zram-optimizer

name	zram-optimizer
description	A-to-Z zram swap optimization skill. Install zram-config from GitHub, benchmark compression algorithms and kernel parameters with a deterministic evaluator, and deploy the optimal 'raw-RAM-like' high-speed swap configuration for any Linux system. Use when setting up swap, optimizing memory, tuning zram, or replacing disk-based swap.
version	1.0.0
author	Human Skill Team
tags	["zram","swap","memory","optimization","linux","performance","kernel-tuning"]
trigger_patterns	["zram","swap","memory optimization","swap setup","zram setup","optimize swap","replace swap","ram optimization","memory pressure","swappiness"]

zram-optimizer — High-Performance Swap Engine Skill

When to Use

Activate this skill when:

• Setting up or replacing swap on a Linux system
• Optimizing zram for maximum responsiveness
• Benchmarking compression algorithms (lz4, lzo-rle, zstd)
• Tuning kernel VM parameters (swappiness, page-cluster)
• Replacing slow disk-based swap with in-memory compressed swap
• The user wants a "raw-RAM-like" or "near-RAM-speed" swap experience

Overview

This skill provides a complete, deterministic, data-driven workflow for deploying a high-performance zram swap engine on any Linux system. It follows the OpenEvolve pattern: build an evaluator, test all combinations, and deploy the empirically proven winner.

Key Principle: No Guesswork

Instead of using generic defaults, this skill:

1. Detects the system's hardware (RAM, CPU cores)
2. Benchmarks every relevant zram parameter combination
3. Ranks configurations by a deterministic fitness score
4. Deploys the objectively fastest profile

Prerequisites

Install zram-config from GitHub

# 1. Clone the repository
sudo git clone https://github.com/ecdye/zram-config /opt/zram-config

# 2. Install
cd /opt/zram-config
sudo ./install.bash

# 3. Verify installation
systemctl status zram-config
cat /etc/ztab

[!IMPORTANT] The /etc/ztab file is the single source of truth for zram configuration. All changes are made here and applied by systemctl restart zram-config.

System Requirements

• Linux kernel 4.14+ (for lz4 and zstd support)
• zramctl utility (part of util-linux)
• Root/sudo access
• Python 3.8+

Workflow

Step 1: Detect Hardware → Step 2: Run Evaluator → Step 3: Analyze Results → Step 4: Deploy Winner

Step 1: Detect System Hardware

# Get total RAM
FREE_RAM=$(free -g | awk '/^Mem:/{print $2}')
echo "Total RAM: ${FREE_RAM}G"

# Get CPU cores (used for zram streams)
CORES=$(nproc)
echo "CPU Cores: $CORES"

# Check supported algorithms
cat /sys/block/zram0/comp_algorithm 2>/dev/null || echo "zram not loaded"

Sizing Rule: For systems with ≤16GB RAM, set disk_size equal to ~50-60% of total RAM. For systems with >16GB RAM, 8GB is usually sufficient.

Step 2: Run the Deterministic Evaluator

Use the combined zram_optimizer.py CLI to benchmark all configurations:

# Run the full benchmark suite
sudo human-skills '{
    "tool_name": "zram_optimizer",
    "tool_args": {
        "command": "bench"
    }
}'

The bench command will:

1. Iterate through all algorithm × swappiness × page-cluster combinations
2. For each combination, set up a fresh zram device
3. Run the run command as a subprocess to stress-test it with deterministic memory patterns
4. Record the fitness score to a CSV file
5. Clean up the zram device for the next test

Step 3: Analyze Results

The benchmark produces a CSV file (results.csv) with columns:

Rank,Algorithm,Priority,Swappiness,PageCluster,FinalScore,...

Higher score = better performance. The agent should:

1. Sort by Score descending
2. Identify the top configuration
3. Present the results to the user

Step 4: Deploy the Winner

# Example: Apply the winning configuration
sudo human-skills '{
    "tool_name": "zram_optimizer",
    "tool_args": {
        "command": "deploy",
        "algorithm": "lz4",
        "disk_size": "8G",
        "priority": 1000,
        "swappiness": 180,
        "page_cluster": 0
    }
}'

This command will:

1. Disable all existing swap
2. Write the optimized /etc/ztab
3. Restart zram-config
4. Automatically run status to verify the deployment

Script Reference

scripts/zram_optimizer.py — Unified CLI Tool

Purpose: Comprehensive zram tuning suite containing the memory evaluator, benchmark harness, and deployment automation.

