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EvoKernel

CUDA kernel optimization agent powered by OpenEvolve.

Quick Start

# Install as CLI tool (requires Python 3.10-3.13)
pipx install evokernel
# or with uv
uv tool install evokernel

# Setup Modal (GPU evaluator)
modal setup

# Configure OpenRouter API key
evokernel setup

# Run in any project directory
evokernel

Development Install

git clone https://github.com/haladir-ai/EvoKernel.git
cd EvoKernel
uv tool install .

Usage

Just run evokernel in a directory with CUDA kernels and chat with the agent:

> optimize my_kernel.cu

> check status

> show the best result

> apply changes

Current Features

  • Speedup optimization: Maximizes kernel throughput via evolutionary search
  • EVOLVE markers: Define which code regions to optimize
  • Modal GPU evaluation: Compile and benchmark on cloud A100 (default)
  • Local GPU evaluation: Run benchmarks on your own GPU (for codebases with dependencies)
  • LLM ensemble: Uses Gemini, Claude, GPT via OpenRouter

Evaluator Modes

Mode Use Case
modal (default) Self-contained kernels, no local GPU needed
local Kernels with custom headers/dependencies

Roadmap (Not Yet Implemented)

  • Memory optimization goal: Minimize memory usage instead of speedup
  • Target hardware selection: Optimize for specific GPUs (A100, H100, V100)
  • Energy efficiency goal: Minimize power consumption

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