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Automated bottleneck detection and solution orchestration

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# IntelliPerf: LLM-Powered Autonomous GPU Performance Engineer

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/AMDResearch/intelliperf/actions/workflows/lint.yml) [![IntelliPerf CI](https://github.com/AMDResearch/intelliperf/actions/workflows/ci.yml/badge.svg)](https://github.com/AMDResearch/intelliperf/actions/workflows/ci.yml) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15845118.svg)](https://doi.org/10.5281/zenodo.15845118)

> [!IMPORTANT]  
> This project is intended for research purposes only and is provided by AMD Research and Advanced Development team. 
This is not a product. Use it at your own risk and discretion.

![IntelliPerf](https://raw.githubusercontent.com/AMDResearch/intelliperf/main/images/intelliperf.png)

## Overview

**IntelliPerf** is an automated performance engineering framework that addresses the complex challenge of GPU kernel optimization. Manual optimization requires deep domain expertise and is time-consuming, error-prone, and resource-intensive. IntelliPerf systematizes this workflow by orchestrating a comprehensive toolchain that automatically profiles applications using [rocprofiler-compute](https://github.com/ROCm/rocprofiler-compute), identifies high-level bottlenecks with [Guided Tuning](./external/guided-tuning/), pinpoints specific source code lines using [Omniprobe](https://github.com/AARInternal/omniprobe), generates optimized code through Large Language Models (LLMs), and validates results using [Accordo](src/accordo/) for correctness and performance. Built on a modular "formula-driven" architecture, it targets specific bottlenecks like bank conflicts, memory access patterns, and atomic contention through a sophisticated multi-stage optimization loop that includes profiling, analysis, code generation, and automated validation.

### Key Features

* **AI-Powered Optimization**: Generates optimized code using LLMs with iterative feedback for performance improvements
* **Precise Analysis**: Pinpoints performance issues down to specific source code lines using compiler-based instrumentation
* **Automated Validation**: Validates both correctness and performance improvements through runtime comparison
* **Comprehensive Coverage**: Supports multiple bottleneck types (bank conflicts, memory access, atomic contention)
* **CI/CD Integration**: Seamless workflow integration with automated pull request generation
* **Extensible Architecture**: Formula-driven design for easy addition of new optimization targets

## Installation

### Quick Start with Containers

We provide both Apptainer and Docker images for easy setup:

#### Using Apptainer
```bash
./apptainer/build.sh
./apptainer/run.sh
```
#### Using Docker
```bash
./docker/build.sh
./docker/run.sh
```

Or use our prebuilt Docker image:
```bash
docker pull audacioussw/intelliperf:latest
export LLM_GATEWAY_KEY="your_api_key_here"
docker run -it --rm --device=/dev/kfd --device=/dev/dri --group-add video -e LLM_GATEWAY_KEY="$LLM_GATEWAY_KEY" audacioussw/intelliperf
```

#### For baremetal installation

1. **Install Additional Dependencies**:
   ```bash
   # ROCm dependencies
   apt-get install -y rocm-llvm-dev libzstd-dev

# KernelDB dependencies
   apt-get install -y libdwarf-dev

# Omniperf dependencies
   apt-get install -y locales
   locale-gen en_US.UTF-8
   ```

### Installation from Source

> [!NOTE]
> Due to the complex dependency chain, IntelliPerf currently supports development mode installation only. Future versions will support standard pip installation.

1. **Clone the Repository**:
   ```bash
   git clone https://github.com/AMDResearch/intelliperf.git
   cd intelliperf
   ```

2. **Install IntelliPerf**:
   ```bash
   pip install -e .
   ```

3. **Install Dependencies**:
   ```bash
   python3 scripts/install_tool.py --all
   ```

## Environment Variables

Set the following environment variable for AI-powered optimization:

```bash
export LLM_GATEWAY_KEY="your_api_key_here"
```

Required for bank conflicts, memory access patterns, and atomic contention optimization. The AI-powered optimization supports various language models and providers through the `--provider` and `--model` command line arguments. The key should be the backend key for the specified provider.

## Supported GPUs

IntelliPerf currently supports:

- **MI300X**

> [!NOTE]
> IntelliPerf may work on other AMD GPUs with ROCm compatibility, but has only been tested on MI300X.

## Usage

IntelliPerf can be used to analyze and optimize your GPU applications:

```bash
intelliperf [options] -- <profile_cmd>
```

### Examples

```bash
# Optimize bank conflicts in a HIP application
intelliperf -b ~/rocBLAS/build.sh -f bankConflict -- ~/rocBLAS/build/bin/rocblas_gemm

# Diagnose a Triton application
intelliperf -- python3 gemm.py
```

### Command Line Options

| Option                           | Description          |
|----------------------------------|----------------------|
| `-h, --help` | Show help message and exit |
| `-v, --verbose` | Increase verbosity level (e.g., -v, -vv, -vvv) |
| `-b, --build_command` | Command to build your application |
| `-i, --instrument_command` | Command to build your application with instrument |
| `-p, --project_directory` | Directory containing your codebase |
| `-f, --formula` | Optimization formula to use (bankConflict, memoryAccess, atomicContention, diagnoseOnly) |
| `--top_n` | Control top-n kernels in diagnoseOnly mode (default: 10) |
| `--num_attempts` | Control optimization attempts (default: 10) |
| `-o, --output_file` | Path to output file |
| `-t, --accordo_absolute_tolerance` | Validation tolerance |
| `-m, --model` | Specify the model to use for optimization (default: gpt-4o) |
| `-r, --provider` | Specify the provider to use for optimization (default: openai) |
| `--internal` | Use AMD's internal LLM service |
| `-l, --in_place` | Modify source files in place during optimization (default: creates backups) |
| `--unittest_command` | Command to run unit tests for additional validation |

> [!NOTE]
> IntelliPerf copies the entire project directory to a temporary location. Make sure your project doesn't include any temporary CMake files if you pass the `project_directory` flag.

## Documentation

- [IntelliPerf Technical Paper](docs/IntelliPerf.md) - Detailed technical overview of the IntelliPerf framework
- [Running Examples](examples/README.md)
- [AMD Developer Cloud Setup Guide](docs/DEVCLOUD.md) - Step-by-step instructions for setting up IntelliPerf on AMD Developer Cloud GPU droplets

## Citation

If you use IntelliPerf or discuss our work in your research, please always cite our work:

```bibtex
@software{   Awad:2025:ILP,
  author        = {Muhammad Awad and Cole Ramos and Keith Lowery},
  title         = {IntelliPerf: {LLM}-Powered Autonomous {GPU} Performance Engineer},
  year          = 2025,
  month         = jul,
  doi           = {10.5281/zenodo.15845118},
  url           = {https://github.com/AMDResearch/intelliperf},
  code          = {https://github.com/AMDResearch/intelliperf}
}
```

You can also use the [CITATION.cff](CITATION.cff) file in the repository root for automatic citation generation.

## Contributing

We welcome contributions! Please see our [Contributing Guide](docs/CONTRIBUTING.md) for details on how to set up your development environment and contribute to the project.

## Support

For support, please:
1. Open an [issue](https://github.com/AMDResearch/intelliperf/issues/new/choose)
2. Contact the development team

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

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