamFS
  version: "1.2.2"
  security_tier: medium
  security_reviewed: true
  tested_with:
    - claude-code
  last_evaluated: "2026-06-24"
  eval_cases: 5
  last_reviewed: "2026-06-23"
  standards:
    - "Amdahl (1967), serial-fraction speedup law S(N)=1/(f+(1-f)/N)"
    - "Gustafson (1988), scaled speedup law S(N)=N-f(N-1)"
    - "Gropp, Lusk & Skjellum (1999), Using MPI (parallel scaling and communication)"
    - "Saad (2003), Iterative Methods for Sparse Linear Systems (solver/preconditioner choice)"
    - "Karypis & Kumar (1998), METIS multilevel graph partitioning (load balancing)"
---

# Performance Profiling

## Goal

Provide tools to analyze simulation performance, identify bottlenecks, and recommend optimization strategies for computational materials science simulations.

## Requirements

- Python 3.10+
- No external dependencies (uses Python standard library only)
- Works on Linux, macOS, and Windows

## Inputs to Gather

Before running profiling scripts, collect from the user:

| Input | Description | Example |
|-------|-------------|---------|
| Simulation log | Log file with timing information | `simulation.log` |
| Scaling data | JSON with multi-run performance data | `scaling_data.json` |
| Simulation parameters | JSON with mesh, fields, solver config | `params.json` |
| Available memory | System memory in GB (optional) | `16.0` |

## Decision Guidance

### When to Use Each Script

```
Need to identify slow phases?
├── YES → Use timing_analyzer.py
│         └── Parse simulation logs for timing data
│
Need to understand parallel performance?
````
END THIRD-PARTY FILE ef313febfa0d9002

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