PHP CUDA Extension

A near production-ready PHP extension that provides CUDA support for high-performance computing and deep learning tasks. It still needs a lot more testing, anyone who can test or wants to contribute, feel free. I want nothing in return for writing this the goal was merely to level the playing field between PHP and Python and prove that PHP is as capable as it's reptilian counterpart.

That being said, any donations are never refused.

Features

Core CUDA Support

• Device Management

  • Get CUDA device count
  • Query device properties
  • Set/Get current device
  • Device synchronization
  • Device reset capabilities

• Memory Management

  • CUDA memory allocation
  • Memory copying (host-to-device, device-to-host, device-to-device)
  • Automatic resource cleanup
  • Unified memory support
  • Pinned memory operations
  • Memory pool with fragmentation handling

cuBLAS Support

• High-performance matrix operations
• GEMM (General Matrix Multiplication)
• Optimized linear algebra operations
• Automatic handle management
• Batch processing capabilities

cuDNN Support

• Deep learning primitives
• Convolution operations
  • Forward convolution
  • Backward convolution (data)
  • Backward convolution (filter)
• Pooling operations
  • Forward pooling
  • Backward pooling
• Activation functions
  • Forward activation
  • Backward activation

Tensor Operations

• Tensor creation and manipulation
• Basic operations (add, multiply)
• Activation functions (ReLU, sigmoid, tanh)
• Gradient computation
• Shape manipulation

Multi-GPU Support

• Device affinity management
• Load balancing
• Multi-GPU computation
• Device synchronization
• Thread safety

Profiling and Monitoring

• CUDA event timing
• Memory usage tracking
• Kernel metrics collection
• Device utilization monitoring
• Performance benchmarking

Error Handling

• Comprehensive error checking
• Error status retrieval
• Error message translation

Requirements

• PHP 7.0 or later
• CUDA Toolkit 8.0 or later
• cuBLAS (included with CUDA Toolkit)
• cuDNN 7.0 or later
• C compiler (gcc/clang)
• PHP development files
• NVTX (optional, for profiling)

Installation

1. Clone the repository:

git clone https://github.com/yourusername/php-cuda.git
cd php-cuda

2. Run the compile script:

./compile.sh

Testing

The extension includes a comprehensive test suite covering various aspects of functionality:

Running Tests

To run all tests after installation:

./compile.sh --test

To run individual test files:

cd tests/
php run-test.php test_name.phpt

Available Tests

1. Basic Functionality (001-basic.phpt)

   • Device management
   • Basic matrix operations
   • Error handling

2. Memory Management (002-memory.phpt)

   • Memory allocation/deallocation
   • Memory pool operations
   • Leak detection

3. Stress Testing (003-stress.phpt)

   • High-load operations
   • Error recovery
   • Concurrent operations

4. Neural Network (004-neural.phpt)

   • Layer operations
   • Training functions
   • Model persistence

5. Tensor Operations (005-tensor.phpt)

   • Tensor creation
   • Basic operations
   • Activation functions
   • Gradient computation

6. Advanced Memory (006-advanced-memory.phpt)

   • Unified memory
   • Pinned memory
   • Memory pool fragmentation
   • Bandwidth measurement

7. Multi-GPU (007-multi-gpu.phpt)

   • Device management
   • Multi-GPU computation
   • Device synchronization
   • Thread safety

8. cuBLAS (008-cublas.phpt)

   • Basic operations
   • GEMM operations
   • Batch processing
   • Performance benchmarks

9. Profiling (009-profiler.phpt)

   • Event timing
   • Memory tracking
   • Kernel metrics
   • Device utilization

Test Requirements

• Some tests require multiple GPUs (007-multi-gpu.phpt)
• Profiling tests require NVTX support (009-profiler.phpt)
• Memory tests require sufficient GPU memory
• Neural network tests require cuDNN

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Usage Examples

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API Reference

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Performance Considerations

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Contributing

Contributions are welcome! Please feel free to submit pull requests.

License

MIT License - see LICENSE file for details.

Support

For issues and questions, please use the GitHub issue tracker.