Commands Overview:

# Review current zram/swap mappings
human-skills '{
    "tool_name": "zram_optimizer",
    "tool_args": {
        "command": "status"
    }
}'

# Low-level memory throughput test
human-skills '{
    "tool_name": "zram_optimizer",
    "tool_args": {
        "command": "run",
        "pressure_gb": "4.0"
    }
}'

Understanding the Parameters

Parameter	What It Does	Recommended
Algorithm	Compression codec. lz4 = fastest decompression, zstd = best ratio, lzo-rle = balanced.	lz4 for speed, zstd for memory-constrained
disk_size	Virtual swap capacity. Can exceed physical RAM because data is compressed.	50-60% of RAM, or 8G for ≥16G systems
mem_limit	Physical RAM zram can consume. Should equal disk_size for full-speed operation.	Same as disk_size
swappiness	How aggressively the kernel uses swap. 60=default, 180=very aggressive zram usage.	150-200 for zram (it's fast, so use it early)
page-cluster	Read-ahead pages. 0=single page (best for zram), 3=default (designed for spinning disks).	0 always for zram
swap_priority	Higher = used first. Set high to prefer zram over any disk swap.	100 or higher

Best Practices

1. Always Benchmark First

Never deploy a configuration without testing it. Even similar systems can behave differently.

2. Use Deterministic Evaluation

The evaluator must produce the same score for the same configuration every time. Never use random in the benchmark.

3. Nuclear Cleanup Between Tests

Each benchmark iteration must start from a completely clean state. The bench command handles this automatically.

4. Don't Oversize

Setting disk_size larger than necessary wastes CPU on compression without benefit. Match it to your actual usage pattern.

5. Monitor After Deployment

# Check compression ratio
human-skills '{
    "tool_name": "zram_optimizer",
    "tool_args": {
        "command": "status"
    }
}'

Troubleshooting

"Device or resource busy"

Cause: A process is still using the swap device. Fix: The scripts handle cleanup automatically, but if stuck manually:

sudo swapoff /dev/zramX
sudo zramctl --reset /dev/zramX

zram-config service fails to start

Cause: Stale zram devices from a previous run. Fix: Disable zram completely then restart:

sudo swapoff -a
for dev in /dev/zram*; do sudo zramctl --reset "$dev" 2>/dev/null; done
sudo systemctl restart zram-config

Example: Full Optimization Run

# 1. Install zram-config
sudo git clone https://github.com/ecdye/zram-config /opt/zram-config
cd /opt/zram-config && sudo ./install.bash

# 2. Run the benchmark (takes ~10-20 minutes)
sudo human-skills '{
    "tool_name": "zram_optimizer",
    "tool_args": {
        "command": "bench"
    }
}'

# 3. Check results
cat results.csv | sort -t, -k6 -rn | head -5

# 4. Deploy the winner (example: lz4, 8G, priority 1000, swappiness 180, page-cluster 0)
sudo human-skills '{
    "tool_name": "zram_optimizer",
    "tool_args": {
        "command": "deploy",
        "algorithm": "lz4",
        "disk_size": "8G",
        "priority": 1000,
        "swappiness": 180,
        "page_cluster": 0
    }
}'

# 5. Verify
human-skills '{
    "tool_name": "zram_optimizer",
    "tool_args": {
        "command": "status"
    }
}'

Summary

Step	Action	Tool
1	Install zram-config	git clone + install.bash
2	Detect hardware	free -g, nproc
3	Benchmark all profiles	zram_optimizer.py bench → zram_optimizer.py run
4	Analyze CSV results	Sort by Score column
5	Deploy winner to /etc/ztab	zram_optimizer.py deploy
6	Verify deployment	zram_optimizer.py status